Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Risk Oncogenic Human Cytomegalovirus

Human cytomegalovirus (HCMV) is a herpesvirus that infects between 40% and 95% of the population worldwide, usually without symptoms. The host immune response keeps the virus in a latent stage, although HCMV can reactivate in an inflammatory context, which could result in sequential lytic/latent viral cycles during the lifetime and thereby participate in HCMV genomic diversity in humans. The high level of HCMV intra-host genomic variability could participate in the oncomodulatory role of HCMV where the virus will favor the development and spread of cancerous cells. Recently, an oncogenic role of HCMV has been highlighted in which the virus will directly transform primary cells; such HCMV strains are named high-risk (HR) HCMV strains. In light of these new findings, this review defines the criteria that characterize HR-HCMV strains and their molecular as well as the phenotypic impact on the infected cell and its tumor microenvironment.
DIMENSIONAL DEPENDENCE OF THE HYDRODYNAMICS OF CORE-COLLAPSE SUPERNOVAE

A major goal over the last decade has been understanding which multidimensional effects are crucial in facilitating core-collapse supernova (CCSN) explosions. Unfortunately, much of this work has necessarily assumed axisymmetry. In this work, we present analyses of simplified two-dimensional (2D) and three-dimensional (3D) CCSN models with the goal of comparing the hydrodynamics in setups that differ only in dimension. Not surprisingly, we find many differences between 2D and 3D models. While some differences are subtle and perhaps not crucial, others are dramatic and make interpreting 2D models problematic. In particular, axisymmetric models produce excess power at the largest spatial scales, power that has been deemed critical in previous explosion models. Nevertheless, our 3D models, which have an order of magnitude less power than 2D models on large scales, explode earlier. Since explosions occur earlier in 3D than in 2D, the vigorous large-scale sloshing is either not critical in any dimension or the explosion mechanism operates differently in 2D and 3D. On the other hand, we find that the average parcel of matter in the gain region has been exposed to net heating for up to 30% longer in 3D than in 2D, an effect we attribute to the differing characters of turbulence in 2D and 3D. We suggest that this effect plays a prominent role in producing earlier explosions in 3D. Finally, we discuss a simple model for the runaway growth of buoyant bubbles that is able to quantitatively account for the growth of the shock radius and predicts a critical luminosity relation.
OBSERVATIONAL CONSTRAINTS ON PLANET NINE: ASTROMETRY OF PLUTO AND OTHER TRANS-NEPTUNIAN OBJECTS

We use astrometry of Pluto and other trans-neptunian objects to constrain the sky location, distance, and mass of the possible additional planet (Planet Nine) hypothesized by Batygin & Brown. We find that over broad regions of the sky, the inclusion of a massive, distant planet degrades the fits to the observations. However, in other regions, the fits are significantly improved by the addition of such a planet. Our best fits suggest a planet that is either more massive or closer than argued for by Batygin & Brown based on the orbital distribution of distant trans-neptunian objects (or by Fienga et al. based on range measured to the Cassini spacecraft). The trend to favor larger and closer perturbing planets is driven by the residuals to the astrometry of Pluto, remeasured from photographic plates using modern stellar catalogs, which show a clear trend in decl. over the course of two decades, that drive a preference for large perturbations. Although this trend may be the result of systematic errors of unknown origin in the observations, a possible resolution is that the decl. trend may be due to perturbations from a body, in addition to Planet Nine, that is closer to Pluto but less massive than Planet Nine.
A Novel Attack Identification Mechanism in IoT-Based Converter-Composed DC Grids

