Miracle-dz/UniFAR-FTUs huggingface dataset:
THE ASSEMBLY OF MILKY-WAY-LIKE GALAXIES SINCE z ∼ 2.5

Galaxies with the mass of the Milky Way dominate the stellar mass density of the universe but it is uncertain how and when they were assembled. Here we study progenitors of these galaxies out to z = 2.5, using data from the 3D-HST and CANDELS Treasury surveys. We find that galaxies with present-day stellar masses of log (M) ≈ 10.7 built ∼90% of their stellar mass since z = 2.5, with most of the star formation occurring before z = 1. In marked contrast to the assembly history of massive elliptical galaxies, mass growth is not limited to large radii: the mass in the central 2 kpc of the galaxies increased by a factor of between z = 2.5 and z = 1. We therefore rule out simple models in which bulges were fully assembled at high redshift and disks gradually formed around them. Instead, bulges (and black holes) likely formed in lockstep with disks, through bar instabilities, migration, or other processes. We find that after z = 1 the growth in the central regions gradually stopped and the disk continued to be built up, consistent with recent studies of the gas distributions in z ∼ 1 galaxies and the properties of many spiral galaxies today.
Research on Smoke Detection Model Based on Improved YOLOv4

In order to improve the accuracy of YOLOv4 network in smoke detection, this paper adds the attention module to YOLOv4 and replaces the first downsampling with the Focus module to construct the YOLO-Focus network (abbreviated as YOLO-F in this paper). The addition of the Attention module and Foucs module enables better extraction of smoke information from images, while the $\mathbf{K}-\mathbf{means}++$ algorithm is used instead of the K-means algorithm to obtain more suitable anchors' size. In order to improve the operation speed of the network in smoke detection devices, a lightweight smoke detection network YOLO-Small (abbreviated as YOLO-S in this paper) is proposed in this paper. In YOLO-S, the number of channels in the network is reduced based on YOLO-F, which makes the network run faster in smoke detection devices. Compared with YOLOv4, YOLO-S has 11.6% higher detection accuracy and 6.69 FPS higher detection rate. Compared with the mainstream lightweight network YOLOv4-mobilenetv3, YOLO-S improves the detection accuracy by 5.4% while ensuring real-time detection. The comparative experimental results of each network demonstrate the effectiveness of the proposed YOLO-S in smoke detection. The comparison experimental results of each network prove the effectiveness of the YOLO-S in the effectiveness of the proposed YOLO-S for smoke detection.
Slow-then-rapid quenching as traced by tentative evidence for enhanced metallicities of cluster galaxies at z ∼ 0.2 in the slow quenching phase

Aims. As large-scale structures in the Universe develop with time, environmental effects become more and more important as a star formation quenching mechanism. Since the effects of environmental quenching are more pronounced in denser structures that form at later times, we seek to constrain environmental quenching processes using cluster galaxies at z < 0.3. Methods. We explored seven clusters from the Local Cluster Substructure Survey (LoCuSS) at 0.15 < z < 0.26 with spectra of 1965 cluster members in a mass-complete sample from the ACReS (Arizona Cluster Redshift Survey) Hectospec survey covering a region that corresponds to about three virial radii for each cluster. We measured fluxes of [O II] λ 3727, Hβ, [O III] λ 5007, Hα, and [N II] λ 6584 emission lines of cluster members, enabling us to unambiguously derive O/H gas metallicities. We also measured star formation rates (SFRs) from extinction-corrected Hα fluxes. We compared our cluster galaxy sample with a field sample of 705 galaxies at similar redshifts observed with Hectospec as part of the same survey. Results. We find that star-forming cluster and field galaxies show similar median specific SFRs in a given mass bin of 1 − 3.2 × 1010 M⊙ and 3.2 − 10 × 1010 M⊙, respectively. But their O/H values are displaced, in the lower mass bin, to higher values (significance 2.4σ) at projected radii of R < R200 compared with galaxies at larger radii and in the field. The comparison with metallicity-SFR-mass model predictions with inflowing gas indicates a slow-quenching scenario in which strangulation is initiated when galaxies pass R ∼ R200 by stopping the inflow of gas. We find tentative evidence that the metallicities of cluster members inside R200 are thereby increasing, but their SFRs are hardly affected for a period of time because these galaxies consume available disk gas. We use the observed fraction of star-forming cluster galaxies as a function of clustercentric radius compared to predictions from the Millennium simulation to constrain quenching timescales to be 1−2 Gyr, which is defined as the time between the moment the galaxy passes R200 until complete quenching of star formation. This is consistent with a slow-then-rapid quenching scenario. Slow quenching (strangulation) starts when the gas inflow is stopped when the galaxy passes R200 with a phase in which cluster galaxies are still star forming, but they show elevated metallicities tracing the ongoing quenching. This phase lasts for 1−2 Gyr, and meanwhile the galaxies travel to denser inner regions of the cluster. This is followed by a “rapid” phase, i.e., a rapid complete quenching of star formation due to the increasing ram pressure toward the cluster center that can also strip the cold gas in massive galaxies.
Herschel/HIFI observations of [CII] and [13CII] in PDRs

