Abstract: Techniques and systems for solving a set of constraints are described. Binary decision diagram (BDD) learning can be applied to a proper subset of the first set of constraints to obtain a set of bit-level invariants. The set of bit-level invariants can then be used for solving the set of constraints. The set of bit-level invariants can include (1) forbidden invariants, (2) conditional invariants, and/or (3) bit-level invariants that are determined by applying BDD learning to a conjunction of constraints and range expressions. If multiple implied constraints have a common right-hand-side (RHS) expression, then BDD learning can be applied to the common RHS expression only once.

Abstract: A constraint solver utilizes a modified relaxation process to generate multiple different stimulus stream arrays that comply with multi-dimensional (e.g., 2D or 3D) constraints. First, an array is generated including rows and columns of randomly generated test vector values. During a first revision phase, the array is modified to comply with first-dimension constraints (e.g., selected test vector values are changed in non-compliant rows until every row complies with all row constraints). A second revision phase is then performed in multiple cycles, where each cycle includes identifying a current element having a greatest impact on non-compliance of the array on second-dimension (e.g., column and/or diagonal) constraints, and revising the current element's test vector value in a way that both minimizes the non-compliance, and also maintains compliance of the array with the first-dimension constraints.

Abstract: A method for fabricating a wire strand from parallel steel wires for a main cable of a suspension bridge, the method including: 1) selecting and coloring a steel wire as a marking steel wire which is to be positioned at a vertex of a wire strand including a plurality of parallel steel wires and having an equilateral polygon section; 2) drawing position markers at positions of the standard steel wire corresponding to control points of splay cable saddles, center points of main cable saddles, middle points of side spans, a middle point of a middle span, and starting points of anchor heads of anchor spans of a suspension bridge; 3) relaxing and shaping coils of the steel wires to yield a prefabricated wire strand; 4) preforming the positions of the cable saddles; 5) coiling the wire strand including; and 6) casting anchor of the wire strand.

Abstract: Methods and apparatuses are described for assigning random values to a set of random variables so that the assigned random values satisfy a set of constraints. A constraint solver can receive a set of constraints that is expected to cause performance problems when the system assigns random values to the set of random variables in a manner that satisfies the set of constraints. For example, modulo constraints and bit-slice constraints can cause the system to perform excessive backtracking when the system attempts to assign random values to the set of random variables in a manner that satisfies the set of constraints. The system can rewrite the set of constraints to obtain a new set of constraints that is expected to reduce and/or avoid the performance problems. The system can then assign random values to the set of random variables based on the new set of constraints.

Abstract: A method for fabricating a steel wire cable having a Zn—Al alloy plating, the method including: arranging steel wires according to an arrangement rule at a cross section of the steel wire cable; controlling a length of the overall cable by a length of a central standard wire; twisting a bunch of the steel wires comprising a zinc-aluminum alloy plating with a torsion angle of between 2° and 4°; wrapping the steel wire bunch with a polyester wrapping bandage and covering a resulting product with a double-layered protective polyethylene sheath; anchoring the two ends of the steel wire cable by anchors using fillers; and coiling the finished-product of the steel wire cables.

Abstract: A method for fabricating a wire strand from parallel steel wires for a main cable of a suspension bridge, the method including: 1) selecting and coloring a steel wire as a marking steel wire which is to be positioned at a vertex of a wire strand including a plurality of parallel steel wires and having an equilateral polygon section; 2) drawing position markers at positions of the standard steel wire corresponding to control points of splay cable saddles, center points of main cable saddles, middle points of side spans, a middle point of a middle span, and starting points of anchor heads of anchor spans of a suspension bridge; 3) relaxing and shaping coils of the steel wires to yield a prefabricated wire strand; 4) preforming the positions of the cable saddles; 5) coiling the wire strand including; and 6) casting anchor of the wire strand.

Abstract: Methods and apparatuses are described for assigning random values to a set of random variables so that the assigned random values satisfy a set of constraints. A constraint solver can receive a set of constraints that is expected to cause performance problems when the system assigns random values to the set of random variables in a manner that satisfies the set of constraints. For example, modulo constraints and bit-slice constraints can cause the system to perform excessive backtracking when the system attempts to assign random values to the set of random variables in a manner that satisfies the set of constraints. The system can rewrite the set of constraints to obtain a new set of constraints that is expected to reduce and/or avoid the performance problems. The system can then assign random values to the set of random variables based on the new set of constraints.

