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An automated analysis system that identifies detectability problems, diagnosability problems, and possible ways to change rank order of diagnoses in a diagnostic system and makes the problems and possible improvements visible to test programmers to aid in test improvement. Components that have no coverage and components that have inadequate coverage (according to a heuristic criteria) are identified as potential detectability problems. Components that are exercised by identical operations in all tests are identified as diagnosability problems. If an incorrect diagnosis is made, the automated analysis system identifies failing tests that have no coverage of any component in the true failure cause. In addition, if an incorrect diagnosis is made, the automated analysis system identifies ways of changing the rank order of diagnoses, including coverages that can be reduced and identification of operation violations that can be eliminated or deliberately added. If no historical data are available, a "diagnosability index" may be computed by randomly sampling from the set of possible failure syndromes and observing the frequencies with which ties occur among the weights of the top-ranked candidate diagnoses. After historical data becomes available, a diagnosability index may be computed from the frequency with which two candidate diagnoses are assigned identical weights by the model-based diagnostic system over a set of representative failures.
1. A method of identifying improvements to a model-based test system, the model-based test system including a test suite and a model, the method comprising the following steps:
(a) automatically identifying improvements to the test suite;
(b) automatically identifying improvements to the model; and
(c) outputting the improvements identified in steps (a) and (b).
2. The method of claim 1, step (a) further comprising:
identifying components in which failures are not likely detectable.
3. The method of claim 2, step (a) further comprising:
identifying components that are not exercised by any test.
4. The method of claim 2, step (a) further comprising:
identifying components that are inadequately exercised according to a predetermined criteria.
5. The method of claim 1, step (a) further comprising:
identifying pairs of components for which one component cannot be distinguished from the other.
6. The method of claim 5, step (a) further comprising:
identifying sets of components that are exercised by identical operations in all tests.
7. The method of claim 1, step (a) further comprising:
identifying pairs of components for which one component is unlikely to be distinguished from the other.
8. The method of claim 1, wherein the model-based diagnostic system has misidentified a particular set of components as the cause of a set of failing tests instead of identifying the true failure cause components as the cause of the set of failing tests, step (b) further comprising:
identifying probable modeling errors causing the misidentification.
9. The method of claim 8, step (b) further comprising:
identifying components of the true failure cause that are not exercised by the set of failing tests.
10. The method of claim 8, step (b) further comprising:
performing utilization sensitivity analysis on the particular set of components.
11. The method of claim 8, step (c) further comprising:
identifying ways of changing the misidentification by modifying operation violations.
12. A method of identifying improvements to a test suite, the test suite based on a model of a system, the mode not including a description of connection of components in the system, the method comprising the following steps:
(a) automatically identifying components, the failure of which has a probability of detectability by the test suite that is low but greater than zero; and
(b) outputting identities of components identified in step (a).
13. The method of claim 12, further comprising the steps of:
identifying components that are not exercised by any test.
14. The method of claim 12, step (a) further comprising:
identifying components that are inadequately exercised according to a predetermined criteria.
15. A method of identifying improvements to a test suite, the test suite based on a model of a system, the model not including a description of connection of components in the system the method comprising the following steps:
(a) automatically identifying sets of components that are exercised by identical operations in all tests; and
(b) outputting identities of components identified in step (a).
16. A method of identifying improvements to a test suite, the method comprising the following steps:
(a) automatically identifying pairs of components for which one component has a probability that is low but greater than zero from being distinguished from the other; and
(b) outputting identities of the components identified in step (a).
17. A method of identifying improvements to a model-based diagnostic system, wherein the model-based diagnostic system has misidentified a particular set of components as the cause of a set of failing tests instead of identifying the true failure cause components as the cause of the set of failing tests, the method comprising:
(a) automatically identifying components of the true failure cause that are not exercised by the set of failing tests; and
(b) outputting identities of components identified in step (a).
18. A method of identifying improvements to a model-based diagnostic system, wherein the model-based diagnostic system has misidentified a particular set of components as the cause of a set of failing tests instead of identifying the true failure cause components as the cause of the set of failing tests, the method comprising:
(a) automatically identifying a way to increase a diagnostic weight for the true failure cause components; and
(b) outputting the identity of the way of increasing a diagnostic weight identified in step (a).
19. The method of claim 18, step (a) further comprising:
identifying a coverage that may be altered for a set of components within the true failure cause components, resulting in an increased diagnostic weight for the true failure cause components.
20. The method of claim 18, step (a) further comprising:
identifying a particular operation that may be removed from at least one test, the particular operation exercising at least one of the true failure cause components and the particular operation having an operation violation before removal from at least one test and having no operation violation after removal from at least one test.
21. The method of claim 18, step (a) further comprising:
identifying a particular operation that may be added to at least one failing test, the additional operation being one that exercises at least one of the true failure cause components and does not have an operation violation.
22. A method of identifying improvements to a model-based diagnostic system, wherein the model-based diagnostic system has misidentified a particular set of components as the cause of a set of failing tests instead of identifying the true failure cause components as the cause of the set of failing tests, the method comprising:
(a) automatically identifying a way to decrease a diagnostic weight for the particular set of components; and
(b) outputting the identity of the way of decreasing a diagnostic weight identified in step (a).
23. The method of claim 22, step (a) further comprising:
identifying a coverage that may be altered for the particular set of components, resulting in a decreased diagnostic weight for the particular set of components.
24. The method of claim 22, step (a) further comprising:
identifying a particular operation that may be added to at least one failing test, the particular operation being one that exercises at least one of the particular set of components and will have an operation violation when added.
This is a continuation of application Ser. No. 08/825,260 filed on Mar. 27, 1997, now abandoned which is a continuation of application Ser. No. 08/612,645 filed on Mar. 8, 1996, now abandoned.
This invention relates generally to automated diagnosis of failure causes from functional tests of a system. In particular, the invention relates to enhancements of a model-based diagnostic system including automated identification of test suite problems and possible modeling problems.
Automated diagnosis of faults in a system can take many forms. One form of diagnosis is history-based. History-based diagnostic systems form a diagnosis based on a substantial body of historical failure data and symptoms. Gathering historical data requires repeated testing over time and assumes availability of true failure cause (TFC) information. An alternative approach is model-based. Model-based systems compare system responses to expected responses, based on computer models of the unit under test (UUT) or individual components. Model-based test systems are typically complex, often requiring manual entry of a substantial amount of data regarding design and structure. Models may require extensive data from experts or designers having specialized knowledge of the system to be tested. History-based test systems and complex model-based test systems are often impractical or are often not cost effective for UUT's that are in the prototype stage, UUT's that are undergoing frequent design revisions, UUT's that have short lifetimes, UUT's that are low-cost, or UUT's that are produced in limited quantities.
U.S. patent application Ser. No. 08/551,054 to Christopher Preist and David Allport (Preist et al), having the same assignee as the present application, discloses a model-based diagnostic system, based on functional tests, in which the modeling burden is greatly reduced. The model requires only a list of functional tests, a list of components exercised by each functional test along with the degree to which each component is exercised by each functional test, and (if available) the historical failure rate for individual components. The data to be entered may be rapidly and easily determined by test programmers or others familiar with, but not necessarily expert on, the UUT. Typically, the models may be developed by test programmers in a few days to a few weeks depending on the complexity of the UUT. The diagnostic system disclosed by Preist et al is particularly well suited to UUT's in the prototype stage, UUT's produced in limited quantities, UUT's with frequent design changes, UUT's having short life cycles, and UUT's where rapid turn on is important.
The diagnostic system disclosed by Preist et al is especially applicable to diagnosis of failures of electronic components on printed circuit boards. In general, however, the techniques apply to diagnosis of components in any system. For example, components may be printed circuit assemblies or other modules, components may be computers in a network, or components may be electromechanical components in an automobile or airplane. The general concepts are also applicable to medical diagnosis.
