Probing Outflows in z= 1~2 Galaxies through FeII/FeII* Multiplets

We report on a study of the 2300-2600\AA FeII/FeII* multiplets in the rest-UV spectra of star-forming galaxies at 1.0<z<2.6 as probes of galactic-scale outflows. We extracted a mass-limited sample of 97 galaxies at z~1.0-2.6 from ultra-deep spectra obtained during the GMASS spetroscopic survey in the GOODS South field with the VLT and FORS2. We obtain robust measures of the rest equivalent width of the FeII absorption lines down to a limit of W_r>1.5 \AA and of the FeII* emission lines to W_r>0.5 \AA. Whenever we can measure the systemic redshift of the galaxies from the [OII] emission line, we find that both the FeII and MgII absorption lines are blueshifted, indicative that both species trace gaseous outflows. We also find, however, that the FeII gas has generally lower outflow velocity relative to that of MgII. We investigate the variation of FeII line profiles as a function of the radiative transfer properties of the lines, and find that transitions with higher oscillator strengths are more blueshifted in terms of both line centroids and line wings. We discuss the possibility that FeII lines are suppressed by stellar absorptions. The lower velocities of the FeII lines relative to the MgII doublet, as well as the absence of spatially extended FeII* emission in 2D stacked spectra, suggest that most clouds responsible for the FeII absorption lie close (3~4 kpc) to the disks of galaxies. We show that the FeII/FeII* multiplets offer unique probes of the kinematic structure of galactic outflows.Comment: 53 pages, 22 Figures, accepted for publication in ApJ, revised according to referee comment

Ising Meson Spectroscopy on a Noisy Digital Quantum Simulator

Quantum simulation has the potential to be an indispensable technique for the investigation of non-perturbative phenomena in strongly-interacting quantum field theories (QFTs). In the modern quantum era, with Noisy Intermediate Scale Quantum~(NISQ) simulators widely available and larger-scale quantum machines on the horizon, it is natural to ask: what non-perturbative QFT problems can be solved with the existing quantum hardware? We show that existing noisy quantum machines can be used to analyze the energy spectrum of a large family of strongly-interacting 1+1D QFTs. The latter exhibit a wide-range of non-perturbative effects like `quark confinement' and `false vacuum decay' which are typically associated with higher-dimensional QFTs of elementary particles. We perform quench experiments on IBM's ibmq_mumbai quantum simulator to compute the energy spectrum of 1+1D quantum Ising model with a longitudinal field. The latter model is particularly interesting due to the formation of mesonic bound states arising from a confining potential for the Ising domain-walls, reminiscent of t'Hooft's model of two-dimensional quantum chromodynamics. Our results demonstrate that digital quantum simulation in the NISQ era has the potential to be a viable alternative to numerical techniques such as density matrix renormalization group or the truncated conformal space methods for analyzing QFTs.Comment: 4 figures, version with updated set of reference

Transcriptional regulation of FoxO3 gene by glucocorticoids in murine myotubes.

Glucocorticoids and FoxO3 exert similar metabolic effects in skeletal muscle. FoxO3 gene expression was increased by dexamethasone (Dex), a synthetic glucocorticoid, both in vitro and in vivo. In C2C12 myotubes the increased expression is due to, at least in part, the elevated rate of FoxO3 gene transcription. In the mouse FoxO3 gene, we identified three glucocorticoid receptor (GR) binding regions (GBRs): one being upstream of the transcription start site, -17kbGBR; and two in introns, +45kbGBR and +71kbGBR. Together, these three GBRs contain four 15-bp glucocorticoid response elements (GREs). Micrococcal nuclease (MNase) assay revealed that Dex treatment increased the sensitivity to MNase in the GRE of +45kbGBR and +71kbGBR upon 30- and 60-min Dex treatment, respectively. Conversely, Dex treatment did not affect the chromatin structure near the -17kbGBR, in which the GRE is located in the linker region. Dex treatment also increased histone H3 and/or H4 acetylation in genomic regions near all three GBRs. Moreover, using chromatin conformation capture (3C) assay, we showed that Dex treatment increased the interaction between the -17kbGBR and two genomic regions: one located around +500 bp and the other around +73 kb. Finally, the transcriptional coregulator p300 was recruited to all three GBRs upon Dex treatment. The reduction of p300 expression decreased FoxO3 gene expression and Dex-stimulated interaction between distinct genomic regions of FoxO3 gene identified by 3C. Overall, our results demonstrate that glucocorticoids activated FoxO3 gene transcription through multiple GREs by chromatin structural change and DNA looping

Prediction of electromagnetic forces and vibrations in SRMs operating at steady-state and transient speeds

Although some research has been conducted on vibrations in switched reluctance motors (SRMs), the response during transients, which may occur during sudden load changes or braking, has not received much investigation. In this paper, a simulation model to predict the transient vibration of SRMs is developed. The vibration prediction model is built based on the detailed normal force versus phase current and rotor position lookup table using finite-element calculations. The model is then verified by a running motor test, which shows acceptable accuracy. The results reveal that there are abundant harmonics of transient force during transients and, thus, resonance may be excited. This model allows the possibility of improved design of SRMs from a vibration and acoustic noise point of view