This article proposes a novel attack identification mechanism for Internet of Things-based (IoT-based) converter-composed dc grids, where each agent collects its own and neighbors’ measurement data for output regulation to meet a preceding power-sharing consensus. Independent from model-free or average-model-based attack detection theories, this mechanism is mainly inspired by converter stitching behavior analysis. Correspondingly, when facing latent signal substitution or agent instigation attacks, through comparing estimated signals with received ones for signal source authentication, both self-sensors and neighbors will be inspected. Eventually, not only can such attacks be detected but also will respective attack sources be identified. A simulation case of 4-agent 800-V IoT-based dc grid on Simulink and a hardware case of 3-agent 90-V IoT-based dc grid on the dSpace testing platform were investigated. Experimental results revealed that the estimation ratio error kept lower than 3.9% and all attacks were successfully identified, verifying the effectiveness of the proposed mechanism.
GALAXY CLUSTERS SELECTED WITH THE SUNYAEV–ZEL'DOVICH EFFECT FROM 2008 SOUTH POLE TELESCOPE OBSERVATIONS

We present a detection-significance-limited catalog of 21 Sunyaev–Zel'dovich-selected galaxy clusters. These clusters, along with one unconfirmed candidate, were identified in 178 deg2 of sky surveyed in 2008 by the South Pole Telescope (SPT) to a depth of 18 μK arcmin at 150 GHz. Optical imaging from the Blanco Cosmology Survey (BCS) and Magellan telescopes provided photometric (and in some cases spectroscopic) redshift estimates, with catalog redshifts ranging from z = 0.15 to z>1, with a median z = 0.74. Of the 21 confirmed galaxy clusters, 3 were previously identified as Abell clusters, 3 were presented as SPT discoveries in Staniszewski et al., and 3 were first identified in a recent analysis of BCS data by Menanteau et al.; the remaining 12 clusters are presented for the first time in this work. Simulated observations of the SPT fields predict the sample to be nearly 100% complete above a mass threshold of M200 ≈ 5 × 1014 M☉ h−1 at z = 0.6. This completeness threshold pushes to lower mass with increasing redshift, dropping to ∼4 × 1014 M☉ h−1 at z = 1. The size and redshift distribution of this catalog are in good agreement with expectations based on our current understanding of galaxy clusters and cosmology. In combination with other cosmological probes, we use this cluster catalog to improve estimates of cosmological parameters. Assuming a standard spatially flat wCDM cosmological model, the addition of our catalog to the WMAP seven-year results yields σ8 = 0.81 ± 0.09 and w = −1.07 ± 0.29, a ∼50% improvement in precision on both parameters over WMAP7 alone.
Inverse volume corrections from loop quantum gravity and the primordial tensor power spectrum in slow-roll inflation

Laboratoire de Physique Subatomique et de Cosmologie, UJF, INPG, CNRS, IN2P353, avenue des Martyrs, 38026 Grenoble cedex, France(Dated: April 16, 2009)Together with holonomy corrections, inverse volume terms should be taken into account whenstudying the primordial universe in loop quantum cosmology. We investigate how the tensor powerspectrum is modiﬁed with respect to the standard general relativistic prediction by those semiclas-sical corrections. Depending on the values of the free parameters of the model, it is shown that thespectrum can exhibit a very large deviation from its usual shape, in particular with a very red slopeand a strong running in the infrared limit.
Clustering Based Hybrid Precoding Design for Multi-User Massive MIMO Systems

Hybrid precoding has been recognized as a promising technology to combat the path loss of millimeter-wave signals in massive multiple-input multiple-output (MIMO) systems. However, due to the joint optimization of the digital and analog precoding matrices as well as extra constraints for the analog part, the hybrid precoding design is still a tough issue in current research. In this paper, we adopt the thought of clustering in unsupervised learning and provide design schemes for fully-connected hybrid precoding (FHP) and adaptively-connected hybrid precoding (AHP) in multi-user massive MIMO systems. For FHP, we propose the hierarchical-agglomerative-clustering-based (HAC-based) scheme to explore the relevance among radio frequency (RF) chains in the optimal hybrid precoding design. The similar RF chains are merged into an individual when insufficient RF chains are available. For AHP, we propose the modified-K-means-based (MKM-based) scheme to explore the relevance among antennas. The similar antennas are supported by the same RF chain to make full use of the flexible connection in AHP. Particularly, in the proposed MKM-based AHP design, the clustering centers are updated by alternating-optimum-based (AO-based) scheme with a specific initialization method, which is capable to independently provide feasible sub-connected hybrid precoding (SHP) design with quick convergence. Simulation results highlight the superior spectrum efficiency of the proposed HAC-based FHP scheme, and the high power efficiency of the proposed MKM-based AHP scheme. Moreover, all the proposed schemes are clarified to effectively handle the inter-user interference and outperform the existing work.
Hints for Hidden Planetary Companions to Hot Jupiters in Stellar Binaries