Recently, we introduced detailed isotopic chemistry into the KOSMA-tau model for photon-dominated regions (PDRs) to give theoretical predictions for the abundance of the carbon isotopologues as a function of PDR parameters. Combined with radiative transfer computations for specific geometries, we estimated the possible intensity ratio of the [CII]/[13CII] lines. Here, we compare these predictions with new observations. We performed Herschel/HIFI observations of the [CII] 158micron line in a number of PDRs. In all sources we observed at least two hyperfine components of the [13CII] transition allowing to determine the [CII]/[13CII] intensity ratio, after some revision of the intrinsic hyperfine ratios. Comparing the intensity ratios with the results from the updated KOSMA-tau model, we identify cases dominated by chemical fractionation and cases dominated by the optical depth of the main isotopic line. An observable enhancement of the [CII]/[13CII] intensity ratio due to chemical fractionation depends mostly on geometry and velocity structure, and less on the gas density and radiation field. In our observations the [CII]/[13CII] ratio for the integrated line intensity was always dominated by the optical depth of the main isotopic line. However, an enhanced intensity ratio is found for particular velocity components in a few sources: the red-shifted material in the ultracompact HII region Mon R2, the wings of the turbulent profile in the Orion Bar, and possibly a blue wing in NGC7023. The mapping of the [13CII] lines in the Orion Bar allows to derive a C+ column density map confirming the temperature stratification of the C+ layer, in agreement with the chemical stratification of the Bar. The C+ column densities for all sources show that at the position of the [CII] peak emission, a dominant fraction of the gas-phase carbon is in the form of C+.
How AGN and SN Feedback Affect Mass Transport and Black Hole Growth in High-redshift Galaxies

Using cosmological hydrodynamical simulations, we study the effect of supernova (SN) and active galactic nucleus (AGN) feedback on the mass transport (MT) of gas onto galactic nuclei and the black hole (BH) growth down to redshift z ∼ 6 . We study the BH growth in relation to the MT processes associated with gravity and pressure torques and how they are modified by feedback. Cosmological gas funneled through cold flows reaches the galactic outer region close to freefall. Then torques associated with pressure triggered by gas turbulent motions produced in the circumgalactic medium by shocks and explosions from SNe are the main source of MT beyond the central ∼100 pc. Due to high concentrations of mass in the central galactic region, gravitational torques tend to be more important at high redshift. The combined effect of almost freefalling material and both gravity and pressure torques produces a mass accretion rate of order ∼ 1 M ⊙ yr−1 at approximately parsec scales. In the absence of SN feedback, AGN feedback alone does not affect significantly either star formation or BH growth until the BH reaches a sufficiently high mass of ∼ 10 6 M ⊙ to self-regulate. SN feedback alone, instead, decreases both stellar and BH growth. Finally, SN and AGN feedback in tandem efficiently quench the BH growth, while star formation remains at the levels set by SN feedback alone, due to the small final BH mass, ∼few times 10 5 M ⊙ . SNe create a more rarefied and hot environment where energy injection from the central AGN can accelerate the gas further.
Track Foreign Object Debris Detection based on Improved YOLOv4 Model