The present application deals with automated analysis of particular applications of the diagnostic system disclosed by Preist et al. Before stating the problems to be solved by the invention, a brief description of the diagnostic system is provided below. First, additional detail for some of the data bases is provided. Next, diagnosis is described, with two example methods of assigning weights for ranking candidate diagnoses. Then, the problems to be solved by the invention are described.
In this application, an operation is a process or action carried out by one or more functional tests. For example, a memory test may include a "read memory" operation and a "write memory" operation. Each operation exercises a specific set of components. In this application, the terms "coverage" or "utilization" may be used interchangeably to mean the extent to which a component is exercised by a particular test. Coverages may be specified either numerically (for example, as a percentage or as a fraction between 0 and 1) or categorically (for example, low, medium and high).
In the diagnostic system disclosed by Preist et al, the model comprises the following data structures:
(a) A data base of components and subcomponents. If components or subcomponents have had any prior testing or if failure rate data are available for components or subcomponents, these data are also included.
(b) A data base for mapping raw test results into categorical information (in the simplest case, pass/fail). For example, if the acceptable (pass) range for a particular voltage measurement is 4.5 V-5.5 V, a numerical test result of 4.0 V might map into a fail-low category.
(c) A functional test model in the form of a data base. Tests are defined as lists of operations. Each operation definition specifies each component or subcomponent exercised by the operation and an estimate of the coverage (degree exercised) of the component by the operation.
(d) Failure specifications, including indict lists and acquit lists. This allows the programmer to specify, if a particular failure occurs, a list of candidate components potentially responsible for the failure (indict list) and/or a list of components that must be functioning correctly to a certain degree (acquit list). For example, consider a functional test of an internal combustion automobile starting system. If the engine turns over at a specified RPM but does not start, the fuel system and ignition systems are suspect (indict list) but the battery and starter motor must be good (acquit list).
As an example of data for the above data bases, consider functional testing of a printed circuit board. One test is a memory test. Memory test includes two operations: access-- memory and output-- to-- busport. The printed circuit board includes the following components: a central processing unit (CPU), a random access memory system (RAM), a databus, an input/output port (port), and a display module. In addition, the random access memory system includes memory and a decoder as subcomponents. The access-- memory operation exercises 10% of the functionality of the CPU, 90% of the functionality of the RAM decoder, and 10% of the functionality of the RAM memory. The output-- to-- busport operation exercises 50% of the functionality of the databus and exercises 90% of the functionality of the port. The model then includes the following items:
______________________________________ components: CPU RAM databus port display module RAM includes subcomponents: memory decoder Operations: access-- memory CPU; .1 RAM, decoder; .9 RAM, memory; .1 output-- to-- busport databus; .5 port; .9______________________________________
The diagnostic system described above uses only data that are readily available at design time. Modeling does not require gathering of historical failure data of the system (although historical failure rates for individual components can be used if available). In the most simple embodiment, the diagnostic system merely requires entry of which components are tested by each operation. In an improved embodiment, the diagnostic system requires entry of the approximate degree to which each component is tested by each operation. This is data that can be provided by a designer or a test programmer. In particular, the diagnostic system does not require structural information (that is, data such as: the output of component A is connected to an input of component B), or failure models (that is, data such as: if test A fails then the most likely cause is component B) or behavioral models (that is, data such as: if both inputs to the NAND gate are high the output is low).
Once the model is defined, the functional tests are executed and failure data are collected. The diagnostic system then determines a diagnosis in three phases. In the first phase, a data abstraction module categorizes each test result as either passing or belonging to one of several possible failure categories. The data abstraction module is not relevant to the present application and is not described in detail here. In the second phase, given the failure results from the data abstraction module, candidate diagnoses are determined. A candidate diagnosis is a minimal set of components, which, if faulty, is capable of explaining all failing test results. Stated differently, every failing test must utilize at least one component in diagnosis D for D to be a candidate diagnosis. The method for determining candidate diagnoses is based on hitting sets, generally described for example in Reiter, R., "A Theory of Diagnosis from First Principles", Artificial Intelligence 32 (1987) 57-95. In the third phase, a relative weight or ranking is assigned to each of the candidate diagnoses. Two example methods of assigning weights are described below, but other types of evidential reasoning may also be used to rank possible diagnoses. Each of the methods described below has the advantage of being computationally efficient.
In the first method for assigning weights, an assumption is made that the probability of a test failing given that a particular component is known to be faulty is proportional to the utilization of that component by that test, and an assumption is made that components fail independently. These assumptions are reasonable in many situations. For example, if all failures are point failures, and all point failures are equally likely, then utilization and the probability of failure are proportional. The assumption leads to the following equation for assigning weights:
W(D,R)=p(D)*p(R|ID) * (operation violation penalty)Equation (1)
D={C1, C2, . . . CM } is a candidate diagnosis (a set of components presumed faulty).
R={R1, R2, . . . RN } is a set of test results.
p(D) is the prior probability of the candidate diagnosis. That is, p(D) is the probability of the components involved in the candidate diagnosis failing given only that some tests have failed (that is, with no information regarding which tests have failed or in what manner). This information is optional and may be omitted if unknown.
p(R|D) is the posterior probability of getting the set of test results R if the candidate diagnosis D is the set of faulty components. This is calculated from the degree of utilization factors in the functional test model. If more than one failing test is involved, the relevant factors are multiplied together. That is, p(R|D)=p(R1 |D)*p(R2 |D) . . . .
(operation violation penalty) is a number between zero and one, used to reduce the weight of an operation that fails in one test, causing the entire test to fail, yet passes in another test. If an operation penalty is appropriate, the value may be set to a system constant used for all operation penalties or variable penalties may be set for each operation. If no operation penalty is appropriate, the operation penalty is set to one (no penalty).
The method for assigning weights described above assumes that component failures are independent, assumes that test results are independent, and assumes that the degree of utilization of a component by a particular test is proportional to the probability of the test failing given that the component is faulty. Even if the assumptions are incorrect, the resulting computed rank order of relative probabilities may still be satisfactorily accurate for some applications. For further explanation of why the embodiment described above may provide satisfactory diagnoses even when the independence assumptions are not true, see Russek, E. "The Effect of Assuming Independence in Applying Bayes' Theorem to Risk Estimation and Classification in Diagnosis", Computers and Biomedical Research 16, 537-552 (1983).
In a second method for assigning weights, useful information can still be derived without making the assumption that the probability of a test failing given failure of a particular component is proportional to the utiliztion of that component by that test. In the second method, weights are computed as bounds on probabilites. A weight W is computed as follows:
W(D,R)=p(D)*minimum(α1, α2, . . . αN)*(operation violation penalty) (Equation 2)
where αi =(one minus utilization of Cj by test i, where test i is a passing test, and Cj is a member of D) or αi =1.0 when test i fails.
The "minimum" function in equation 2 results from the fact that an upper bound of a logical AND of a set of probabilities is the minimum of the probabilities, or more generally, their least upper bound. The probability of the event of all the passing tests given a failure of a component in diagnosis D is therefore bounded by the minimum of the probabilities of individual tests passing (or the minimum of the upper bounds on those probabilities). An additional refinement is made when subcomponents are present. For example, assume that a component C consists of subcomponents A and B. If either A or B fails, then C fails. In estimating a weight for C, weights WA for subcomponent A and WB for subcomponent B are computed. A lower bound of the logical OR is the greatest lower bound of this set of weights. That is, the probability that either subcomponent A or subcomponent B or both fail is bounded by the maximum of the lower bounds on the probabilities of failure of the individual subcomponents. Accordingly, the appropriate weight for C is then the maximum of WA and WB.
