Dynamical quantum phase transitions in non-Hermitian lattices

In closed quantum systems, a dynamical phase transition is identified by nonanalytic behaviors of the return probability as a function of time. In this work, we study the nonunitary dynamics following quenches across exceptional points in a non-Hermitian lattice realized by optical resonators. Dynamical quantum phase transitions with topological signatures are found when an isolated exceptional point is crossed during the quench. A topological winding number defined by a real, noncyclic geometric phase is introduced, whose value features quantized jumps at critical times of these phase transitions and remains constant elsewhere, mimicking the plateau transitions in quantum Hall effects. This work provides a simple framework to study dynamical and topological responses in non-Hermitian systems.Comment: 7 pages, 5 figure

Aspects of Floquet Bands and Topological Phase Transitions in a Continuously Driven Superlattice

Recently the creation of novel topological states of matter by a periodic driving field has attracted great attention. To motivate further experimental and theoretical studies, we investigate interesting aspects of Floquet bands and topological phase transitions in a continuously driven Harper model. In such a continuously driven system with an odd number of Floquet bands, the bands are found to have nonzero Chern numbers in general and topological phase transitions take place as we tune various system parameters, such as the amplitude or the period of the driving field. The nontrivial Floquet band topology results in a quantized transport of Wannier states in the lattice space. For certain parameter choices, very flat yet topologically nontrivial Floquet bands may also emerge, a feature that is potentially useful for the simulation of physics of strongly correlated systems. Some cases with an even number of Floquet bands may also have intriguing Dirac cones in the spectrum. Under open boundary conditions, anomalous counter-propagating chiral edge modes and degenerate zero modes are also found as the system parameters are tuned. These results should be of experimental interest because a continuously driven system is easier to realize than a periodically kicked system.Comment: 29 pages, 9 figures. Comments are welcom

Decarbonization potential of on-road fuels and powertrains in the European Union and the United States: a well-to-wheels assessment

Transportation is fundamental for any modern economy, but its growing energy demand and the related climate impact call for urgent action. Life-cycle analysis (LCA) is a suitable approach to assessing the greenhouse gas (GHG) performance and decarbonization potential of transportation fuels and vehicle powertrains. Here, we assessed well-to-wheels (WTW) GHG emission reductions for a wide set of light-duty vehicle fuel and powertrain technologies used in the European Union (EU) and the United States (U.S.) for their decarbonization potential. We focused on the similarities and differences of the results and the underlying methodologies and data of the two analyses. We evaluated the decarbonization potential of new fuel–vehicle systems in Europe and the United States in comparison to the baseline petroleum gasoline and diesel vehicles in each market. For the transportation fuels examined in both regions, waste-to-fuel technologies and drop-in renewable diesel fuels (biofuels) produced from residues offer the biggest opportunities for reducing per-energy-unit GHG emissions, but may be limited in scale-up potentials given feedstock availabilities, qualities, and logistics challenges. The potential benefits of electricity and hydrogen as fuels span a wide range, determined by the primary energy source and the potential deployment of carbon capture and sequestration technologies. From a tank-to-wheels perspective, electric powertrains, with higher energy efficiency than internal combustion engines, provide incontrovertible evidence of GHG savings. For vehicle–fuel combined systems, the per km WTW results from GREET are generally higher than the JEC estimates, owing to greater vehicle fuel consumption attributable to larger vehicle sizes and more aggressive driving cycles in the U.S. This paper highlights key drivers of WTW fuel–vehicle system GHG emissions as well as opportunities and limitations to decarbonize light-duty transportation in Europe and the United States with promising alternative fuel production and vehicle powertrain technologies. Results show that major solutions in both regions are aligned, despite certain differences in the methodologies and results of the WTW analyses. As well as informing optimal selection of fuel and powertrain technologies for future vehicles, these findings are also useful in informing how existing vehicles can best be decarbonized through the use of renewable fuels and advanced powertrain technologies

Floquet dynamical quantum phase transitions

Dynamical quantum phase transitions (DQPTs) are manifested by time-domain nonanalytic behaviors of many-body systems.Introducing a quench is so far understood as a typical scenario to induce DQPTs.In this work, we discover a novel type of DQPTs, termed "Floquet DQPTs", as intrinsic features of systems with periodic time modulation.Floquet DQPTs occur within each period of continuous driving, without the need for any quenches.In particular, in a harmonically driven spin chain model, we find analytically the existence of Floquet DQPTs in and only in a parameter regime hosting a certain nontrivial Floquet topological phase. The Floquet DQPTs are further characterized by a dynamical topological invariant defined as the winding number of the Pancharatnam geometric phase versus quasimomentum.These findings are experimentally demonstrated with a single spin in diamond.This work thus opens a door for future studies of DQPTs in connection with topological matter

