To assess the properties of the quark-gluon plasma formed in nuclear collisions, the Pearson correlation coefficient between flow harmonics and mean transverse momentum, $rholeft(v_{n}^{2},left[p_{mathrm{T}}right]right)$, reflecting the overlapped ge
ometry of colliding atomic nuclei, is measured. $rholeft(v_{2}^{2},left[p_{mathrm{T}}right]right)$ was found to be particularly sensitive to the quadrupole deformation of the nuclei. We study the influence of the nuclear quadrupole deformation on $rholeft(v_{n}^{2},left[p_{mathrm{T}}right]right)$ in $rm{Au+Au}$ and $rm{U+U}$ collisions at RHIC energy using $rm{AMPT}$ transport model, and show that the $rholeft(v_{2}^{2},left[p_{mathrm{T}}right]right)$ is reduced by the prolate deformation $beta_2$ and turns to change sign in ultra-central collisions (UCC).
Zero-Shot Learning (ZSL) targets at recognizing unseen categories by leveraging auxiliary information, such as attribute embedding. Despite the encouraging results achieved, prior ZSL approaches focus on improving the discriminant power of seen-class
features, yet have largely overlooked the geometric structure of the samples and the prototypes. The subsequent attribute-based generative adversarial network (GAN), as a result, also neglects the topological information in sample generation and further yields inferior performances in classifying the visual features of unseen classes. In this paper, we introduce a novel structure-aware feature generation scheme, termed as SA-GAN, to explicitly account for the topological structure in learning both the latent space and the generative networks. Specifically, we introduce a constraint loss to preserve the initial geometric structure when learning a discriminative latent space, and carry out our GAN training with additional supervising signals from a structure-aware discriminator and a reconstruction module. The former supervision distinguishes fake and real samples based on their affinity to class prototypes, while the latter aims to reconstruct the original feature space from the generated latent space. This topology-preserving mechanism enables our method to significantly enhance the generalization capability on unseen-classes and consequently improve the classification performance. Experiments on four benchmarks demonstrate that the proposed approach consistently outperforms the state of the art. Our code can be found in the supplementary material and will also be made publicly available.
Target-speaker voice activity detection (TS-VAD) has recently shown promising results for speaker diarization on highly overlapped speech. However, the original model requires a fixed (and known) number of speakers, which limits its application to re
al conversations. In this paper, we extend TS-VAD to speaker diarization with unknown numbers of speakers. This is achieved by two steps: first, an initial diarization system is applied for speaker number estimation, followed by TS-VAD network output masking according to this estimate. We further investigate different diarization methods, including clustering-based and region proposal networks, for estimating the initial i-vectors. Since these systems have complementary strengths, we propose a fusion-based method to combine frame-level decisions from the systems for an improved initialization. We demonstrate through experiments on variants of the LibriCSS meeting corpus that our proposed approach can improve the DER by up to 50% relative across varying numbers of speakers. This improvement also results in better downstream ASR performance approaching that using oracle segments.
Plutonium-based compounds establish an ideal platform for exploring the interplay between long-standing itinerant-localized 5$f$ states and strongly correlated electronic states. In this paper, we exhaustively investigate the correlated 5$f$ electron
ic states of PuSn$_3$ dependence on temperature by means of a combination of the density functional theory and the embedded dynamical mean-field theory. It is found that the spectral weight of narrow 5$f$ band grows significantly and remarkable quasiparticle multiplets appear around the Fermi level at low temperature. A striking $c-f$ hybridization and prominent valence state fluctuations indicate the advent of coherence and itinerancy of 5$f$ states. It is predicted that a 5$f$ localized to itinerant crossover is induced by temperature accompanied by the change in Fermi surface topology. Therefore itinerant 5$f$ states are inclined to take in active chemical bonding, suppressing the formation of local magnetic moment of Pu atoms, which partly elucidates the intrinsic feature of paramagnetic ground state of PuSn$_3$. Furthermore, the 5$f$ electronic correlations are orbital selective manifested themselves in differentiated band renormalizations and electron effective masses. Consequently, the convincing results remain crucial to our understanding of plutonium-based compounds and promote ongoing research.