In the diagnostic system disclosed by Preist et al and briefly described above, problems may occur in either the test suite or the model. For example, in the suite of functional tests, some components may not be exercised by any of the tests or may be only partially exercised. If no functional tests fail as a result of component failure or partial component failure, then the component failure will be undetectable. In addition, the diagnostic system may not be able to distinguish one component within a set of components or components may be inadequately distinguishable. Finally, inaccurate modeling data (for example, an incorrect estimate of the degree to which a component is exercised) may result in an incorrect diagnosis.
There is a need for further enhancement of the diagnostic system disclosed by Preist et al by providing automated analysis of the effectiveness of the test suite (ability of the model to detect and differentiate among potential faults), identification of possible test suite changes, and identification of possible modeling errors via automated analysis of incorrect diagnoses. The results of the analysis may then be used by test programmers to improve the particular application of the diagnostic system. In addition, there is a need for further enhancement of the performance of the diagnostic system disclosed by Preist et al by utilizing historical TFC data when such data are available.
An automated analysis and troubleshooting system is provided that identifies potential problems with the test suite (ability of the model to detect and discriminate among potential faults), and also identifies probable modeling errors based on incorrect diagnoses. The test suite and modeling problems are made visible to test programmers to aid in improvement of a particular application of the diagnostic system. In the automated test suite analysis, components that have no coverage or inadequate coverage are identified as detectability problems. Once fault detectability has been analyzed, the model is scanned for problems involving an inability to discriminate among potential faults (components exercised by identical operations in all tests). An overall measure of diagnosability (ability to discriminate among potential failures) may be computed. If no historical TFC data are available, a "diagnosability index" may be computed by randomly sampling from the set of possible failure syndromes and observing the frequencies with which ties occur among the weights of the top-ranked candidate diagnoses. If historical test results are available, a diagnosability index may be computed from the frequency with which two candidate diagnoses are assigned identical weights by the model-based diagnostic system over a set of representative failures.
Given one or more incorrect diagnoses, the analysis system identifies possible modeling changes that could alter the diagnoses. First, the analysis system identifies failing tests that have no coverage of any component in the true failure cause. Such cases indicate an error in modeling. In addition, the analysis system identifies ways of changing the model to change the rank order of diagnoses, including coverages that can be changed and identification of operation violations that can be eliminated or deliberately added. These identified changes are not automatically made, but are only identified as possible changes to be examined by a test programmer.
One significant advantage of any model-based test system is that historical TFC data are not needed. However, when historical TFC data becomes available, the model-based system disclosed by Preist et al can be combined in a complementary way with a diagnosis based on historical TFC data, and the combination is more broadly applicable and more accurate than a system based on either technique alone. Historical TFC data are used to provide confidence indicators on model-based diagnoses, to provide alarms when a process goes out of control, and to provide an independent means of diagnosis when sufficient data are available.
FIG. 1 is a block diagram of a test system and the associated environment, including the automated analysis of the invention.
FIG. 2 is a block diagram showing additional detail for test suite analysis shown in FIG. 1.
FIG. 3 is a block diagram showing additional detail for debug shown in FIG. 1.
FIG. 1 illustrates a test system and its associated environment. A test system 100 runs functional tests (test suite 101) on a UUT 102, generating pass/fail information or other test results 104. A model-based diagnostic system 106 (for example, the diagnositic system of Preist et al) provides a diagnosis 110 based on failure information from the test results 104 and a model 108. The model 108 includes information on tests, operations, components tested by operations, and utilization of the tested components by the associated operations. With diagnosis 110 as a guide, the UUT is repaired (box 112). During repair, the TFC 114 may be determined. Additional tests may be conducted during repair and these additional tests may also be modeled by the diagnostic system 106 (line 130).
In the invention, automated test suite analysis (116) analyzes the test suite 101 as described in the model 108 and generates possible improvements 118 to the test suite 101. Also in the invention, if a diagnosis 110 is not consistent with the TFC 114, then automated model debug 120 analyzes the model 108 for possible changes to the model that would result in a correct diagnosis 110. Test suite analysis 116 and model debug analysis 120 can be used with simulated data 124 or with historical data 126 when available. One advantage of the model-based diagnostic system 106 is that no historical data are required. However, when even partial historical data becomes available, the model-based diagnostic system can synergistically take advantage of that data, as indicated by dashed line 128 and discussed further in section IV below.
Note that the test system 100, the model based diagnostic system 106, test suite analysis 116 and model debug 120 are all typically computer or processor based. That is, software is executed by a processor within an automated test system or the test system is controlled by a separate computer. In general, the test suite analysis 116 and model debug 120 can be performed by a processor or computer used for the test system 100 or used for the model based diagnostic system 106. Alternatively, test suite analysis and model debug may be performed offline on a separate system or distributed among several systems. Note also that some of the statistical inferences involved may be determined by fuzzy logic or neural networks so that the word "automated" is not restricted to sequential execution of computer instructions.
FIG. 2 illustrates additional detail for test suite analysis (FIG. 1, 116). As illustrated in FIG. 2, test suite analysis is divided into two parts. In a first part, detectability analysis 200 is performed. A fault is detectable if one or more tests in the test suite fail when a UUT containing that fault is tested. Detectability analysis examines the set of tests modeled (assuming the model to be error free) and provides an analysis of how effective the set of tests will be at fault detection.
The model-based diagnostic system (FIG. 1, 106) is based on the degree to which the various test operations utilize individual components (and their subcomponents). Analysis of these utilizations directly yields information regarding the detectability of possible faults. If a component has no coverage in any test, then faults in that component will be not be detectable. If a component has only low coverage and only in a small number of tests, faults in that component may not be reliably detectable. Similarly, if a component contains multiple subcomponents, adequate coverage of each subcomponent is required if all possible faults are to be detectable. Detectability analysis proceeds in two stages. In the first stage, components or subcomponents with no coverage are flagged and indicated to the user (FIG. 2, block 204). In the example model in the background section, the display module is not exercised by any of the operations and a failure of the display module will not result in failure of any tests. Therefore, faults in the display module are not detectable.
In the second stage of detectability analysis, coverage analysis of the remaining components/subcomponents is performed. During the second stage, the system may be structured to either provide numerical indicators of the degree of fault detectability for particular components, or to simply flag those judged to have inadequate coverage (FIG. 2, block 206). Criteria during the second stage are primarily heuristic. For example, adequate coverage might be defined to consist of one or more tests with coverage judged to be high, two or more tests with coverage judged to be moderate, or five or more tests with coverage judged to be low. These criteria are meant as examples and it is to be understood that a variety of numerical or heuristic criteria may be used instead. In the example system model in the background section, the CPU is exercised by only one operation (access-- memory) and only 20% of the functionality is exercised. Using the above example criteria, and the example system model, since the CPU is partially exercised by only a single operation, the CPU is under-exercised. For components that are under-exercised, a message is displayed or printed for a test programmer, indicating components that need additional testing (FIG. 2, 206).
As illustrated in FIG. 2, box 202, The test suite may also be automatically examined for diagnosability problems. In this application, the term "diagnosability" means the ability to uniquely identify faulty components within a larger set of candidates. A fault may be diagnosed correctly if the fault results in a pattern of test results (a syndrome) which is either uniquely characteristic or highly suggestive of that fault. Conversely, diagnosability problems arise when a fault is associated with a syndrome that results in equal weights being assigned to two or more candidate diagnoses. Diagnosability problems become particularly severe if two components can never be distinguished from each other by the test suite. Detectability is necessary but not sufficient for diagnosability. Analysis of diagnosability, or the ability to discriminate between components, is complicated by the fact that, in many cases, information about the prevalence of various faults is not available. As a result, diagnosability analysis is designed so that it will work in the absence of failure distribution information, but can exploit this information if it is available.