SCULPTOR: Skeleton-Consistent Face Creation Using a Learned Parametric Generator

Recent years have seen growing interest in 3D human faces modelling due to its wide applications in digital human, character generation and animation. Existing approaches overwhelmingly emphasized on modeling the exterior shapes, textures and skin properties of faces, ignoring the inherent correlation between inner skeletal structures and appearance. In this paper, we present SCULPTOR, 3D face creations with Skeleton Consistency Using a Learned Parametric facial generaTOR, aiming to facilitate easy creation of both anatomically correct and visually convincing face models via a hybrid parametric-physical representation. At the core of SCULPTOR is LUCY, the first large-scale shape-skeleton face dataset in collaboration with plastic surgeons. Named after the fossils of one of the oldest known human ancestors, our LUCY dataset contains high-quality Computed Tomography (CT) scans of the complete human head before and after orthognathic surgeries, critical for evaluating surgery results. LUCY consists of 144 scans of 72 subjects (31 male and 41 female) where each subject has two CT scans taken pre- and post-orthognathic operations. Based on our LUCY dataset, we learn a novel skeleton consistent parametric facial generator, SCULPTOR, which can create the unique and nuanced facial features that help define a character and at the same time maintain physiological soundness. Our SCULPTOR jointly models the skull, face geometry and face appearance under a unified data-driven framework, by separating the depiction of a 3D face into shape blend shape, pose blend shape and facial expression blend shape. SCULPTOR preserves both anatomic correctness and visual realism in facial generation tasks compared with existing methods. Finally, we showcase the robustness and effectiveness of SCULPTOR in various fancy applications unseen before.Comment: 16 page, 13 fig

Challenges and opportunities in atomistic simulations of glasses: a review

Atomistic modeling and simulations have been pivotal in our understanding of the glassy state. Indeed, atomistic modeling offers direct access to the structure and dynamics of atoms in glasses—which is typically hidden from conventional experiments. Simulations also offer a more economical, faster alternative to systematic experiments to decode composition-property relationships and accelerate the discovery of new glasses with desirable properties and functionalities. However, the atomistic modeling of glasses remains plagued by a series of challenges, e.g., high computational cost, limited accessible timescale, lack of accurate interatomic forcefields, etc. These challenges often result in the existence of discrepancies between simulation and experimental data, thereby limiting the predictive power of atomistic modeling. Here, we review recent accomplishments and remaining challenges facing the atomistic modeling of glasses. We discuss future opportunities offered by the seamless integration of simulations, knowledge, experiments, and machine learning in advancing glass modeling to a new era

Stability analysis of inclined coal seam roadway along goaf considering non-uniform filling of gob gangue

After the mining of inclined coal seam, the roof falling gangue of goaf moves downward to fill the goaf due to gravity, which makes the strata behavior law of gob-side entry retaining in inclined coal seam different from that in horizontal coal seam. In order to study the influence of non - uniform filling of gangue on surrounding rock stability of gob - side entry retaining in inclined coal seam, taking No.3131 machine roadway of Longmenxia South Coal Mine in Sichuan as engineering background. The filling zone length of caving rock in goaf was quantified, and the compaction parameters of rock in quantified goaf were obtained by double yield model inversion. On this basis, a numerical calculation model was established to study the evolution characteristics of surrounding rock stress field and the distribution pattern of plastic zone in the whole service cycle of gob-side entry retaining in inclined coal seam, as well as the stress state and support performance of roadside filling body under bearing state. The results showed that the inclined lengths of filling compaction zone, complete filling zone and partial filling zone in goaf of 3131 working face were 57.20 m, 72.18 m and 10.62 m, respectively. For the gob-side entry retaining of inclined coal seam considering the zoning compaction characteristics of goaf, the abutment pressure in front of working face and the residual abutment pressure in goaf increase with the increase of depth. Compared with the first mining, the peak value and influence range of the abutment pressure in front of the working face under the second mining were significantly increased, and the concentration degree of the lateral abutment pressure was also significantly improved. The rock in the caving zone of goaf had a certain supporting effect on the roof strata of roadway, and the lateral abutment pressure had obvious stress concentration. Affected by repeated mining, the failure range of the plastic zone of the two sides extended along the layer, the plastic zone of roadway roof and goaf roof was connected, and the roof near the high sidewall and the surrounding rock of the two sidewalls were seriously broken and the stability was poor. The bearing stress of gob side of roadside backfill used in the mine was significantly greater than that of roadway side, and the horizontal stress was greater than the vertical stress. The roadside filling body can meet the strength requirements, but the anti-tilting performance was weak. On this basis, the control effect of roadway surrounding rock stability under existing support conditions was analyzed, and the reinforcement support scheme of 3131 machine roadway surrounding rock was put forward

Challenges and opportunities in atomistic simulations of glasses: a review

Atomistic modeling and simulations have been pivotal in our understanding of the glassy state. Indeed, atomistic modeling offers direct access to the structure and dynamics of atoms in glasses—which is typically hidden from conventional experiments. Simulations also offer a more economical, faster alternative to systematic experiments to decode composition-property relationships and accelerate the discovery of new glasses with desirable properties and functionalities. However, the atomistic modeling of glasses remains plagued by a series of challenges, e.g., high computational cost, limited accessible timescale, lack of accurate interatomic forcefields, etc. These challenges often result in the existence of discrepancies between simulation and experimental data, thereby limiting the predictive power of atomistic modeling. Here, we review recent accomplishments and remaining challenges facing the atomistic modeling of glasses. We discuss future opportunities offered by the seamless integration of simulations, knowledge, experiments, and machine learning in advancing glass modeling to a new era