Higher-order protection of quantum gates: Hamiltonian engineering coordinated with dynamical decoupling

Dynamical decoupling represents an active approach towards the protection of quantum memories and quantum gates. Because dynamical decoupling operations can interfere with system's own time evolution, the protection of quantum gates is more challenging than that of quantum states. In this work, we put forward a simple but general approach towards the realization of higher-order protection of quantum gates. The central idea of our approach is to engineer (hence regain the control of) the quantum gate Hamiltonian in coordination with higher-order dynamical decoupling sequences originally proposed for the protection of quantum memories. In our computational examples presented for illustration, the required engineering can be implemented by only quenching the phase of an external driving field at particular times.Comment: 5 pages, 2 figures. Improved discussion

A direct unified wave-particle method for simulating non-equilibrium flows

In this work, the Navier-Stokes (NS) solver is combined with the Direct simulation Monte Carlo (DSMC) solver in a direct way, under the wave-particle formulation [J. Comput. Phys. 401, 108977 (2020)]. Different from the classical domain decomposition method with buffer zone for overlap, in the proposed direct unified wave-particle (DUWP) method, the NS solver is coupled with DSMC solver on the level of algorithm. Automatically, in the rarefied flow regime, the DSMC solver leads the simulation, while the NS solver leads the continuum flow simulation. Thus advantages of accuracy and efficiency are both taken. At internal flow regimes, like the transition flow regime, the method is accurate as well because a kind of mesoscopic modeling is proposed in this work, which gives the DUWP method the multi-scale property. Specifically, as to the collision process, at $t < \tau$, it is supposed that only single collision happens, and the collision term of DSMC is just used. At $t > \tau$, it is derived that $1-\tau/\Delta t$ of particles should experience multiple collisions, which will be absorbed into the wave part and calculated by the NS solver. Then the DSMC and NS solver can be coupled in a direct and simple way, bringing about multi-scale property. The governing equation is derived and named as multi-scale Boltzmann equation. Different from the original wave-particle method, in the proposed DUWP method, the wave-particle formulation is no more restricted by the Boltzmann-BGK type model and the enormous research findings of DSMC and NS solvers can be utilized into much more complicated flows, like the thermochemical non-equilibrium flow. In this work, one-dimensional cases in monatomic argon gas are preliminarily tested, such as shock structures and Sod shock tubes

Stilling and its Aerodynamic Effects on Pan Evaporation

Declines in wind speed (u) (termed as “stilling”) has been reported in many regions of the world. To explore the temporal trends of u and its aerodynamic effects is vital to understand the changes in water resources. This study analyzed the changes of temporal trends for u and its aerodynamic effects using the data during 1959-2000 at 266 stations across China. The improved PenPan model was used to estimate pan evaporation (Epan) and quantify the contribution of radiative and aerodynamic components (aerodynamic component separated into wind speed u, vapour pressure deficit D, and air temperature Ta). Climate factors include Epan measured with the standard Chinese 20 cm diameter pan, u, Ta, relative humidity (rh) and sunshine hours (sh). The results showed: stilling occurred in most of stations (206 among 266) and 105 stations presented significant decreasing trends at 99% confidence level; stilling was the main cause for controlling the trends in Epan in most part of China, especially in the west and north of China. The results indicated that decreasing trends in Epan due to stilling would inevitably alter water resources, and should be put further investigation incorporation other factors

Real-time Model Predictive Control and System Identification Using Differentiable Physics Simulation

Developing robot controllers in a simulated environment is advantageous but transferring the controllers to the target environment presents challenges, often referred to as the "sim-to-real gap". We present a method for continuous improvement of modeling and control after deploying the robot to a dynamically-changing target environment. We develop a differentiable physics simulation framework that performs online system identification and optimal control simultaneously, using the incoming observations from the target environment in real time. To ensure robust system identification against noisy observations, we devise an algorithm to assess the confidence of our estimated parameters, using numerical analysis of the dynamic equations. To ensure real-time optimal control, we adaptively schedule the optimization window in the future so that the optimized actions can be replenished faster than they are consumed, while staying as up-to-date with new sensor information as possible. The constant re-planning based on a constantly improved model allows the robot to swiftly adapt to the changing environment and utilize real-world data in the most sample-efficient way. Thanks to a fast differentiable physics simulator, the optimization for both system identification and control can be solved efficiently for robots operating in real time. We demonstrate our method on a set of examples in simulation and show that our results are favorable compared to baseline methods