In diagnosability analysis, the model is scanned for components exercised via identical operations in all tests (box 208). Consider two components, C1 and C2. Assume that whenever C1 is exercised by an operation, C2 is also exercised by the same operation. Then, whenever C1 is a candidate diagnosis, C2 will be as well. Recall also that in the terminology of Preist et al, an operation is "violated" if it fails in one test, causing the entire test to fail, yet passes in another test. Operation violation penalties may be assessed on candidate diagnoses involved in operation violations. If C1 and C2 are found to be exercised by identical operations in all tests, the operation violation penalty term will always be identical in computing weights for these two diagnoses. Hence they can be distinguished only by their prior failure probabilities. If the prior failure probabilities are identical, the system will never be able to break ties between these diagnoses.
When modeling is done at the level of subcomponents, the above analysis becomes slightly more complicated. In this case, two components will always be indistinguishable (aside from prior failure probabilities) if their sub-components can be matched in such a way that each subcomponent is used by operations identical to those of its matched partner in every test. While the problem of matching sub-components is combinatorially explosive, the number of subcomponents is typically small, and either direct search or hashing methods suffice.
In cases where the number of subcomponents becomes too large for such methods to be practical, approximate analysis may be used. In the simplest form of approximate analysis, all the subcomponent operations (and associated coverages) are simply propagated to the component level and the analysis is performed at that level. For example, propagation to the component level can be done either by recording the presence (or absence) of utilization or by recording the maximum utilization of any subcomponent of each component. In either case, comparison may be performed at the component level. It is also possible to compare components with differing numbers of subcomponents by partial propagation. For example, suppose we wish to compare components A and B, and A has more subcomponents than B. By aggregating some of A's subcomponents into pseudo-subcomponents, it may be possible to find a grouping that matches the coverages of B in all operations. This grouping should be reported as a potential diagnosability problem. However, components that appear identical by these methods may still be resolved by the test suite, since this approach does not fully reflect operation violation penalties, or the complete computation of the coverage-related weight term. As a result, components judged identical by these approximate means should be reported as having "similar" coverage, rather than as being indistinguishable by any set of test results (box 210).
A "diagnosability index" may be computed as 1.0 minus the frequency with which two candidate diagnoses are assigned identical weights by the model-based diagnostic system over a set of representative failures. Either simulated data (FIG. 1, 124) or historical TFC data (FIG. 1, 126) may be used for such analysis. If failure distribution information is available, the simulated failures may be created accordingly. Even when neither historical data nor distribution information are available, a diagnosability index may be computed by randomly sampling from the set of possible failure syndromes and observing the frequencies with which ties occur among the weights of the top-ranked candidate diagnoses.
Finally, the test suite may be evaluated for overall accuracy by analysis of historical data (FIG. 1, 126). For example, the system could compute the mutual information (or other statistical or information theoretic measures) between the model-based diagnosis and the distribution of TFC's recorded in the database.
Analysis of detectability and diagnosability may be performed before the diagnostic system is actually used. Further model and test suite improvement may be provided after one or more cases are misdiagnosed by the model-based system. That is, the diagnosis (set of components) predicted by the diagnostic system is not the set of components which were actually faulty in the particular case (TFC). For each misdiagnosed case, automatic identification of possible changes requires both the associated test results and the TFC. Historical TFC data may be used, if available.
FIG. 3 illustrates additional detail for debug of the model based on incorrect diagnoses. As illustrated in FIG. 3, model debugging (FIG. 1, 120) is divided into three parts (300, 302 and 304), but additional debug analyses are possible as discussed below.
In FIG. 1, the diagnostic system 106 generates a list of candidate diagnoses, and then weights are computed for each candidate diagnosis. One candidate diagnosis (110) is selected based on its weight. If the TFC 114 is not included among the candidate diagnoses computed by the model-based system 106, or if the TFC 114 is included but does not have the highest weight among the candidate diagnoses, the debug system 120 is used to help determine why. A candidate diagnosis must explain all the failing tests. Therefore, in troubleshooting, the starting point is to determine if any of the failing tests have no coverage of any component in the TFC. If failing tests are found that have no coverage of any component in the TFC, a comment is printed or displayed to inform the user that adding coverage of at least one component in the diagnosis to each such test will remedy the problem. In FIG. 3, section 300 depicts analysis of missing coverages and section 306 depicts display or printing of identified possible missing coverages. Note that this is similar to but not identical to analysis for detectability. In detectability analysis (FIG. 2, 200), the analysis system determines whether components are exercised by any tests. In troubleshooting after an incorrect diagnosis, the debug system (FIGS. 1 and 3, 120) determines whether every failing test exercises at least one component in the TFC.
Note that changes involving the addition of unique coverage to multiple tests will generally be less likely than those involving a small number of corrections. However, it is not unusual to find errors in which the same coverage or operation has inadvertently been omitted from a large number of tests.
The debug system 120 also determines whether the TFC is a candidate diagnosis, but is assigned a weight that is lower than that of some other candidate diagnosis. In this situation, the goal of the debug system 120 is to determine: (a) how to get a particular diagnosis to be ranked in a particular position, or (b) how to raise or lower the weight assigned to a particular diagnosis. Since (a) can always be decomposed as a series of one or more operations of type (b), only (b) will be discussed in detail.
In equation (2) from Preist et al (given in the background section above), the weights assigned to each candidate diagnosis are determined by the product of: (1) a prior failure probability term, (2) an operation violation penalty term (often identically 1.0), and (3) the maximum (over subcomponent-level hitting sets) of the minimum (over tests) of 1.0 minus the coverage in any passing test. First, consider just the term comprising the maximum (over subcomponent-level hitting sets) of the minimum (over tests) of 1.0 minus the coverage in any passing test. For simplicity, assume without loss of generality that only a single hitting set is involved, so the "max" operation may be ignored. Components in the diagnosis under consideration will frequently show utilization by a large number of passing tests with varying degrees of coverage. Since the weight is proportional to the maximum coverage in any passing test, the number of coverages which actually affect the final weight is typically much smaller than the number of passing tests. For example, suppose u1 is used with coverage 0.1 in test {t1}, with coverage 0.5 in tests {t2, t3, t4, t5}, and with coverage 0.9 in test {t6}. Changing the coverage of u1 in any of t1through t5 will not alter the weight assigned to diagnosis {u1} unless at least one of the coverages is made larger than 0.9. On the other hand, even a small change in the coverage in t6 directly alters the assigned weight. This phenomenon is typical of the embodiment of the diagnostic system using equation (2), and enables a direct search for a small number of utilization parameters which, if changed, would alter the relative ranlking of the weights assigned various diagnoses. Referring back to FIG. 3, the search for utilization parameters that would alter the relative ranking is depicted as the second part of debug (utilization sensitivity analysis 302) and the identified possible utilization changes are printed or displayed (box 308).
This approach is a special application of the classical method of sensitivity analysis. Sensitivity analysis attempts to determine the effect on an output variable (in this case the weight) of an infinitesimal change in any of the parameters. In typical model-based systems, sensitivity analysis is of limited utility due to the fact that varying sensitivities to a large number of parameters are frequently encountered. In the embodiment of the diagnostic system using equation (2), however, sensitivity is zero except for those coverages identical to the maximum coverage in any passing test of some component contained in the diagnosis. When multiple hitting sets are involved, taking the maximum over subcomponent-level hitting sets further reduces the number of parameters affecting the final weight. Hence it is frequently possible to identify a very small number of parameters which, if altered, will modify the weight assigned to a diagnosis. Note that this approach is applicable whenever the underlying formulation confers sensitivity to a small subset of the much larger number of parameters comprising the model. For example, many fuzzy logic systems fall into this category. The approach is thus not limited to the particular embodiment described by Preist et al.
