The low-lying excited states in the neutron-deficient $N=Z+1$ nucleus $^{87}_{43}$Tc$^{ }_{44}$ have been studied via the fusion-evaporation reaction $^{54}$Fe($^{36}$Ar, $2n1p$)$^{87}$Tc at the Grand Accelerateur National dIons Lourds (GANIL), Franc
e. The AGATA spectrometer was used in conjunction with the auxiliary NEDA, Neutron Wall, and DIAMANT detector arrays to measure coincident prompt $gamma$-rays, neutrons, and charged particles emitted in the reaction. A level scheme of $^{87}$Tc from the (9/2$^{+}_{g.s.}$) state to the (33/2$^{+}_{1}$) state was established based on 6 mutually coincident $gamma$-ray transitions. The constructed level structure exhibits a rotational behavior with a sharp backbending at $hbaromegaapprox 0.50$ MeV. A decrease in alignment frequency and increase in alignment sharpness in the odd-mass isotonic chains around $N=44$ is proposed as an effect of the enhanced isoscalar neutron-proton interactions in odd-mass nuclei when approaching the $N=Z$ line.
Drug combination therapy has become a increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefor
e, computational screening has become an important way to prioritize drug combinations. Graph neural network have recently shown remarkable performance in the prediction of compound-protein interactions, but it has not been applied to the screening of drug combinations. In this paper, we proposed a deep learning model based on graph neural networks and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The feature embeddings of drug molecule structure and gene expression profiles were taken as input to multi-layer feedforward neural network to identify the synergistic drug combinations. We compared DeepDDS with classical machine learning methods and other deep learning-based methods on benchmark data set, and the leave-one-out experimental results showed that DeepDDS achieved better performance than competitive methods. Also, on an independent test set released by well-known pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive methods by more than 16% predictive precision. Furthermore, we explored the interpretability of the graph attention network, and found the correlation matrix of atomic features revealed important chemical substructures of drugs. We believed that DeepDDS is an effective tool that prioritized synergistic drug combinations for further wet-lab experiment validation.
Nuclear liquid-gas phase transitions are investigated in the framework of static antisymmetrized molecular dynamics (static AMD) model under either a constant volume or a constant pressure. A deuteron quadrupole momentum fluctuation thermometer is ap
plied to extract the temperature of fragmenting systems of $^{36}$Ar and $^{100}$Sn. A plateau structure of caloric curves is observed under a constant volume for those system with a density $rho leq$ 0.03 fm$^{-3}$. A clear backbending in the caloric curves, which indicates a first order phase transition, is observed under a constant pressure with all pressures studied. The similar behavior of caloric curves of $^{36}$Ar and $^{100}$Sn systems indicates that there is no strong system size effect under a constant volume or a constant pressure. Both the mass distributions and the light particle multiplicities show a strong $alpha$ clusterization at low excitation energies in the static AMD simulations. The liquid-gas phase transition measures of the multiplicity derivative (dM/dT) and the normalized variance of $Z_{max}$ (NVZ) are applied. The experimental caloric curves are also compared with those of $^{100}$Sn of the static AMD simulations under both the constant volume and the constant pressure conditions. Discussions are presented with the available experimental results and those from the static AMD simulations. Large errors in the experimental temperature measurements and those in the reconstruction technique for the primary fragmenting source hinder to draw a conclusion whether the phase transition occurs under either a constant volume or a constant pressure. This study suggests that different measures for the liquid-gas phase transitions should be examined besides the caloric curves in order to draw a conclusion.
By exploiting the exotic quantum states of a probe, it is possible to realize efficient sensors that are attractive for practical metrology applications and fundamental studies. Similar to other quantum technologies, quantum sensing is suffering from
noises and thus the experimental developments are hindered. Although theoretical schemes based on quantum error correction (QEC) have been proposed to combat noises, their demonstrations are prevented by the stringent experimental requirements, such as perfect quantum operations and the orthogonal condition between the sensing interaction Hamiltonian and the noise Lindbladians. Here, we report an experimental demonstration of a quantum enhancement in sensing with a bosonic probe with different encodings, by exploring the large Hilbert space of the bosonic mode and developing both the approximate QEC and the quantum jump tracking approaches. In a practical radiometry scenario, we attain a 5.3 dB enhancement of sensitivity, which reaches $9.1times10^{-4},mathrm{Hz}^{-1/2}$ when measuring the excitation population of a receiver mode. Our results demonstrate the potential of quantum sensing with near-term quantum technologies, not only shedding new light on the quantum advantage of sensing by revealing its difference from other quantum applications, but also stimulating further efforts on bosonic quantum technologies.
A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station (BS) serves multiple clusters with unfixed number of users in each clu
ster. The goal is to maximize the sum rate of all users by jointly optimizing the passive beamforming vector at the IRS, decoding order, power allocation coefficient vector and number of clusters, subject to the rate requirements of users. In order to tackle the formulated problem, a three-step approach is proposed. More particularly, a long short-term memory (LSTM) based algorithm is first adopted for predicting the mobility of users. Secondly, a K-means based Gaussian mixture model (K-GMM) algorithm is proposed for user clustering. Thirdly, a deep Q-network (DQN) based algorithm is invoked for jointly determining the phase shift matrix and power allocation policy. Simulation results are provided for demonstrating that the proposed algorithm outperforms the benchmarks, while the throughput gain of 35% can be achieved by invoking NOMA technique instead of orthogonal multiple access (OMA).