Abstract: Methods and apparatuses are described for assigning random values to a set of random variables so that the assigned random values satisfy a set of constraints. A constraint solver can receive a set of constraints that is expected to cause performance problems when the system assigns random values to the set of random variables in a manner that satisfies the set of constraints. For example, modulo constraints and bit-slice constraints can cause the system to perform excessive backtracking when the system attempts to assign random values to the set of random variables in a manner that satisfies the set of constraints. The system can rewrite the set of constraints to obtain a new set of constraints that is expected to reduce and/or avoid the performance problems. The system can then assign random values to the set of random variables based on the new set of constraints.

Abstract: Methods and apparatuses are described for assigning random values to a set of random variables so that the assigned random values satisfy a set of constraints. An embodiment can receive a set of constraints, wherein each constraint is defined over one or more random variables from a set of random variables. Next, the embodiment can generate a circuit model based on the set of constraints, wherein assignable values for at least one node in the circuit model are represented in multiple value systems. The embodiment can then assign random values to the set of random variables based on the circuit model.

Abstract: Methods and apparatuses are described for assigning random values to a set of random variables so that the assigned random values satisfy a set of constraints. An embodiment can receive a set of constraints, wherein each constraint is defined over one or more random variables from a set of random variables. Next, the embodiment can generate a circuit model based on the set of constraints, wherein assignable values for at least one node in the circuit model are represented in multiple value systems. The embodiment can then assign random values to the set of random variables based on the circuit model.

Abstract: Methods and apparatuses are described for assigning random values to a set of random variables so that the assigned random values satisfy a set of constraints. A constraint solver can receive a set of constraints that is expected to cause performance problems when the system assigns random values to the set of random variables in a manner that satisfies the set of constraints. For example, modulo constraints and bit-slice constraints can cause the system to perform excessive backtracking when the system attempts to assign random values to the set of random variables in a manner that satisfies the set of constraints. The system can rewrite the set of constraints to obtain a new set of constraints that is expected to reduce and/or avoid the performance problems. The system can then assign random values to the set of random variables based on the new set of constraints.

Abstract: Some embodiments provide a system for adaptively performing state-to-symbolic transformation in a canonical representation which is used for generating random stimulus for a constrained-random simulation. The system can construct a canonical representation for a set of constraints using the set of random variables and the subset of the state variables in the constraints. Next, the system can use the canonical representation to generate random stimulus for the constrained-random simulation, and monitor parameters associated with the constrained-random simulation. Next, the system can add state variables to or remove state variables from the canonical representation based at least on the monitored parameters. The system can then use the modified canonical representation which has a different set of state variables to generate random stimulus for the constrained-random simulation.

Abstract: Some embodiments provide a system for adaptively performing state-to-symbolic transformation in a canonical representation which is used for generating random stimulus for a constrained-random simulation. The system can construct a canonical representation for a set of constraints using the set of random variables and the subset of the state variables in the constraints. Next, the system can use the canonical representation to generate random stimulus for the constrained-random simulation, and monitor parameters associated with the constrained-random simulation. Next, the system can add state variables to or remove state variables from the canonical representation based at least on the monitored parameters. The system can then use the modified canonical representation which has a different set of state variables to generate random stimulus for the constrained-random simulation.

Abstract: A mouse having a marking function is provided. The mouse includes a first button, a second button, and a marking function triggering button. A mouse driver including a marking program is installed in the computer host. When the marking function triggering button is depressed, the marking program is activated. In response to a first button signal and a second button signal generated from the first button and the second button, various marking functions of the marking program are implemented.

Abstract: A mouse having a screen capture function is provided. The mouse includes a first button and a screen capture function triggering button. A mouse driver including a screen capture program is installed in the computer host. When the screen capture function triggering button is depressed, the screen capture program is activated. In response to a first button signal generated from the first button, an image of a selected area on the screen is captured.