Searches for stellar companions to hot Jupiters (HJs) have revealed that planetary systems hosting an HJ are approximately three times more likely to have a stellar companion with a semimajor axis between 50 and 2000 au, compared to field stars. This correlation suggests that HJ formation is affected by the stellar binary companion. A potential model is high-eccentricity migration, in which the binary companion induces high-eccentricity Lidov–Kozai (LK) oscillations in the proto-HJ orbit, triggering orbital migration driven by tides. A pitfall of this “binary-LK” model is that the observed stellar binaries hosting HJs are typically too wide to produce HJs in sufficient numbers because of suppression by short-range forces. We propose a modification to the binary-LK model in which there is a second giant planet orbiting the proto-HJ at a semimajor axis of several tens of au. Such companions are currently hidden to observations, but their presence could be manifested by a propagation of the perturbation of the stellar binary companion inward to the proto-HJ, thereby overcoming the barrier imposed by short-range forces. Our model does not require the planetary companion orbit to be eccentric and/or inclined with respect to the proto-HJ, but its semimajor axis should lie in a specific range given the planetary mass and binary semimajor axis, and the inclination with respect to the binary should be near 40° or 140°. Our prediction for planetary companions to HJs in stellar binaries should be testable by future observations.
Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering

Recently, distant supervision has gained great success on Fine-grained Entity Typing (FET). Despite its efficiency in reducing manual labeling efforts, it also brings the challenge of dealing with false entity type labels, as distant supervision assigns labels in a context-agnostic manner. Existing works alleviated this issue with partial-label loss, but usually suffer from confirmation bias, which means the classifier fit a pseudo data distribution given by itself. In this work, we propose to regularize distantly supervised models with Compact Latent Space Clustering (CLSC) to bypass this problem and effectively utilize noisy data yet. Our proposed method first dynamically constructs a similarity graph of different entity mentions; infer the labels of noisy instances via label propagation. Based on the inferred labels, mention embeddings are updated accordingly to encourage entity mentions with close semantics to form a compact cluster in the embedding space, thus leading to better classification performance. Extensive experiments on standard benchmarks show that our CLSC model consistently outperforms state-of-the-art distantly supervised entity typing systems by a significant margin.
Wind braking of magnetars: To understand magnetars' multiwave radiation properties

Magnetars are proposed to be peculiar neutron stars powered by their super-strong magnetic field. Observationally, anomalous X-ray pulsars and soft gamma-ray repeaters are believed to be magnetar candidates. While more and more multiwave observations of magnetars are available, we see unfortunately an accumulation of failed predictions of the traditional magnetar model. These challenges urge a reconsidering of the magnetar phenomenon. Wind braking of magnetars is one of the alternative modelings. The release of magnetic energy may generate a particle outflow (i.e., particle wind) which results in both an anomalous X-ray luminosity (LX) and a significantly high spindown rate (Ṗ). In this wind braking scenario, only a strong multipole field is necessary for a magnetar (a strong dipole field is no longer needed). Wind braking of magnetars may help us to understand their multiwave radiation properties, including (1) non-detection of magnetars in the Fermi -LAT observations, (2) timing behaviors of low magnetic field magnetars, (3) nature of anti-glitches, (4) criterion for magnetar radio emission, etc. In the wind braking model of magnetars, timing events of magnetars should always be accompanied by radiative events. (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Deep Reinforcement Learning for the Detection of Abnormal Data in Smart Meters