In heavy ion collisions, elliptic flow $v_2$ and radial flow, characterized by event-wise average transverse momentum $[p_{mathrm{T}}]$, are related to the shape and size of the overlap region, which are sensitive to the shape of colliding atomic nuc
lei. The Pearson correlation coefficient between $v_2$ and $[p_{mathrm{T}}]$, $rho_2$, was found to be particularly sensitive to the quadrupole deformation parameter $beta$ that is traditionally measured in low energy experiments. Built on earlier insight that the prolate deformation $beta>0$ reduces the $rho_2$ in ultra-central collisions (UCC), we show that the prolate deformation $beta<0$ enhances the value of $rho_2$. As $beta>0$ and $beta<0$ are the two extremes of triaxiality, the strength and sign of $v_2^2-[p_{mathrm{T}}]$ correlation can be used to provide valuable information on the triaxiality of the nucleus. Our study provide further arguments for using the hydrodynamic flow as a precision tool to directly image the deformation of the atomic nuclei at extremely short time scale ($<10^{-24}$s).
To generate drug molecules of desired properties with computational methods is the holy grail in pharmaceutical research. Here we describe an AI strategy, retro drug design, or RDD, to generate novel small molecule drugs from scratch to meet predefin
ed requirements, including but not limited to biological activity against a drug target, and optimal range of physicochemical and ADMET properties. Traditional predictive models were first trained over experimental data for the target properties, using an atom typing based molecular descriptor system, ATP. Monte Carlo sampling algorithm was then utilized to find the solutions in the ATP space defined by the target properties, and the deep learning model of Seq2Seq was employed to decode molecular structures from the solutions. To test feasibility of the algorithm, we challenged RDD to generate novel drugs that can activate {mu} opioid receptor (MOR) and penetrate blood brain barrier (BBB). Starting from vectors of random numbers, RDD generated 180,000 chemical structures, of which 78% were chemically valid. About 42,000 (31%) of the valid structures fell into the property space defined by MOR activity and BBB permeability. Out of the 42,000 structures, only 267 chemicals were commercially available, indicating a high extent of novelty of the AI-generated compounds. We purchased and assayed 96 compounds, and 25 of which were found to be MOR agonists. These compounds also have excellent BBB scores. The results presented in this paper illustrate that RDD has potential to revolutionize the current drug discovery process and create novel structures with multiple desired properties, including biological functions and ADMET properties. Availability of an AI-enabled fast track in drug discovery is essential to cope with emergent public health threat, such as pandemic of COVID-19.
The temperature-dependent evolution pattern of 5f electrons helps to elucidate the long-standing itinerant-localized dual nature in plutonium-based compounds. In this work, we investigate the correlated electronic states of PuIn3 dependence on temper
ature by using a combination of the density functional theory and the dynamical mean-field theory. Not only the experimental photoemission spectroscopy is correctly reproduced, but also a possible hidden 5f itinerant-localized crossover is identified. Moreover, it is found that the quasiparticle multiplets from the many-body transitions gradually enhance with decreasing temperature, accompanied by the hybridizations with 5f electrons and conduction bands. The temperature-induced variation of Fermi surface topology suggests a possible electronic Lifshitz transition and the onset of magnetic order at low temperature. Finally, the ubiquitous existence orbital selective 5f electron correlation is also discovered in PuIn3. These illuminating results shall enrich the understanding on Pu-based compounds and serve as critical predictions for ongoing experimental research.