FACILITATING NO-NOTICE EVACUATION THROUGH OPTIMAL PICK-UP LOCATION SELECTION

Under no-notice disasters, dependents in facilities such as schools and daycare centers usually wait for their families to pick them up. This family pickup behavior could increase individual evacuation time and cause extra delay to other vehicles in the network. Relocating the dependents to other pickup sites may facilitate no-notice evacuation. This study developed an optimization model to determine optimal pickup locations, assuming that all evacuating families have personal vehicles; the objective is to maximize the number of evacuees who can successfully pick up dependents and then escape from the dangerous zones within a safe evacuation time threshold. The optimization model was based on anticipated travel time output from the simulation model (VISSIM in this study); iteration between the two models was performed. The methodology was applied to a case study based on a simplified version of Chicago Heights, Illinois. The case study involved three facilities with 492 dependents and three safe time thresholds (i.e., 30, 45 and 60 minutes). Improvements in total travel time, average speed, total delay time and average delay time per vehicle and increases in the number of successful evacuations of dependents were used to evaluate the performance of the relocation strategy. This study also examined the sensitivity of the strategy to parents&apos; arrival time, number of dependents, and safe time. Finally, relocation sites were recommended based on the results of all scenarios. The results found that the relocation strategy was sensitive to safe evacuation time and number of pickup evacuees (pickup evacuees refer to those persons with a need to pick up their dependents inside the dangerous zones). The relocation strategy was prominently effective when safe evacuation time fell into a moderate range or the number of pickup evacuees was fairly high. Application of the proposed methodology to a certain area can assist local decision-makers to take effective measures during no-notice evacuation and the relocation sites could be part of local evacuation management plans. Abstract Under no-notice disasters, dependents in facilities such as schools and daycare centers usually wait for their families to pick them up. This family pickup behavior could increase individual evacuation time and cause extra delay to other vehicles in the network. Relocating the dependents to other pickup sites may facilitate no-notice evacuation. This study developed an optimization model to determine optimal pickup locations, assuming that all evacuating families have personal vehicles; the objective is to maximize the number of evacuees who can successfully pick up dependents and then escape from the dangerous zones within a safe evacuation time threshold. The optimization model was based on anticipated travel time output from the simulation model (VISSIM in this study); iteration between the two models was performed. The methodology was applied to a case study based on a simplified version of Chicago Heights, Illinois. The case study involved three facilities with 492 dependents and three safe time thresholds (i.e., 30, 45 and 60 minutes). Improvements in total travel time, average speed, total delay time and average delay time per vehicle and increases in the number of successful evacuations of dependents were used to evaluate the performance of the relocation strategy. This study also examined the sensitivity of the strategy to parents&apos; arrival time, number of dependents, and safe time. Finally, relocation sites were recommended based on the results of all scenarios. The results found that the relocation strategy was sensitive to safe evacuation time and number of pickup evacuees (pickup evacuees refer to those persons with a need to pick up their dependents inside the dangerous zones). The relocation strategy was prominently effective when safe evacuation time fell into a moderate range or the number of pickup evacuees was fairly high. Application of the proposed methodology to a certain area can assist local decision-makers to take effective measures during no-notice evacuation and the relocation sites could be part of local evacuation management plans. 3 The primary purpose of a no-notice evacuation is to save people&apos;s lives Evacuation time In a short-notice case, people can choose whether to evacuate or not, and when to evacuate In a no-notice scenario, almost everyone in the impacted area evacuates at the time of the disaster occurrence Origin evacuation place In a &quot;short notice&quot; evacuation case, evacuees often start from their homes together with their families. In a &quot;no-notice&quot; scenario, evacuees start evacuation from wherever they are at that time, (i.e., schools, work places, entertainment place, etc.) most likely by themselves. Family gathering process Families gather together before they start to evacuate. Families gather together during or after the evacuation When a no-notice disaster occurs during the daytime, household members may be scattered throughout the road network. Household dependents in facilities such as schools and daycare centers may wait for their families to pick them up; this family pickup behavior could increase individual evacuation time and cause extra delay for other vehicles in the network. When a large amount of vehicles rush into certain places to pick up dependents within a short period of time, bottlenecks could easily be formed for those locations whose entries/exits cannot accommodate heavy traffic. Facilities&apos; current locations may not be well designed for the emergency