Collabs: A Flexible and Performant CRDT Collaboration Framework

A collaboration framework is a distributed system that serves as the data layer for a collaborative app. Conflict-free Replicated Data Types (CRDTs) are a promising theoretical technique for implementing collaboration frameworks. However, existing frameworks are inflexible: they are often one-off implementations of research papers or only permit a restricted set of CRDT semantics, and they do not allow app-specific optimizations. Until now, there was no general framework that lets programmers mix, match, and modify CRDTs. We solve this with Collabs, a CRDT-based collaboration framework that lets programmers implement their own CRDTs, either from-scratch or by composing existing building blocks. Collabs prioritizes both semantic flexibility and performance flexibility: it allows arbitrary app-specific CRDT behaviors and optimizations, while still providing strong eventual consistency. We demonstrate Collabs's capabilities and programming model with example apps and CRDT implementations. We then show that a collaborative rich-text editor using Collabs's built-in CRDTs can scale to over 100 simultaneous users, unlike existing CRDT frameworks and Google Docs. Collabs also has lower end-to-end latency and server CPU usage than a popular Operational Transformation framework, with acceptable CRDT metadata overhead.Comment: 18 pages, 19 figure

Binary Star Evolution in Different Environments: Filamentary, Fractal, Halo and Tidal-tail Clusters

Using membership of 85 open clusters from previous studies (Pang et al. 2021a,b, 2022b; Li et al. 2021) based on Gaia DR3 data, we identify binary candidates in the color-magnitude diagram, for systems with mass ratio q > 0.4. The binary fraction is corrected for incompleteness at different distances due to the Gaia angular resolution limit. We find a decreasing binary fraction with increasing cluster age, with substantial scatter. For clusters with a total mass > 200$M_\odot$, the binary fraction is independent of cluster mass. The binary fraction depends strongly on stellar density. Among four types of cluster environments, the lowest-density filamentary and fractal stellar groups have the highest mean binary fraction: 23.6% and 23.2%, respectively. The mean binary fraction in tidal-tail clusters is 20.8%, and is lowest in the densest halo-type clusters: 14.8%. We find clear evidence of early disruptions of binary stars in the cluster sample. The radial binary fraction depends strongly on the cluster-centric distance across all four types of environments, with the smallest binary fraction within the half-mass radius $r_h$, and increasing towards a few $r_h$. Only hints of mass segregation is found in the target clusters. The observed amount of mass segregation is not significant to generate a global effect inside the target clusters. We evaluate the bias of unresolved binary systems (assuming a primary mass of 1$M_\odot$) in 1D tangential velocity, which is 0.1-1$\,\rm km\,s^{-1}$. Further studies are required to characterize the internal star cluster kinematics using Gaia proper motions

Distinct lesion features and underlying mechanisms in patients with acute multiple infarcts in multiple cerebral territories

ObjectiveTo determine the etiology spectrum and lesion distribution patterns of patients with acute multiple infarcts in multiple cerebral territories (AMIMCT) and provide guidance for treatment and prevention strategies in these patients.MethodsPatients with acute ischemic stroke diagnosed using diffusion-weighted imaging (DWI) were consecutively included in this study between June 2012 and Apr 2022. AMIMCT was defined as non-contiguous focal lesions located in more than one cerebral territory with acute neurological deficits. We retrospectively analyzed the clinical and imaging characteristics, etiology spectra and underlying mechanisms in patients with and without AMIMCT. Infarct lesion patterns on DWI and their relevance to etiology were further discussed.ResultsA total of 1,213 patients were enrolled, of whom 145 (12%) were diagnosed with AMIMCT. Patients with AMIMCT tended to be younger (P = 0.016), more often female (P = 0.001), and exhibited less common conventional vascular risk factors (P &lt; 0.05) compared to those without AMIMCT. The constitution of the Trial of Org 10,172 in Acute Stroke Treatment classification was significantly different between patients with and without AMIMCT (P = 0.000), with a higher proportion of stroke of other determined causes (67.6% vs. 12.4%). For detailed etiologies, autoimmune or hematologic diseases were the most common (26.2%) etiologies of AMIMCT, followed by periprocedural infarcts (15.2%), cardioembolism (12.4%), tumor (12.4%), large artery atherosclerosis (10.3%), and sudden drop in blood pressure (8.3%). Hypercoagulability and systemic hypoperfusion are common underlying mechanisms of AMIMCT. Distinctive lesion distribution patterns were found associated with stroke etiologies and mechanisms in AMIMCT. Most of patients with large artery atherosclerosis (73.3%), autoimmune/hematologic diseases (57.9%) manifested the disease as multiple infarct lesions located in bilateral supratentorial regions. However, 66.7% of cardioembolism and 83.8% of cardiovascular surgery related stroke presented with both supratentorial and infratentorial infarct lesions.ConclusionThe etiologies and mechanisms of patients with AMIMCT were more complex than those without AMIMCT. The distribution characteristics of infarct lesions might have important implications for the identification of etiology and mechanism in the future, which could further guide and optimize clinical diagnostic strategies

Integrating audio and visual modalities for multimodal personality trait recognition via hybrid deep learning

Recently, personality trait recognition, which aims to identify people’s first impression behavior data and analyze people’s psychological characteristics, has been an interesting and active topic in psychology, affective neuroscience and artificial intelligence. To effectively take advantage of spatio-temporal cues in audio-visual modalities, this paper proposes a new method of multimodal personality trait recognition integrating audio-visual modalities based on a hybrid deep learning framework, which is comprised of convolutional neural networks (CNN), bi-directional long short-term memory network (Bi-LSTM), and the Transformer network. In particular, a pre-trained deep audio CNN model is used to learn high-level segment-level audio features. A pre-trained deep face CNN model is leveraged to separately learn high-level frame-level global scene features and local face features from each frame in dynamic video sequences. Then, these extracted deep audio-visual features are fed into a Bi-LSTM and a Transformer network to individually capture long-term temporal dependency, thereby producing the final global audio and visual features for downstream tasks. Finally, a linear regression method is employed to conduct the single audio-based and visual-based personality trait recognition tasks, followed by a decision-level fusion strategy used for producing the final Big-Five personality scores and interview scores. Experimental results on the public ChaLearn First Impression-V2 personality dataset show the effectiveness of our method, outperforming other used methods