The physical properties of plutonium and plutonium-based intermetallic compounds are extremely sensitive to temperature, pressure, and chemical alloying. A celebrated example is the high-temperature $delta$ phase plutonium, which can be stabilized at
room temperature by doping it with a few percent trivalent metal impurities, such as gallium or aluminum. The cubic phase Pu$_{3}$Ga, one of the plutonium-gallium intermetallic compounds, plays a key role in understanding the phase stability and phase transformation of the plutonium-gallium system. Its electronic structure might be essential to figure out the underlying mechanism that stabilizes the $delta$ phase plutonium-gallium alloy. In the present work, we studied the temperature-dependent correlated electronic states of cubic phase Pu$_{3}$Ga by means of a combination of the density functional theory and the embedded dynamical mean-field theory. We identified orbital selective 5$f$ itinerant-localized (coherent-incoherent) crossovers which could occur upon temperature. Actually, there exist two well-separated electronic coherent temperatures. The higher one is for the $5f_{5/2}$ state [$T_{text{coh}}(5f_{5/2}) approx 700$ K], while the lower one is for the $5f_{7/2}$ state [$T_{text{coh}}(5f_{7/2}) approx 100$ K]. In addition, the quasiparticle multiples which originate from the many-body transitions among the $5f^{4}$, $5f^{5}$, and $5f^{6}$ electronic configurations, decay gradually. The hybridizations between the localized 5$f$ bands and conduction bands are subdued by high temperature. Consequently, the Fermi surface topology is changed, which signals a temperature-driven electronic Lifshitz transition. Finally, the calculated linear specific heat coefficient $gamma$ is approximately 112 mJ / (mol K$^2$) at $T = 80$ K.
At high magnetic fields, monolayer graphene hosts competing phases distinguished by their breaking of the approximate SU(4) isospin symmetry. Recent experiments have observed an even denominator fractional quantum Hall state thought to be associated
with a transition in the underlying isospin order from a spin-singlet charge density wave at low magnetic fields to an antiferromagnet at high magnetic fields, implying that a similar transition must occur at charge neutrality. However, this transition does not generate contrast in typical electrical transport or thermodynamic measurements and no direct evidence for it has been reported, despite theoretical interest arising from its potentially unconventional nature. Here, we measure the transmission of ferromagnetic magnons through the two dimensional bulk of clean monolayer graphene. Using spin polarized fractional quantum Hall states as a benchmark, we find that magnon transmission is controlled by the detailed properties of the low-momentum spin waves in the intervening Hall fluid, which is highly density dependent. Remarkably, as the system is driven into the antiferromagnetic regime, robust magnon transmission is restored across a wide range of filling factors consistent with Pauli blocking of fractional quantum hall spin-wave excitations and their replacement by conventional ferromagnetic magnons confined to the minority graphene sublattice. Finally, using devices in which spin waves are launched directly into the insulating charge-neutral bulk, we directly detect the hidden phase transition between bulk insulating charge density wave and a canted antiferromagnetic phases at charge neutrality, completing the experimental map of broken-symmetry phases in monolayer graphene.
The discoveries of high-temperature superconductivity in H3S and LaH10 have excited the search for superconductivity in compressed hydrides. In contrast to rapidly expanding theoretical studies, high-pressure experiments on hydride superconductors ar
e expensive and technically challenging. Here we experimentally discover superconductivity in two new phases,Fm-3m-CeH10 (SC-I phase) and P63/mmc-CeH9 (SC-II phase) at pressures that are much lower (<100 GPa) than those needed to stabilize other polyhydride superconductors. Superconductivity was evidenced by a sharp drop of the electrical resistance to zero, and by the decrease of the critical temperature in deuterated samples and in an external magnetic field. SC-I has Tc=115 K at 95 GPa, showing expected decrease on further compression due to decrease of the electron-phonon coupling (EPC) coefficient {lambda} (from 2.0 at 100 GPa to 0.8 at 200 GPa). SC-II has Tc = 57 K at 88 GPa, rapidly increasing to a maximum Tc ~100 K at 130 GPa, and then decreasing on further compression. This maximum of Tc is due to a maximum of {lambda} at the phase transition from P63/mmc-CeH9 into a symmetry-broken modification C2/c-CeH9. The pressure-temperature conditions of synthesis affect the actual hydrogen content, and the actual value of Tc. Anomalously low pressures of stability of cerium superhydrides make them appealing for studies of superhydrides and for designing new superhydrides with even lower pressures of stability.