case and limited entry/exit by itself could be a bottleneck. Relocating facilities&apos; dependents to appropriate sites would eliminate unnecessary bottlenecks and smooth road traffic. Therefore, this study addresses selecting appropriate pickup locations to facilitate no-notice evacuation. A school is a typical facility having a number of carless dependents who need to be picked up. Most school districts have developed an emergency plan, which in summary, requests three kinds of action, i.e., shelter in place, lockdown, and off-site evacuation. According to the Office of the Superintendent, Arlington High School, Massachusetts 7 (2006), shelter-in-place is used when a danger happens outside the school, such as a chemical spill; lock-down is used when a danger is inside the school and makes evacuation impractical; off-site evacuation is used in an extreme emergency situation, and an evacuation location such as another school, church, Boys &amp; Girls Club, Town Hall or ice skating rink, are prearranged for each individual school. This school&apos;s existing emergency plans demonstrate that moving dependents to other sites is a feasible strategy. Moreover, some previous studies have involved this issue; for example, Sinuany-Stern and Stern (1993) studied relocating carless people under an emergency situation, where carless households are assumed to move to a certain point first and are then picked up by organized transportation and transported to the shelter, and the households are assumed to use shortcuts and not interfere with road traffic. This study develops a mathematical model to determine optimal pickup locations for facilities; the objective is to maximize the number of evacuees who can successfully pick up dependents and escape from the dangerous zones afterwards. The model is tested for a given network based on the City of Chicago Heights, Illinois. This report is organized as follows. Chapter 1 describes the background and purpose of this study. Chapter 2 reviews the previous studies on evacuation modeling and the location problem. Chapter 3 formulates the optimization model and explains the methodologies adopted in this study. Chapter 4 describes basic information of the case study in Chicago Heights, such as the network, demand, assumptions and scenarios, and presents the results and the sensitivity analysis. Chapter 5 concludes the study and discusses the future directions. Chapter 2 Literature Review This chapter mainly reviews the previous studies on evacuation modeling. Numerous studies on evacuation planning and modeling were conducted since the 1980s, driven by tragic events such as the Three Mile Island nuclear reactor incident in 1979, September 11 terrorist attacks in 2001, and Hurricanes Katrina and Rita in 2005. Those studies generally focused on estimating evacuation time and determining optimal evacuation routes and optimal shelter locations, using operations research methods and simulation models. Evacuation studies, according to scopes and features of impacted areas, fall into five general categories: regions, neighborhoods, buildings, ships, and airplanes Regional Evacuation Regional (urban) evacuation models can be classified into aggregate models and disaggregate models. An aggregate model investigates a group of vehicles as a whole, while a disaggregate model evaluates each individual driver&apos;s behavior. An aggregate model overlooks the difference of individual driver&apos;s behavior among the population. The model developed in this study is a disaggregate model that relies on microsimulation. Aggregate Evacuation Modeling Most evacuation models were developed on an aggregate level and simulation-based (macroscopic), such as NETVAC, DYNEV, MASSVAC, and TEVACS; most of these models were dealing with hurricanes or nuclear plant incidents, as both are among the most frequent and severe disasters in the United Stated. Few previous works exist regarding regional evacuation models on the micro simulation level In general, most of the aforementioned models are capable of estimating network clearance time and identifying evacuation bottlenecks of the network. Most of these simulators assume that the evacuation process has reached equilibrium, thereby estimating evacuation time based on determined equilibrium traffic flow. However, under abnormal situations such as evacuation, equilibrium of road traffic is hard to achieve due to the practical reason that no historical experience exists for evacuees to choose routes and minimize their evacuation time; this is contrary to normal situations recurring almost every day, in which travelers can learn from past experience to choose routes 9 with minimum travel time Disaggregate Evacuation Modeling The previously mentioned models are aggregate as they do not consider an individual&apos;s behavior while modeling the evacuation progress. Stern and Sinuany-Stern (1989) first incorporated some behavior-related parameters, including diffusion time of evacuation instruction and individuals&apos; preparation time, in a microscopic simulation model for an urban evacuation. Later Sinuany-Stern and Stern (1993) developed the SLAM Network Evacuation Model (SNEM) based on this behavioral-based model to test the effects of traffic factors (e.g. household size, car ownership and intersection traversing time) and route choice parameters on network clearance time. Sinuany-Stern and Stern&apos;s work assumes that household members are together when a disaster occurs; therefore it takes households as entities, instead of individual household members. In reality, family members could be scattered at different places under nonotice