The debug system 120 also identifies ways to raise or lower weights by modifying operation violation penalties (FIG. 3, 304). The weight of a diagnosis can be raised by avoiding operation violation penalties or the weight of a diagnosis can be lowered by generating operation violation penalties. Operation violation penalties may be avoided (to raise the weight of the TFC candidate diagnosis) by removing a particular violated operation from one or more tests so that it is not used in both passing and failing test(s), or by adding coverage of the faulty components in at least one failing test via addition of operations which are not themselves violated. Conversely, penalties may be imposed (to lower the weight of an incorrect candidate diagnosis) by ensuring that a violation is present (i.e. that a particular operation is used in both passing and failing tests), and that the violated operation provides the only utiliztion in the set of failing tests of some component in the diagnosis. The diagnostic system maintains lists of violated operations, of passing and failing tests, and of candidate diagnoses and their associated weights and penalties. As a result, the above search is feasible although combinatorially intensive. In practical embodiments, it may be appropriate to perform a limited rather than an exhaustive search, thus detecting automatically only a subset of all possible alterations to operation violation penalties.
Possible changes to prior failure probabilities should also be identified for consideration by a test programmer. Prior failure probabilities are generally based on historical failure rates or subjective probabilities. As a result, altering prior failure probabilities to correct a single diagnosis is rarely appropriate (and is not depicted in FIG. 3). Nonetheless, the debug system can suggest this possibility when the alternative is modification of a large number of coverages. In general, for all model changes suggested by the automated system, test programmers should review the actual tests to determine what model changes are appropriate before accepting the automatically generated suggestions.
Certain diagnoses may not be reported because they are not minimal (at the component level) or are judged too complicated. For example, if the diagnosis {u1} is sufficient to explain all the test failures, {u1+u2} will not be reported, since it is a super-set and hence non-minimal. Similarly, if there are N-component diagnoses which explain the test failures, one may choose not to report any diagnoses involving N+k or more components, where k is a non-negative integer (typically 2). Either of these reasons for not listing a particular diagnosis is easily detected automatically (by checking for super-sets of candidate diagnoses, and by comparing cardinality with that of the smallest candidate diagnosis, respectively).
Historical data may be used in conjunction with the model-based diagnostic system as follows:
(a) Providing cases for performance evaluation of the model-based system.
(b) Replaying historical syndromes to choose one or more on which to debug the model.
(c) Providing cases for automatic (or manual) debugging.
(d) Allowing evaluation of the effect of changes suggested by debugging on overall performance.
(e) Providing a source of cases for test coverage analysis.
(f) Determining if suggested changes are validated by historical data.
Additionally, because model-based diagnosis and diagnosis based on history of past TFC's are largely complementary, a system which utilizes both techniques simultaneously is more broadly applicable, more accurate, and more able to provide a confidence indicator on each diagnosis than a system based on either technique alone. For example, one of the strengths of model-based diagnosis is that the model can be constructed using data available at design time. As a result, diagnoses are available from the first failure, long before sufficient historical TFC data can be accumulated. Additionally, even after historical data collection is well under way, the model-based system provides the preferred way for diagnosis of rare syndromes which are absent from the historical database or present in such low numbers that the TFC statistics are not considered reliable. Conversely, diagnosis based on the frequency with which various TFC's have been seen in association with a particular syndrome requires accumulation of statistically significant data on a particular syndrome before a reliable diagnosis for that syndrome can be made. Once adequate data are available, however, the historical approach is typically preferable for stationary processes. This approach most accurately reflects the repair actions which fixed defects associated with the syndrome in question previously. Additionally, historical diagnosis frequently allows an objective confidence indicator to be placed on the weights assigned to candidate diagnoses. For example, assume 10,000 instances of the syndrome in question have been seen; in 6,400 cases the TFC was u1, and in 3,600 cases u2. Assuming components fail independently according to a stationary Poisson process, the standard deviation may be estimated by the square root of the number of observations. Hence, we could assign weights 64±8 to u1 and 36±6 to u2. Similar results are available under alternative distributional assumptions. Additionally, if historical data are collected in real time as is frequently done in statistical quality control, alarms may be raised when anomalous failure rates are seen (i. e. if the process goes out of control or becomes non-stationary).
While historical and model-based diagnosis each have several factors in their favor, a combined system has virtually all of the above advantages with none of the drawbacks. Additionally, in a tightly-integrated system, having two independent means of diagnosis provides an additional confidence indicator. When history-based and model-based diagnosis agree on the most likely failure cause, for example, confidence that this is likely to be the correct diagnosis is higher than it would be with either system alone. When historical data are not available for a particular syndrome, the model-based diagnosis can be relied on. Finally, when historical and model-based diagnosis are both available but disagree, the historical diagnosis will generally be more reliable if the statistics are solid and the process is still in control.
An additional advantage arises when users may wish to construct a model-based system for a UUT for which historical data are already available. For example, users may wish to add model-based diagnostics to a line which is already in production. In this case, the historical database may be used to guide or check construction of the model. For example, suppose one extracted from the database all single-component TFC's and arranged them by failing component. Consider, for example, the syndromes associate with TFC component C1. Tests which fail in any of these syndromes must each have some coverage of C1. Similarly, tests which always pass when C1 is the TFC are unlikely to have high coverage of C1 (although it is not prohibited). Changes suggested by automatic debugging can be analyzed against historical data using heuristics of this type.
The foregoing description of the present invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed, and other modifications and variations may be possible in light of the above teachings. The embodiment was chosen and described in order to best explain the principles of the invention and its practical application to thereby enable others skilled in the art to best utilize the invention in various embodiments and various modifications as are suited to the particular use contemplated. It is intended that the appended claims be construed to include other alternative embodiments of the invention except insofar as limited by the prior art.
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MITCHELL, S.D. – Elizabeth Lamb doesn't put up flashy numbers every game, but she's not someone opponents want to overlook.
Lamb, a senior forward for the Dakota Wesleyan University women's basketball team, hasn't led the team in scoring once, but that doesn't mean her presence isn't felt. Last week, Lamb averaged 14 points and 3.5 steals per game to help the Tigers go 2-0 on the week and improve to 10-4 overall. For her performance, Lamb has been named the Great Plains Athletic Conference Women's Basketball Player-of-the-Week, it was announced Monday by the league.
Saturday Lamb, an Onida, S.D., native, scored 18 points to help Dakota Wesleyan beat Doane College 81-69. She hit four 3-pointers, including back-to-back threes early in the game to help keep DWU ahead of Doane. She also hit a 3-pointer late in the game to push DWU's lead to double digits and help the Tigers take control of the game for good. Lamb went 5-for-8 from the field – including 4-for-5 from long range – and 4-for-4 from the line. She also had three rebounds and four steals.
Lamb started the week with a 10-point performance against Mount Marty College Wednesday in Yankton. She went 4-for-10 from the field and hit two 3-pointers to help the Tigers beat Mount Marty on its home court. She also had three steals and two rebounds.
Lamb is averaging nine points and 3.7 rebounds per game through 14 games played this season. She has 36 assists and 19 total steals. She is shooting .386 from three-point range, and is part of the reason Dakota Wesleyan leads the nation in three-point percentage as a team (.386).
Dakota Wesleyan – which is 4-3 in the GPAC – is on the road Thursday and Friday for a pair of nonconference games in the Wisconsin Dells. The Tigers play Ashford University at 7:30 p.m. Thursday, Dec. 20, and Lakeland College at 5 p.m. on Dec. 21.

subset: commoncrawl


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Zmodo 4CH H.264 CCTV Real-time Surveillance Security DVR - iPhone & Android Network - 500GB HD - DD-SBN4-500GB
|The ZMD-DD-SBN4-500GB is a 4 channel, fully integrated, real time hardware-based digital video recorder. This standalone DVR is based on an embedded Linux operating system and includes a remote control for easy setup and operation. With the Pan/Tilt/Zoom (PTZ) camera control, network access and USB2.0 backup, this DVR is perfect for the home or small business.