The rapidly growing power data in smart grids have created difficulties in security management. The processing of large-scale power data with the use of artificial intelligence methods has become a hotspot research topic. Considering the early warning detection problem of smart meters, this paper proposes an abnormal data detection network based on Deep Reinforcement Learning, which includes a main network and a target network composed of deep learning networks. This work uses the greedy policy algorithm to find the action of the maximum value of Q based on the Q-learning method to obtain the optimal calculation policy. It also uses the reward value and discount factor to optimize the target value. In particular, this study uses the fuzzy c-means method to predict the future state information value, which improves the computational accuracy of the Deep Reinforcement Learning model. The experimental results show that compared with the traditional smart meter data anomaly detection method, the proposed model improves the accuracy of meter data anomaly detection.
Effects of the local structure dependence of evaporation fields on field evaporation behavior

Accurate three dimensional reconstructions of atomic positions, and full quantification of the information contained in atom probe tomography data relies on understanding the physical processes taking place during field evaporation of atoms from needle-shaped specimens. However, the modeling framework for atom probe tomography has remained qualitative at best. Building on the continuum field models previously developed, we introduce a more physical approach with the selection of evaporation events based on density functional theory calculations. This new model reproduces key features observed experimentally in terms of sequence of evaporation, desorption maps, and depth resolution, and provides insights into the physical limit for spatial resolution.
Challenges of interpreting dielectric dilatometry for the study of pressure densification

We report an experimental study documenting the challenge of employing dielectric dilatometry for the study of pressure densification in glass-forming materials. An influence of the dielectric cell geometry on the resulting capacitance of 5-poly-phenyl-ether upon vitrification under different thermobaric pathways is documented. The capacitive response is studied for two different multilayer capacitors: one with, in principle, fixed plate distance and one with Kapton spacers allowing for contraction/expansion. A combination of changes in the dielectric permittivity of the material and modifications of the capacitor geometry determines the final capacitance. We conclude that, in order to convert the measured capacitance to material density, it is of paramount importance to understand the geometry. The data presented do not make it possible to conclude on whether or not simple glass formers such as 5-poly-phenyl-ether can be pressure densified, but our work highlights the challenge of utilizing dielectric spectroscopy to tackle this problem effectively.
Ultimate entanglement robustness of two-qubit states against general local noises

We study the problem of optimal preparation of a bipartite entangled state, which remains entangled the longest time under action of local qubit noises. We show that for unital noises, such a state is always maximally entangled, whereas for nonunital noises, it is not. We develop a decomposition technique relating nonunital and unital qubit channels, based on which we find the explicit form of the ultimately robust state for general local noises. We illustrate our findings by amplitude damping processes at finite temperature, for which the ultimately robust state remains entangled up to two times longer than conventional maximally entangled states.
How Cloud Computing and Business Intelligence support UAV Technology

In recent years, the majority of UAVs are implemented using technologies that require a lot of effort, budget and time. This will consume the available resources in an exaggerated manner and will therefore affect the overall performance of the UAV itself. In this paper, we present the use of cloud computing in conjunction with business intelligence for the aim of adding more efficiency to the UAV technology in the high speed era and the use of 5G technology. The recent introduction of the cloud computing is a very good opportunity which can support business intelligence. Through this paper, we will discuss the procedure of mapping cloud computing to the UAVs and the challenges that will be emerged when doing so. Many of these challenges are not limited to the UAVs energy levels, motion, and GPS settings.
Neonatal kaempferol exposure attenuates gait and strength deficits and prevents altered muscle phenotype in a rat model of cerebral palsy