Because the disc--jet coupling likely depends on various properties of sources probed, the sample control is always an important but challenging task. In this work, we re-analyzed the INTEGRAL hard X-ray-selected sample of Seyfert galaxies. We only c
onsider sources that have measurements in black hole mass, and luminosities in radio and X-rays. Our sample includes 64 sources, consists of both bright AGNs and low-luminosity ones. We first find that, because of the similarity in the $L_{HX}/L_X$ distribution, the X-ray origin of radio-loud Seyferts may be the same to that of radio-quiet ones, where we attribute to the hot accretion flow (or similarly, the corona). We then investigate the connections between luminosities in radio and X-rays. Since our sample suffers a selection bias of a black hole mass $M_{BH}$ dependence on $L_X/L_{Edd}$, we focus on the correlation slope $xi_X$ between the radio (at 1.4 GHz) and X-ray luminosities in Eddington unit, i.e. $(L_R/L_{Edd})propto(L_X/L_{Edd})^{xi_X}$. We classify the sources according to various properties, i.e. 1) Seyfert classification, 2) radio loudness, and 3) radio morphology. We find that, despite these differences in classification, all the sources in our sample are consistent with a universal correlation slope $xi_X$, with $xi_X=0.77pm0.10$. This is unexpected, considering various possible radio emitters in radio-quiet systems. For the jet interpretation, our result may suggest a common/universal but to be identified jet launching mechanism among all the Seyfert galaxies, while properties like black hole spin and magnetic field strength only play secondary roles. We further estimate the jet production efficiency $eta_{jet}$ of Seyfert galaxies, which is $eta_{jet}approx1.9^{+0.9}_{-1.5}times10^{-4}$ on average. We also find that $eta_{jet}$ increases as the system goes fainter.
We have experimentally tested the hypothesis of free charge carrier mediated spin-transport in the small molecule organic semiconductor Alq3 at room temperature. A spin current was pumped into this material by pulsed ferromagnetic resonance of an adj
acent NiFe layer, while a charge current resulting from this spin current via the inverse spin-Hall effect (ISHE) was detected in a Pt layer adjacent on the other side of the Alq3 layer, confirming a pure spin current through the Alq3 layer. Charge carrier spin states in Alq3, were then randomized by simultaneous application of electron paramagnetic resonance (EPR). No influence of the EPR excitation on the ISHE current was found, implying that spin-transport is not mediated by free charge-carriers in Alq3.
We report the measurements and analysis of weak antilocalization (WAL) in Pb1-xSnxSe topological quantum wells in a new regime where the elastic scattering length is larger than the magnetic length. We achieve this regime through the development of h
igh-quality epitaxy and doping of topological crystalline insulator (TCI) quantum wells. We obtain elastic scattering lengths that exceeds 100nm and become comparable to the magnetic length. In this transport regime, the Hikami-Larkin-Nagaoka model is no longer valid. We employ the model of Wittmann and Schmid to extract the coherence time from the magnetoresistance. We find that despite our improved transport characteristics, the coherence time may be limited by scattering channels that are not strongly carrier dependent, such as electron-phonon or defect scattering.
We use time-resolved x-ray resonant magnetic scattering (tr-XRMS) at the Co M$_{2,3}$- and Tb O$_1$-edges to study ultrafast demagnetization in an amorphous Co$_{88}$Tb$_{12}$ alloy with stripe domains. Combining the femtosecond temporal with nanomet
er spatial resolution of our experiment, we demonstrate that the equilibrium spin texture of the thin film remains unaltered by the optical pump-pulse on ultrashort timescales ($<$1 ps). However, after $simeq$ 4 ps, we observe the onset of a significant domain wall broadening, which we attribute to a reduction of the uniaxial magnetic anisotropy of the system, due to energy transfer to the lattice. Static temperature dependent magnetometry measurements combined with analytical modeling of the magnetic structure of the thin film corroborate this interpretation.
Gaia is regularly producing Alerts on objects where photometric variability has been detected. The physical nature of these objects has often to be determined with the complementary observations from ground-based facilities. We have compared the list
of Gaia Alerts (until 20181101) with archival LAMOST and SDSS spectroscopic data. The date of the ground-based observation rarely corresponds to the date of the Alert, but this allows at least the identification of the source if it is persistent, or the host galaxy if the object was only transient like a supernova. A list of Gaia Nuclear Transients from Kostrzewa-Rutkowska et al. (2018) has been included in this search also. We found 26 Gaia Alerts with spectra in LAMOST+SDSS labelled as stars (12 with multi-epoch spectra). A majority of them are CVs. Similarly 206 Gaia Alerts have associated spectra labelled as galaxies (49 with multi-epoch spectra). Those spectra were generally obtained on a date different from the Alert date, are mostly emission-line galaxies, leading to the suspicion that most of the Alerts were due to a SN. As for the GNT list, we found 55 associated spectra labelled as galaxies (13 with multi-epoch spectra). In two galaxies, Gaia17aal and GNTJ170213+2543, was the date of the spectroscopic observation close enough to the Alert date: we find a trace of the SN itself in their LAMOST spectrum, both classified here as a type Ia SN. The GNT sample has a higher proportion of AGNs, suggesting that some of the detected variations are also due to the AGN itself. Similar for Quasars, we found 30 Gaia Alerts but 68 GNT cases have single epoch quasar spectra, while 12 plus 23 have multi-epoch spectra. For ten out of these 35, their multi-epoch spectra show appearance or disappearance of the broad Balmer lines and also variations in the continuum, qualifying them as Changing Look Quasars.