As the train runs faster and faster, track foreign object invasion will have a serious impact on train running safety. Unfortunately, the current track foreign object intrusion detection algorithm has low recognition rate, large model size and poor real-time performance. In order to solve this problem, we propose an improved track intrusion detection algorithm YOLOv4-GCBAMFPN based on the YOLOv4 model. Firstly, K-means++ clustering algorithm is used to optimize the selection of the prior box size, so that the prior box fits the feature layer better. Then the lightweight network GhostNet is used to replace the original backbone network CSPDarkNet53 for feature extraction, at the same time, the feature fusion network of YOLOv4 is improved and the PANet is replaced by CBAMFPN network. We also use the k-means++ algorithm to optimize the a priori anchor box parameters. The experimental results show that the average recognition accuracy of the improved YOLOv4 algorithm on the orbital foreign object data set reaches 90.07%, 8.88% higher than the traditional YOLOv4 algorithm and the frame rate increased from 23.0 f/s to 72.2 f/s. It effectively improves the real-time and accuracy of track foreign body intrusion identification.
Accretion on Reissner–Nordström–(anti)-de Sitter black hole with global monopole

In this paper, we investigate the accretion on the Reissner–Nordström–anti-de-Sitter black hole with global monopole charge. We discuss the general solutions of accretion using the isothermal and polytropic equations of state for steady state, spherically symmetric, non-rotating accretion on the black hole. In the case of isothermal flow, we consider some specific fluids and derive their solutions at the sonic point as well. However, in the case of polytropic fluid we calculate the general expressions only, as there exists no global (Bondi) solutions for polytropic test fluids. In addition to this, the effect of fluid on the mass accretion rate are also studied. Moreover, the large monopole parameter β greatly suppresses the maximum accretion rate.
An Experimental Study on Applying Supervised Machine Learning Techniques for Identification and Detection of Cardiac Attacks

Predicting cardiac disease is one of the most challenging undertakings in the medical industry. At this time, heart disease is responsible for about one death per minute. Using data science to process massive volumes of information is critical in the healthcare industry. Automating the forecasting procedure is necessary to reduce risks connected by it and warn the patient well in advance because predicting heart conditions can be a difficult undertaking. The healthcare sector employs millions of people worldwide and creates a considerable amount of data. The multidimensional medical datasets are being broken down by machine learning-based algorithms, which are revealing new information. In this study, a cardiovascular dataset is successfully categorized to produce illness predictions utilizing a number of cutting-edge supervised machine learning techniques. The findings showed that the Decision Tree classification model outperformed Naive Bayes and Logistic Regression in predicting cardiovascular disease. The Decision Tree was used to obtain accuracy of 74%. This technique may help doctors identify cardiac problems early and start treatment on schedule.
Interacting fermionic symmetry-protected topological phases in two dimensions

Symmetry-protected topological (SPT) phases (e.g., the well known topological insulators) are a class of energetically gapped condensed matter systems that exhibit interesting topological properties only in the presence of certain global symmetries. While a great understanding of noninteracting fermionic SPT phases has been achieved recently, interacting SPT phases, in particular those that exist only in strongly interacting systems, are much less understood in general. Here, the authors study strongly interacting fermionic SPT phases in two spatial dimensions with finite Abelian unitary symmetries and provide a potentially complete classification of them.
Analog Compute in Memory and Breaking Digital Number Representations