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FOX News Host Calls Robin Williams a Coward Over Tragic Suicide (Video)
John Prager
Americans Against the Tea Party
August 12, 2014
FOX News host Shepard Smith didnt even wait until Robin Williams body was cold to begin insulting the legendary actor. At the end of a report on Williams death, Smith began to seethe at the thought of what Williams did to his children in ending his own life.
Smith recalled that Williams once told a story about his daughter Zelda, who asked him to read to her in his normal voice. One of the children he so loved, one of the children grieving tonight, Smith said. Because their father killed himself in a fit of depression.
Its hard to imagine, isnt it? he added. You could love three little things so much, watch them grow, theyre in their mid-20s, and theyre inspiring you, and exciting you, and they fill you up with the kind of joy you could never have known. And yet, something inside you is so horrible or youre such a coward or whatever the reason that you decide that you have to end it. Robin Williams, at 63, did that today.
Smith has since apologized for his lack of compassion for depressed individuals. Smith told Mediaite that he didnt actually mean to call Williams a coward. It just came out of his mouth:
"I spent an entire hour talking about how much this man affected peoples lives and brought greatness to this world. I was just wondering aloud what could have made this man want to end it all. And it reminds us that we all have responsibility as friends and neighbors to help take responsibility to prevent this from happening. There are people who process suicide as a black-and white-issue. I dont process anything as black-and-white.
I was sorry to read online the people who see the world in a black-and-white way, and to suggest that they might have a definitive analysis of why he did such a thing. What I wanted to do is celebrate his life and find some meaning in our own lives, since we often do know someone who is reaching out, and we have a responsibility to help that person maybe a phone call or a door knock is in order.
But no matter how you process it: Look at what this family is going through. I would never presume to know anything about his private life. And if any of his family members and friends were to have seen me use the word coward, I would be horrified. I would just to apologize to the end of the earth to anyone who might think that I meant to openly call him a coward.
To the core of my being, I regret it. It just came out of my mouth. And Im so sorry. And to anyone and their families who see that, I am sorry."


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The book Colleges That Change Lives notes that Willamette students are "zealous about sports" — one in four compete in Division III athletics, and a majority participate in intramurals each year.
Willamette students love to stay active, whether they're cycling through the surrounding Willamette Valley, tackling on the rugby pitch, backpacking in a nearby forest or joining an ultimate Frisbee tournament.
Our wide variety of intramurals and club sports gives you plenty of opportunities to show your competitive side. And our popular Outdoor Program is your entry point to the Pacific Northwest's natural playground — with numerous organized trips every year for skiing, camping, white-water rafting, hiking or snowboarding.
By the numbers
Club sports: 24
Intramurals: 15
Intramural athletes annually: 65%


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Wednesday, February 11, 2004
© Copyright 2015 Clayton News Daily
By Jeffery ArmstrongFAYETTEVILLE n The boys Region 4-AAAAA Basketball Tournament began with a bang Tuesday night at Starr's Mill High as Lovejoy and Riverdale earned state-tournament berths with victories. Lovejoy's Derek Brooks continued on his torrid scoring pace, connecting for 28 points and also adding five assists and three steals, as the No. 1 seeded Wildcats rolled to an 84-58 thrashing over Newnan at 8:30 p.m. Brooks scored 30 points against the same Newnan team last Friday night as Lovejoy now advances to play Fayette County in Thursday's semifinals. "I had good looks tonight; Newnan played me honestly and I took advantage," said Brooks, who was 11 of 15 from the field. No. 15 Lovejoy (20-6) had complete control of the game from the start, racing to a 23-10 lead after the first quarter and were never challenged after that. The Wildcats got complete efforts from their players, even though not every one scored in double figures. They rebounded well, limiting Newnan to one shot and scored several points on the fast break. Lovejoy center Joseph Ayers played big on defense and point guard Faderain Thompson also hit the boards. Ayers scored eight points and also had 12 rebounds, four blocks and five assists while Thompson chipped in nine points and eight rebounds as he controlled the game's tempo. n Riverdale 69, Starr's Mill 61: After a two-year absence, the Riverdale Raiders have made it back to the state playoffs. Sean Hightower's hot start propelled the Raiders to a 69-61 victory over the host team, the Starr's Mill Panthers, to advance in the tourney and clinch state in David Boyd's first season as head coach. "Any win is a good win because you never know when you'll get the next one," Boyd said. "This was a good win." Riverdale (17-9) advances to play Carver-Columbus in Thursday's 5:30 p.m. boys semifinal. Carver beat Mundy's Mill 76-61. Hightower scored 28 points, including 6 of 10 from 3-point range. In fact, he sank his first four treys in the opening quarter to help Riverdale, the No. 3 seed, take a 23-15 lead. Starr's Mill hung in with Riverdale thanks to hot 3-point shooting as well, with point guard Andy Drum sinking 3 of 6 treys. The game was deadlocked at 32 at the half. A Demarius Patrick layup gave Riverdale a 53-46 lead with 6:16 left in regulation, but Starr's Mill fought back to get within three at 59-56 with 1:43 left. But Hightower sank a jumper and Riverdale sank six more free throws to ice the game. Patrick and Gani Lawal added 14 each for Riverdale. Carver-Columbus 76, Mundy's Mill 61: The No. 2 seed Carver Tigers probably took Mundy's Mill's Tigers a little lightly since they whipped Mundy's Mill by 35 last Friday. Mundy's Mill, the No. 7 seed, showed tremendous toughness on offense and defense, getting 21 points from Corey Kimbro (including four straight treys in the fourth quarter) to put a scare into Carver Tuesday night. Mundy's Mill cut a 17-point fourth-quarter deficit to six as Chad Dodd's trey made the score 61-55 with 2:40 left in regulation. During the comeback, Mundy's Mill got in foul trouble and Carver sank their free throws late to pull away. Carver center Rickie Darden led all scorers with 29 points.
More like this story
- Lovejoy, Riverdale earn state berths ( February 12, 2004 )
- Tradition continues in Clayton ( February 18, 2004 )
- Lady Tigers clinch state playoff berth ( February 8, 2005 )
- Mundy's Mill slows the pace for region win at Lovejoy ( February 10, 2010 )
- Lovejoy picked to win Region 4-AAAAA ( July 23, 2005 )


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I'd say the Gazette is going rather light-handed on the municipal employees taking freely from the village coffers. The $39,975 loan by Ms. Kiel, at $100 per week, will take just over 7 and 1/2 years to repay. The $11,600 loan by Ms Langevin, at $50 per week, will take about 4 and 1/2 years to repay. In the meantime, what if these employees cease to be employed by Vicksburg, or they move, or pass away? More important is why, in the first place, should public money be available for personal borrowing - unless it is part of a specific loan program (such as a sanctioned business development loan) or formally agreed upon to supplement one's job duties (like borrowing to purchase bullet proof vests)? The two loans referenced in the Gazette article are rather substantial. One wonders how many others have borrowed this kind of money in the past.
This is a story that Paul Harvey would have loved to report.
A.A.R.P. ought to be participating in this protest, too, opposing the new state taxes on its constituents.
To concur with the questions about why a FOIA hasn't been filed by the Gazette (or what's left of the newspaper), I'll note that it has been standard practice in years gone by to slap FOIA requests on the city of Kalamazoo for in-depth coverage and also report the FOIA filing in the news articles. Maybe WKZO can move this story along and identify who has been doing their banking off the backs of the Vicksburg tax payers.
Instead of getting in the middle of this argument, the instructor should have handed each student a handgun and told them to take their differences out back, behind the building. Isn't this exactly the case that justifies allowing students to bring guns to school?
Grammar: paragraph 4, "remain" should be "remains"
Early on, Kalamazoo College officials said that they need to light the fields at night so they can rent them out to outside organizations. They need the rent revenue to help pay for a complex that they cannot otherwise afford to finance by themselves.
