Quantum Annealing Based Difficulty Adjustable Maze Generation

In this paper, the maze generation using quantum annealing is proposed. We reformulate a standard algorithm to generate a maze into a specific form of a quadratic unconstrained binary optimization problem suitable for the input of the quantum annealer. To generate more difficult mazes, we introduce an additional cost function $Q_{update}$ to increase the difficulty. The difficulty is evaluated by the time to solve the maze. To check the efficiency of our scheme to create the maze, we investigated the time-to-solution of a quantum processing unit, classical computer, and hybrid solver.Comment: 14pages, 12figure

Isolation of a Mutant ofArabidopsis thalianaCarrying Two Simultaneous Mutations Affecting Tobacco Mosaic Virus Multiplication within a Single Cell

AbstractTobacco mosaic virus strain Cg (TMV-Cg) infectsA. thalianasystemically. In order to identify host factors involved in the multiplication of TMV-Cg, we isolated a mutant ofA. thalianafrom an M2 population mutagenized by fast neutron irradiation, in which the accumulation of the coat protein in upper systemic leaves was reduced to low levels. The phenotype of the mutant, YS241, was controlled primarily by a single nuclear recessive mutation namedtom2-1,which was distinct fromtom1, a separate mutation which also affects TMV-Cg multiplication. Thetom2-1mutation affected the accumulation of TMV-related RNAs in protoplasts in a tobamovirus-specific manner, suggesting that the wild-typeTOM2gene product is necessary for efficient amplification of TMV-related RNAs within a single cell, through specific interaction with virus-coded factors. Furthermore, we found that YS241 contained a single dominant modifier namedttm1,which increased the efficiency of multiplication of TMV-Cg and a tomato strain of TMV in atom2-1genetic background, both in plants and in protoplasts. We propose that thettm1element might be a translocated form of theTOM2gene

Interconversion of Two GDP-Bound Conformations and Their Selection in an Arf-Family Small G Protein

SummaryADP-ribosylation factor (Arf) and other Arf-family small G proteins participate in many cellular functions via their characteristic GTP/GDP conformational cycles, during which a nucleotide∗Mg2+-binding site communicates with a remote N-terminal helix. However, the conformational interplay between the nucleotides, the helix, the protein core, and Mg2+ has not been fully delineated. Herein, we report a study of the dynamics of an Arf-family protein, Arl8, under various conditions by means of NMR relaxation spectroscopy. The data indicated that, when GDP is bound, the protein core, which does not include the N-terminal helix, reversibly transition between an Arf-family GDP form and another conformation that resembles the Arf-family GTP form. Additionally, we found that the N-terminal helix and Mg2+, respectively, stabilize the aforementioned former and latter conformations in a population-shift manner. Given the dynamics of the conformational changes, we can describe the Arl8 GTP/GDP cycle in terms of an energy diagram

Efficacy of Bidens pilosa Extract against Herpes Simplex Virus Infection In Vitro and In Vivo

The development of strains of herpes simplex virus (HSV) resistant to drugs has been reported among the immunocompromised patients. Thus, there is a need to develop new therapeutic agents for HSV infections. We evaluated the anti-HSV activity of Bidens pilosa (B. pilosa), a tropical weed, in tissue culture cells and a mouse model. B. pilosa extract showed potent virucidal activity. It inhibited plaque formation and suppressed virus yield in Vero and RAW 264.7 cells infected with HSV-1 and HSV-2. Both the binding of virus to host cells and penetration of virus into cells were also blocked by B. pilosa. Furthermore, B. pilosa was effective against thymidine kinase-deficient and phosphonoacetate-resistant HSV-1 strains. B. pilosa treatment increased the survival rate of HSV-infected mice and limited the development of skin lesions. Our results indicate that B. pilosa has anti-HSV activity and is thus a potentially useful medical plant for treatment of HSV infection

Integrated monitoring and reviewing systems for the Rokkasho Spent Fuel Receipt and Storage Facility

The Rokkasho Spent Fuel Receipt and Storage (RSFS) Facility at the Rokkasho Reprocessing Plant (RRP) in Japan is expected to begin operations in 1998. Effective safeguarding by International Atomic Energy Agency (IAEA) and Japan Atomic Energy Bureau (JAEB) inspectors requires monitoring the time of transfer, direction of movement, and number of spent fuel assemblies transferred. At peak throughput, up to 1,000 spent fuel assemblies will be accepted by the facility in a 90-day period. In order for the safeguards inspector to efficiently review the resulting large amounts of inspection information, an unattended monitoring system was developed that integrates containment and surveillance (C/S) video with radiation monitors. This allows for an integrated review of the facility`s radiation data, C/S video, and operator declaration data. This paper presents an outline of the integrated unattended monitoring hardware and associated data reviewing software. The hardware consists of a multicamera optical surveillance (MOS) system radiation monitoring gamma-ray and neutron detector (GRAND) electronics, and an intelligent local operating network (ILON). The ILON was used for time synchronization and MOS video triggers. The new software consists of a suite of tools, each one specific to a single data type: radiation data, surveillance video, and operator declarations. Each tool can be used in a stand-alone mode as a separate ion application or configured to communicate and match time-synchronized data with any of the other tools. A data summary and comparison application (Integrated Review System [IRS]) coordinates the use of all of the data-specific review tools under a single-user interface. It therefore automates and simplifies the importation of data and the data-specific analyses

Multi-Objective Bayesian Optimization with Active Preference Learning

There are a lot of real-world black-box optimization problems that need to optimize multiple criteria simultaneously. However, in a multi-objective optimization (MOO) problem, identifying the whole Pareto front requires the prohibitive search cost, while in many practical scenarios, the decision maker (DM) only needs a specific solution among the set of the Pareto optimal solutions. We propose a Bayesian optimization (BO) approach to identifying the most preferred solution in the MOO with expensive objective functions, in which a Bayesian preference model of the DM is adaptively estimated by an interactive manner based on the two types of supervisions called the pairwise preference and improvement request. To explore the most preferred solution, we define an acquisition function in which the uncertainty both in the objective functions and the DM preference is incorporated. Further, to minimize the interaction cost with the DM, we also propose an active learning strategy for the preference estimation. We empirically demonstrate the effectiveness of our proposed method through the benchmark function optimization and the hyper-parameter optimization problems for machine learning models