Analog in Memory Computing (AiMC) is a promising solution to achieve energy efficient deployment of Deep Neural Networks (DNNs). However, to this day, the accuracies of DNNs deployed using AIMC do not compare favorably with digital alternatives. In this work, we illustrate that these lacking performance can be partly attributed to mismatched arithmetic assumption. We show that correcting those assumptions results in more robust and accurate DNNs on analog platforms. By further leveraging insights from AiMC related methods, we question modern neural network quantization techniques, revealing, in the process, alternative multiplication-less modes of executions for DNNs.
ZaLoBI: Zero avoiding Load Balanced Inference accelerator

Convolutional neural networks are prevalent machine learning tools used in computer vision. Their ubiquitous use and high compute requirement have given rise to the design and development of accelerators for the same. Among several approaches to improve the performance of these accelerators, exploiting data sparsity has become very popular. Along similar lines, this paper proposes a design that skips the computation of zero-valued data operands and achieves better speedup. The savings in zero-valued computations also results in energy savings. The proposed accelerator exploits two levels of data parallelism to distribute work across multiple processing elements (PEs). The random distribution of zero values results in certain PEs getting idle due to the skipping of computations, thus creating load imbalance in the system. To address this issue, we extend our contribution in performing load balancing by dynamically scheduling tasks to the idle PEs. Our zero avoiding load-balanced accelerator (ZaLoBI) achieves around 76% and 5.57% speedup over the respective baselines and also outperforms the state-of-the-art works while saving energy.
Dynamic Electricity Tariff in Distribution Systems: Bibliometric Analysis and Literature Review

This article presents a bibliometric analysis and literature review related to dynamic electricity rates, focusing on Time of Use tariff (TOU), Real-Time Pricing (RTP), and Prediction of Use tariff (POU). Through the bibliometric analysis, articles related to these tariffs are determined, which consider the consumption of electrical energy based on the uncertainty of the price of electrical energy, the base load of the users, the increase in distributed generation, the use of electric vehicles, the reduction of the loss of useful life of the assets of the system, among others. Besides, the aforementioned allows a better understanding of trends of research about this specific topic. The analysis reported here may be useful for distribution system operators and the scientific community, but it can also be used as a guide to conduct a review of the literature on other related topics or delve into any of the topics described.
Easy5G: Software for Simulation and Study of 5G NR Interface

The new 5G mobile internet standard, expected to arrive in mid-2022, promises greater connection capacity for billions of devices and a balanced experience between speed, latency, and low power consumption. To achieve high error correction power, the 3GPP used a class of encoder known as low-density parity-check code (LDPC). In this sense, this work presents the development of a software, named Easy5G, that allows simulation and evaluation of the 5G New Radio (NR) scenarios and channel coding schemes planned for 5G NR, using LDPC codes. Easy5G, through the GNU Radio platform, allows parameter changes and simulations of bit error rate (BER) results. This software allows the creation of direct contact between this new technology, academia, and industry.
Instability of hairy black holes in shift-symmetric Horndeski theories

Recently it was pointed out that in shift-symmetric scalar-tensor theories a black hole can have nontrivial scalar hair which depends linearly on time. We develop black hole perturbation theory for such solutions and compute the quadratic action of odd-parity perturbations. We show that around all the solutions known so far with such time-dependent scalar hair the perturbations trigger instabilities or are presumably strongly coupled.
ENDURA : Enhancing Durability of Multi Level Cell STT-RAM based Non Volatile Memory Last Level Caches

With high packing density and low leakage power, Spin Transfer Torque Random Access Memories (STT-RAM) are a promising alternative to replace traditional memory technologies such as SRAM and DRAM. Applications will continue to demand more memory for processing in the coming decades. To achieve higher cell density, Multi-Level Cell STT-RAM (MLC STT-RAM) that can store two or more bits in a single memory cell is preferred over Single Level Cell STT-RAM (SLC STT-RAM). But their multistep read and write operations lead to significant read and write latency. The multistep write operations are also affecting the durability of MLC STT-RAM. Specialised wear levelling techniques are not available for MLC STT-RAM. We propose ENDURA, a technique that could improve the lifetime and latency of MLC STT-RAMs. ENDURA extends the lifetime of 2MB and 4MB MLC STT-RAM L2 caches by 2.05x and 2.59x on a single-core system and reduces write latency by 9.6% and 8.06% respectively with minimal overhead.
Statistical Ensembles with Fluctuating Extensive Quantities