evacuation. Murray-Tuite and Mahmassani Neighborhood Evacuation Less attention was paid to the subject of neighborhood-scale evacuation under an emergency during the last twenty years, compared to region-scale evacuation or building evacuation No-notice Evacuation Recently, more and more focus is placed on no-notice evacuation. As a no-notice disaster requires quick response, real time estimation tools are important, for which computation time is a critical issue. Chiu et al. Shelter Location Many other previous studies focused on different specific aspects of an emergency evacuation. One is that the location of shelters may influence network clearance time significantly under hurricane evacuation. Facility Location Problem This study involves relocating dependents at facilities to make an evacuation efficient, so we here provide an overview of basic facility location problems. The facility location problem is a critical issue for strategic planning of a wide range of enterprises, e.g., a retailer chooses where to locate a store or a city planner selects locations of fire stations based on a set of rules (Owen and Diskin, 1998). Basic location problems, such as the P-median, P-center, set covering and maximal covering problems, are reviewed by Owen and Diskin (1998). The P-median problem is to locate P facilities in order to minimize the total travel cost between demands and facilities; the P-Center problem, also called the minmax problem, is to locate P facilities so as to minimize the maximum travel cost between a demand and its nearest facility; the set covering problem is to locate the minimum number of facilities that will serve all demands within a specified time; the maximal covering problem is to place P facilities with the goal to maximize the amount of demand covered within an acceptable distance between demands and facilities (Owen and Diskin, 1998). The P-Center, set covering and maximal covering problems can all be applied to locate emergency 11 medical services (EMS); the P-center and maximal covering problems are used to locate a given number of EMS, and the set covering problem is used to determine the least number of EMS to cover all population of a certain area. The above mentioned basic location problems do not account for location costs, which limits their application to practical problems. The fixed charge facility location problems are thus introduced with a fixed cost for each potential location site, and categorized as uncapacitated and capacitated according to whether facility capacities are incorporated or not (Owen and Diskin, 1998). The fixed charge facility location problems determine the number of facilities located endogenously, rather than pre-specified as in median and center problems. However, without considering varying costs associated with flows between facilities and demands, fixed charge problems still cannot solve such a problem as locating a warehouse, which is a general case in industry and needs to find the best shipments between facilities and customers. Hence, the location-allocation problems incorporate flow allocation between demands and facilities into a basic location problem (usually a median problem or a fixed charge problem) (Owen and Diskin, 1998). The location problem presented in this study is essentially a P-center problem that locates students to minimize the maximum of a pickup travel time. The flow allocation is not the case of this study as it is predetermined which parent picks up which child. The previous works provide valuable contributions to the emergency evacuation field, however most of them omit family gathering behavior under no-notice evacuation conditions. This omission could lead to optimistic estimates of evacuation time. This report explores this issue and considers the fact that parents need to pick up their carless household members during an evacuation. A strategy of relocating dependents to more accessible sites to facilitate no-notice evacuation is proposed and evaluated in this report. Chapter 3 Methodology This chapter describes the methodology adopted in this study. First, an integer optimization model is formulated to determine optimal relocation sites for facilities. The microscopic simulation model is then introduced to provide zonal travel time information for the optimization model. As the two models interact with each other, iteration between them is performed to achieve the &quot;real&quot; optimal point. A procedure to accomplish this iteration process is illustrated by a chart and explained step by step. This chapter also includes the methodology to generate the trip chain. Optimization Model A no-notice evacuation usually starts at the moment when a disaster is confirmed or announced, then evacuees surge onto the road network in a very short time and cause traffic to dramatically change. In this study, time-dependent road traffic is taken into consideration by discretizing the evacuation period into several time intervals and capturing the travel time for each time interval. A mathematical optimization model was developed to find optimal relocation sites for facilities such as schools and daycare centers. Unlike a short-notice evacuation that seeks reductions in total evacuation time or personal property loss, a no-notice evacuation aims at maximizing the number of evacuees successfully escaping from the dangerous zones or minimizing total fatalities or injuries within a given time threshold (Chiu et al., 2006; is an index of pickup evacuees&apos; origin nodes; j is an index of pickup evacuees&apos; destination nodes; k is an index of current locations of facilities; l is an index of possible relocation sites