It would have been nice to have Nieuwenhuis' and Rogowski's "yes" vote, but the proponents needed one more commissioner to side with them. A tie vote would have meant the motion fails. Even if Nasim Ansari had shown up for the meeting, the request faced a lot of opposition. In the end, the well-meaning public got "politicked."
Bingo - Misterdebate.
And there you have it. The agenda of the House Energy and Commerce Committee by its very own Chair: keep the United States held hostage to foreign oil.
Yup, keep driving those giant gas hogs and let's worry about it later.
Seems strange to receive mail (postcard) from a shelter station high in the mountain. Is there actually mail service along these trails?
Please reread and edit the second-to-last paragraph.
So what makes this story so sensational that the rest of the country (or NBC News) would find it interesting?
Gesmundo sold the land to himself (WMU Foundation chief -> AVB chief). You gotta wonder what kind of a deal he swung for himself.
Hey, here's a novel idea for that site downtown:
Big oil companies: don't worry, you're safe now. Your boy is on board to protect you.
Why won't WMU announce where this thing is going to be built? Why the big secret?
More high priority spending by the city, I see.
After begging and borrowing from the state (taxpayers) for money to pay for projects like Sangren Hall, where on earth does this university come up with this kind of spare change? Here we'd been told all along that they are cash strapped.
According to the Gazette on 7/6, the county will have $23.6 million at year end to pay for construction, architects, and contingency fees for a new jail.
Let's see: $23,600,000 divided by $20 per day = 1,180,000 days in the St. Joseph county jail. That gives you just shy of 3,233 years for a prisoner.
Does it really make sense to increase the size of Kalamazoo's jail and add the required staff to run the place?
More unnecessary visual distractions aimed at diverting the drivers' eyes from an already busy roadway. One news report very recently told of a community in western Michigan that finally got the clue that this type of advertising creates traffic hazards and is working to curtail this. Too bad Kalamazoo and Portage can't do the same.
You want republican support for a ballot question? Tell them the tax is for a multi-purpose ARENA that will be used at night for a tax supported homeless shelter. OR tell them this is for a jail expansion that will serve the homeless. Remember, the political trend for both parties in Kalamazoo is always based on a double standard. You've got to cater to that concept to get ahead here.
How accurate can the results of a study be when the researchers are looking for potential problems and the subjects are all made aware that they are under the microscope during the examination period? It's too bad that whatever information they now need isn't available from prior violations.


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Liverpool Football Club have released the following late availability notice for the Barclays Premier League clash with Manchester United to be played at Old Trafford on Sunday, March 16, with kick off at 1.30pm GMT.
A limited number of tickets will be available to season ticket holders, members and fan card holders who recorded nine or more Premier League away games during the 2012-13 season.
Tickets will be available from 11am on Wednesday, March 12. Tickets can be purchased online, on telephone number 0843 170 5555 (overseas +44 151 907 9399) or in person at the ticket office.
Tickets in this sale are subject to availability.
Disabled tickets have sold out.
Tickets are non-transferrable and must not be passed on to anyone else.
Random security checks may be carried out on the turnstiles to check your tickets.
If the ticket has been purchased by a different person, the holder of the ticket and the purchaser may be subject to further investigation by the club that could ultimately result in the club pursuing a ban for each person involved from purchasing tickets for future LFC games.
Each case will be viewed on its individual merits.
Tagged: manchester united , tickets , united
Submit your views in the box below. Alternatively, you can debate these issues with fellow fans on our Message Boards or blog about them on The Kop
The views expressed here are not those of Liverpool Football Club (read more)


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The top 5 ACC storylines for Week 2:
1. "New" Clemson. A No. 4 ranking, back-to-back wins over good SEC teams, winning a game in which the stats titled the other way … Heady stuff for the Tigers. But inspiring depth turned up all night and was revealed on Tajh Boyd's 31-yard touchdown pass to Zac Brooks that gave the Tigers a 28-21 lead (they never trailed again). Boyd took the snap from backup center Reid Webster. It was the only catch for Brooks all night, and only the fourth of his career.
2. Gators-Hurricanes. Miami coach Al Golden knows Saturday's home game against Florida – the first in the series since 2008 – is a biggie. The schools recruit many of the same players. "I think winning would go a long way for sure," Golden said this week.
3. QB issues at N.C. State. The Wolfpack opened the Dave Doeren era with a 40-14 win over Louisiana Tech but quarterback Brandon Mitchell suffered a foot injury that will require surgery and is likely out four to six weeks. Pete Thomas, a less mobile transfer from Colorado State, takes over as the starter. Look for the Wolfpack to get creative with play-calling and try some Wildcat looks. Clemson plays at N.C. State on Thursday night, Sept. 19.
4. Jameis Winston. Florida State's redshirt freshman quarterback looked like Matthew Stafford with speed Monday night, completing 25 of 27 passes for 356 yards and four touchdowns in a 41-13 victory at Pittsburgh. The big arm on display in spring practice looks more than legit. Moreover, Winston ran the offense with veteran poise.
5. Better than 35-10? Alabama pummeled Virginia Tech, but scored touchdowns on punt, kickoff and interception returns. The Tide gained only 212 yards and was held to 2.5 yards per rush. "One of our better defensive games around here," Hokies head coach Frank Beamer said.
Follow Gene Sapakoff on Twitter @sapakoff
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Tonight I received several text messages from friends in Boston near the Convention and Exhibition Center. Mitt Romney rented the place and was throwing a black-tie party to watch the election outcome on Fox "News".
Apparently, as the evening wore on, the oligarchy came tottering out a few at a time, somber and quiet. Every last one was wearing a frownie-face.
I only wish I'd been there to see reality bite down hard on the 1%.
Views: 18


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Candice Swanepoel Returns for The Upside Fall/Winter 2014 Campaign
Candice Swanepoel is the face of The Upside Fall/Winter 2014 campaign. The new campaign was filmed in New Jersey. Candice wears the Hydrangea prana pant in the above photo with a tank top. Candice also models a new Mod Leopard collection from the Australian brand founded by designer Jodhi Meares. Take a look:
Candice was also the face of The Upside's Spring-Summer 2013 campaign.
Photos: The Upside
Posted on May 26, 2014
More from Shopping Blog


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SAN ANTONIO - A San Antonio woman is the hospital with a broken leg and damaged pelvis, after she was hit by an SUV on Thursday.
Sheila Vasquez, 28, was riding her scooter home after work and crossing Josephine Street when she said a red SUV came speeding toward her.
"I could hear everything when it hit me," said Vasquez. "I heard my bones. I was just spinning."
Vasquez said she yelled, but the driver of the SUV just took off.
According to some witnesses, he continued to drive recklessly, running over a stop sign and eventually, wrecking his own vehicle.
Although that driver, Aaron Rosillo, 21, was arrested, Vasquez said she can't believe that he not only put her life in danger, but also the lives of a one-year-old child, a teenager, and the driver's girlfriend, who were all in the SUV with him at the time.
"Keep your eyes open because that 7 seconds it takes to get in front of somebody, you want to be early or you want to hurry up and get home, that can save somebody's life if you just drive carefully," she said.
Vasquez was in the hospital Friday with a broken leg, pelvic damage and injuries to her hands.
She said she's just thankful to everyone who helped her out.
"I'm just so thankful and blessed that I had all those people come out of nowhere," she said. "They gave me their baby blanket. One man quickly got out his cell phone and said, "Who can I call for you?" and another lady just sat there and held my hand."
Vasquez had to undergo surgery on Friday and will remain hospitalized at least for the next several days.
Rosillo is in custody at the Bexar County Jail, facing charges of failure to stop and render aid and child endangerment.
For more stories from Stephanie Serna, click here
Copyright 2012 by KSAT.com All rights reserved. This material may not be published, broadcast, rewritten or redistributed.