We suggest an extension of the standard concept of statistical ensembles. Namely, we introduce a class of ensembles with extensive quantities fluctuating according to an externally given distribution. As an example, the influence of energy fluctuations on multiplicity fluctuations in limited segments of momentum space for a classical ultra-relativistic gas is considered. The system volume fluctuations are also discussed.
Electrostatic and electromagnetic instabilities associated with electrostatic shocks: Two-dimensional particle-in-cell simulation

A two-dimensional electromagnetic particle-in-cell simulation with the realistic ion-to-electron mass ratio of 1836 is carried out to investigate the electrostatic collisionless shocks in relatively high-speed (∼3000 km s−1) plasma flows and also the influence of both electrostatic and electromagnetic instabilities, which can develop around the shocks, on the shock dynamics. It is shown that the electrostatic ion-ion instability can develop in front of the shocks, where the plasma is under counterstreaming condition, with highly oblique wave vectors as was shown previously. The electrostatic potential generated by the electrostatic ion-ion instability propagating obliquely to the shock surface becomes comparable with the shock potential and finally the shock structure is destroyed. It is also shown that in front of the shock the beam-Weibel instability gradually grows as well, consequently suggesting that the magnetic field generated by the beam-Weibel instability becomes important in long-term evolution .
Research on Anti-pursuit Evasion Strategy of Unmanned Surface Vehicle Based on T-D3QN

From the perspective of the evader in the pursuit evasion problem of the unmanned surface vehicle (USV), this paper proposes an anti-pursuit evasion strategy for the USV based on Twin-Dueling Double Deep Q Network (T-D3QN), which uses two independent Q networks to estimate the Q value. According to characteristics of the pursuit-evasion problem of the USV, the research designs state space, action space and reward function and constructs the simulation environment to train the model. Simulation experiments show that the performance of T-D3QN is better than DQN because it can improve the escape success rate of the USV, and verify the effectiveness of the evasion strategy for the USV.
Systematic perturbation theory for dynamical coarse-graining

We demonstrate how the dynamical coarse-graining approach can be systematically extended to higher orders in the coupling between system and reservoir. Up to second order in the coupling constant we explicitly show that dynamical coarse-graining unconditionally preserves positivity of the density matrix -- even for bath density matrices that are not in equilibrium and also for time-dependent system Hamiltonians. By construction, the approach correctly captures the short-time dynamics, i.e., it is suitable to analyze non-Markovian effects. We compare the dynamics with the exact solution for highly non-Markovian systems and find a remarkable quality of the coarse-graining approach. The extension to higher orders is straightforward but rather tedious. The approach is especially useful for bath correlation functions of simple structure and for small system dimensions.
Detecting extrasolar asteroid belts through their microlensing signatures

We propose that extrasolar asteroid belts can be detected through their gravitational microlensing signatures. Asteroid belt + star lens systems create so-called "pseudo-caustics", regions in the source plane where the magnification exhibits a finite but discontinuous jump. These features allow such systems to generate distinctive signatures in the microlensing light curves for a wide range of belt configurations, with source trajectories as far as tenths of the Einstein ring radius from the centre of the lens. Sample light curves for a range of asteroid belt parameters are presented. In the near future, space-based microlensing surveys like WFIRST, which will have the power to detect percent-level changes in microlensing light curves even with sub-minute exposure times, may be able to discover extrasolar asteroid belts with masses of the order of an earth mass.
A conceptual framework for personality-sensitive scheduling models

The paper considers the assignment problem as a model of online schedules with a deadline in a personality-sensitive setting. In this setting, processing speeds are heterogeneous and depend not only on a type of request but also on individual characteristics of groups of customers and operators. We propose a formal statement of the optimization problem and discuss its applicability for real-world scenarios using the example of a call center request processing system.