Cerebral palsy (CP) is characterized by brain damage at a critical period of development of the central nervous system, and, as a result, motor, behavioural and learning deficits are observed in those affected. Flavonoids such as kaempferol have demonstrated potential anti‐inflammatory and neuroprotective properties for neurological disorders. This study aimed to assess the effects of neonatal treatment with kaempferol on the body development, grip strength, gait performance and morphological and biochemical phenotype of skeletal muscle in rats subjected to a model of CP. The groups were formed by randomly allocating male Wistar rats after birth to four groups as follows: C = control treated with vehicle, K = control treated with kaempferol, CP = CP treated with vehicle and CPK = CP treated with kaempferol. The model of CP involved perinatal anoxia and sensorimotor restriction of the hind paws during infancy, from the second to the 28th day of postnatal life. Treatment with kaempferol (1 mg/kg) was performed intraperitoneally during the neonatal period. Body weight and length, muscle strength, gait kinetics and temporal and spatial parameters were evaluated in the offspring. On the 36th day of postnatal life, the animals were euthanized for soleus muscle dissection. The muscle fibre phenotype was assessed using the myofibrillar ATPase technique, and the muscle protein expression was measured using the Western blot technique. A reduction in the impact of CP on body phenotype was observed, and this also attenuated deficits in muscle strength and gait. Treatment also mitigated the impact on muscle phenotype by preventing a reduction in the proportion of oxidative fibres and in the histomorphometric parameters in the soleus muscle of rats in the CP group. The results demonstrate that neonatal treatment with kaempferol attenuated gait deficits and impaired muscle strength and muscle maturation in rats subjected to a model of CP.
A Search for Black Hole Microlensing Signatures in Globular Cluster NGC 6656 (M22)

We report the results of a study aiming to detect signs of astrometric microlensing caused by an intermediate-mass black hole (IMBH) in the center of globular cluster M22 (NGC 6656). We used archival data from the Hubble Space Telescope (HST) taken between 1995 and 2014 to derive long-baseline astrometric time series for stars near the center of the cluster, using state-of-the-art software to extract high-precision astrometry from images. We then modeled these time-series data and compared microlensing model fits to simple linear proper-motion fits for each selected star. We find no evidence for astrometric microlensing in M22, in particular for bulge stars, which are much more likely to be lensed than cluster stars, due to the geometry of microlensing events. Although it is in principle possible to derive mass limits from such nondetections, we find that no useful mass limits can be derived for M22 with available data, mostly due to a 10 year gap in coverage. This is a result from difficulties with deriving precise enough astrometry from Wide Field Planetary Camera 2 observations for stars that do not fall on the planetary camera chip. However, this study shows that, for other HST instruments, we are able to reach precisions at which astrometric microlensing signals caused by IMBH in globular clusters could be detected and that this technique is a promising tool to make a first unambiguous detection of an IMBH.
Gamma-ray burst optical light-curve zoo: comparison with X-ray observations

We present a comprehensive analysis of the optical and X-ray light curves (LCs) and spectral energy distributions (SEDs) of a large sample of gamma-ray burst (GRB) afterglows to investigate the relationship between the optical and X-ray emission after the prompt phase. We collected the optical data from the literature and determined the shapes of the optical LCs. Then, using previously presented X-ray data we modeled the optical/X-ray SEDs. We studied the SED parameter distributions and compared the optical and X-ray LC slopes and shapes. The optical and X-ray spectra become softer as a function of time while the gas-to-dust ratios of GRBs are higher than the values calculated for the Milky Way and the Large and Magellanic Clouds. For 20% of the GRBs the difference between the optical and X-ray slopes is consistent with 0 or 1=4 within the uncertainties (we did it not consider the steep decay phase), while in the remaining 80% the optical and X-ray afterglows show significantly different temporal behaviors. Interestingly, we find an indication that the onset of the forward shock in the optical LCs (initial peaks or shallow phases) could be linked to the presence of the X-ray flares. Indeed, when X-ray flares are present during the steep decay, the optical LC initial peak or end plateau occurs during the steep decay; if instead the X-ray flares are absent or occur during the plateau, the optical initial peak or end plateau takes place during the X-ray plateau. The forward-shock model cannot explain all features of the optical (e.g. bumps, late re-brightenings) and X-ray (e.g. flares, plateaus) LCs. However, the synchrotron model is a viable mechanism for GRBs at late times. In particular, we found a relationship between the presence of the X-ray flares and the shape of the optical LC that indicates a link between the prompt emission and the optical afterglow.