for facilities; a is an index of time interval, a th ; x kl are binary integer decision variables. x kl = 1, if we assign facility k to site l; 0 otherwise; Ф is the average number of dependents a pickup evacuee gathers at a facility. Equation (1-1) calculates that the number of successful pickup evacuees for each i, j, l; p ijl S is the number of pickup evacuees (originating from i and evacuating to j) with dependent(s) relocated to l from all facilities before the last safe time interval, ijl A . Equation (1) determines the total number of successful pickup evacuees by summating p ijl S over i, j, l. Equation (2) requires the number of dependents relocated to possible site l to be no greater than facility l&apos;s capacity. The average number of dependents for a pickup evacuee is assumed to be the same over all of the facilities. Multiple intermediate stops for a pickup trip are not considered in this study; parents who have more than one dependent in the dangerous zone are assumed to have them in one facility. Equation (3) restricts the relocation site to a walkable distance (0.5 miles) from the original site. Equation (4) guarantees that a facility is assigned to one and only one relocation site. Facilities&apos; current locations are also treated as possible relocation sites. Equation A ijl is an input to the optimization model and based on travel time from micro-simulation. Micro-simulation provides travel time of multiple paths for each OD pair; the path with the least travel time is assumed to be selected by pickup evacuees. Equations is the duration of a time interval (sec); ) ( E is the expected value of (sec). Equation Traffic Simulation Model Microscopic simulation outputs travel time among origins, destinations, and facility/relocation sites for the optimization model. Micro-simulation was chosen instead of simulation models on other levels, because it can model the road network in great detail, has the ability to model queues, and reflects the impacts of facilities&apos; entry/exit configuration on travel time, which is crucial for the special case here. VISSIM, part of the PTV VISION traffic analysis package, was used in this study. VISSIM is a driver behavior based, second by second microscopic traffic simulation program, and developed to model major elements of transportation systems, such as lane configuration, vehicle composition, driver behavior, traffic controls and so on VISSIM&apos;s built-in dynamic traffic assignment algorithm was used to find routes for pickup evacuees. VISSIM accomplishes dynamic assignment procedures by iterated simulation runs. For each iterated run, drivers make decisions on route choice based on road traffic situations they experienced from the previous iterations. After multiple simulation runs, the iterations end when network traffic reaches stability, defined in VISSIM as when travel times or volumes do not vary significantly between two consecutive runs Framework The road traffic situation determines optimal relocation sites; reversely, relocation sites affect road traffic. In this study, relocation sites are determined by the optimization model and the network traffic is modeled using the simulation model, VISSIM. The optimization model uses travel time output from VISSIM, which assumes current facility locations as pickup points at the beginning. When the optimization model finds new relocation sites, travel time from VISSIM should be updated accordingly; as a result, these determined optimal sites may not be &quot;real&quot;. In order to achieve &quot;real&quot; optimal sites, iteration between the two models is performed until convergence is reached. A procedure to accomplish this iteration process is shown in Figure 1 Flowchart of the Study In this procedure, first, the road network under normal conditions is simulated in VISSIM and normal travel times are achieved and adopted by the optimization model to be initial travel time. Then, micro simulation for emergency situations is iteratively executed with the optimization model until the termination criteria are satisfied, to determine optimal relocation sites. Facility current locations are set to be initial pickup locations; during each iteration, new relocation sites are found, and the travel time corresponding to those new sites is updated accordingly. The procedure follows the steps below: Step 0. Run VISSIM to gain travel time in a normal situation (without pickup evacuees considered). As VISSIM can only output travel time for those OD pairs with actual vehicles dispatched, we generate dummy trips for specific OD pairs to collect the travel times needed. a) Specify OD pairs we need to collect travel times for; b) Divide the simulation time period into several periods 1 ; for each period, generate one du

Association between -238 but not -308 polymorphism of Tumor necrosis factor alpha (TNF-alpha)v and unexplained recurrent spontaneous abortion (URSA) in Chinese population

<p>Abstract</p> <p>Objectives</p> <p>TNF-alpha is a critical cytokine produced by Th1 cells while altered T helper 1 (Th1)-Th2 balance is found crucial for a successful pregnancy.</p> <p>Study Design</p> <p>A cohort of 132 Southern Chinese Han RSA patients and 152 controls constituted the subjects of this study. Two functional polymorphisms -308 and -238 of TNF-alpha were studied by association analysis.</p> <p>Results</p> <p>lack of association was found in TNF-alpha -308 SNP yet a significant difference was discovered in -238 polymorphism.</p> <p>Conclusion</p> <p>This study suggested that TNF-alpha may be a risk factor in Chinese RSA patients. However the ethnic differences may also contribute to the results.</p