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This stunning video shows Toyota's rotary carbon fibre loom, used to weave the Lexus LFA supercar's A-pillar. This is what goes into making a $US375,000 car, folks. And my god, is it beautiful. [NotCot via Autoblog]
Toyota's Carbon Fibre Loom: A Stunning Piece Of Machinery
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Teen headed to national spelling bee for second shot at title
Though she described herself as "lazy" and possessing poor time management skills, 14-year-old Deanna Hambleton spends eight hours each Saturday and Sunday studying words, and for good reason. She's headed to the Scripps National Spelling Bee on May 28 and 29 for her second chance at the national title.
The Varsity Lakes eighth-grader fell out of last year's competition during the preliminary stage, and has since lived by a word very unlike those she uses in her self-description: focused.
Right after school and before her nightly spelling study session, the Daily News caught up with Deanna and discussed how she manages to balance school, a social life and spelling bees.
Daily News: How have you been preparing for the Scripps National Spelling Bee?
D: I've been studying Greek and Latin roots, because they will help me with any words that I don't know, if they pop up on me. I've also been studying a lot of words in general.
DN: How much time each week would you say you spend studying for the competition?
D: I usually spend about two to three hours on the weekdays, eight (hours per day) on the weekends.
DN: That seems like a pretty big commitment on top of going to school and making time for your hobbies and hanging out with your friends. How do you balance all of it?
D: I usually do the things I like at night. They're kind of like a reward for the whole day of studying.
DN: What kinds of things do you like to do in your spare time?
D: I like to surf the Web a lot. I have a computer addiction — that's what my mom says ... I have spent eight hours on the computer before. My dad at one point restricted the use of the computer on weekdays — no computer on weekdays at all.
DN: Last year you said that you were really nervous going into the national competition. How do you plan on keeping your nerves at bay?
D: I'll probably try to remember that this isn't the whole world. Whether I win or lose, it's not going to matter that much.
DN: What did you do after you lost last year's national competition?
D: I cried.
DN: How do you think you'll react this year then if you win?
D: I'll probably cry of happiness for all I know. (laughs)
DN: Will you be doing anything differently this year to prepare for the spelling bee?
D: I'm actually going to be studying for once. Last year, I didn't do anything really. Two months (the time she had to study last year) was kind of a short time frame, but I practically studied the whole year this time.
DN: What's the best advice you've been given to help you with the spelling bee?
D: "Stay focused" and "don't be too nervous when you get up there."
DN: Your mom said last year that luck was a big part of the competition. Do you think that luck has been a factor in your success?
D: I believe so. In the county bee, the last few words were words I didn't know, and I was lucky that I got the ones I did.
TV Schedule of the Scripps National Spelling Bee
Quarterfinals: ESPN360.com from 2 p.m. to 5:30 p.m. on May 29th
Semifinals: Live on ESPN from 11 a.m. to 2 p.m. on May 30th
Championship Finals: Live on ABC from 8 p.m. to 10 p.m. on May 30th
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Amazing History of the World in 2Mins Video
January 18th 2013 21:36
I actually have to thank my mum for emailing me this forward she received - what a very cool and powerful little 2min compilation of the world - although I think they went to a few too many liberties with asteroids and, seriously, a glimpse of Tom Hanks?? - but besides this it is very cool and I do pray that Aliens do not watch it on their approach to planet earth or they may just wipe us out for being so foolish.
So enjoy and share it if you like it
So enjoy and share it if you like it
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Advising Proposal
Southern Polytechnic State University
Academic Affairs
Office of Faculty Support and Development
Faculty Growth and Development Unit
Student Success through Advising for Retention, Progress and Graduation (SSARPG)
Goal: To develop and deliver a program to SPSU faculty (full time and part time) to increase their knowledge and skills in academic advising and to provide easily accessible advising support materials and resources for their use.
Student success is contingent upon retention, progression and graduation. Faculty advising is an ongoing critical component of the process. This proposal is based on the directives of P&P 308.0 - Academic Advising – which includes the following: "The advising activity is viewed as a normal part of the faculty or staff member's work load…".
Advising can be divided into two basic components: core advising which may be delivered via faculty and staff and mentoring/coaching advising that best resides with faculty advisors. P&P 308.0 also lists advising goals for SPSU. As a result of information included in P&P 308.0 and a vision to provide comprehensive academic advising to the diverse student population we serve, faculty expectations for advising include:
- Advising students on their course of study, including major and course selection,
- Serving as life coaches to students as they move throughout their academic careers,
- Guiding students in professional networking (including professional associations, etc.)
- Supporting students through transitional periods, including moving from undergraduate study to career positions and/or graduate study.
- Providing career advice, direction, and industry contacts.
- Mentoring students as they make decisions on career options, course delivery options, etc.
Academic advising is more than signing off on a class schedule each semester or providing core and major advising. The time requirement may be more than a few minutes once a semester. Successful advising may involve partnerships with staff advisors, student service professionals, peer advisors/tutors, and part time faculty. Schools and departments within schools may have different modes of advising that need to be fitted into any advising plans.
As noted in the MIT/Stanford Tomorrow's Professor Blog (http://tomprofblog.mit.edu/2007/06/12/806-developing-faculty-for-new-roles-and-changing...) SPSU must…"lay the groundwork for success by paying attention to the structure for academic advising and rewarding faculty and professional staff who provide effective advising."
SSARPG seeks to:
- establish a standing committee to focus on academic advising and its role in retention, progression and graduation at SPSU,
- build upon the work done previously by the SPSU Advising Task Force,
- expand and improve the existing advising website, and
- incorporate best practices,
This proposal seeks to implement a program that provides training and resources to improve academic advising and, as a result, positively affect student success.
To move forward with the SSARPG plan, the following steps are critical:
- Seek the approval and support of the Dean's Council on a consensus regarding faculty expectations, revisions to this SSARPG plan and implementation of SSARPG. [This approval occurred on February 3, 2010.]
- Seek the endorsement of the Faculty Senate for SSARPG implementation.
SSARPG Phases and Timeline
Phase 1: Convene Faculty Advising Planning Committee (February 2010)
- Develop a draft research plan and investigate resources.
- Convene a SSARPG Faculty Advising Planning Committee of up to 9 members including a faculty representative from each of the SPSU schools and divisions (5 individuals), one Faculty Growth and Development (FGD) representative, one ATTIC representative, and one Student and Enrollment Services (SES) representative. The committee will also seek the advice of staff advisors and others who can contribute to the discussion and planning.
- Secure the support of a consultant familiar with successful academic advising in a top tier institution such as MIT, RIT, etc.
- Planning Committee revises and approves the research plan.
Who: FGD staff, GA, Planning Committee members
Phase 2: Research, Resource Development and Training Plan (February-March 2010)
- Review Advising Task Force results and review existing advising website.
- Implement the literature search.
- Conduct online search with telephone follow-up of institutions with outstanding advising programs
- Review advisor training options and materials.
- Conduct student survey and focus groups to develop baseline data.
- Compile best practices.
- Update and expand advising webpage
- Develop training plan for full time faculty (approximately 6 hours) and part time faculty (approximately 3 hours). Several staff members take on the advising role on campus. Where appropriate, this training and the associated resource materials will also be made available to those individuals identified by the school/division deans.
- Develop training materials and advising resources.
- Identify a cadre of Advising Fellows to deliver training for the initial roll out and for future sessions including new faculty orientations.
- Present results to Dean's Council.
Who: FGD staff, GA, Planning Committee members, consultants as needed
Phase 3: Training Pilots and Training Rollout (April - June 2010)
- Schedule and register for pilot training programs
- Pilot the full time faculty training
- Pilot the part time faculty training
- Evaluate training programs and revise as necessary.
- Schedule and register for full time and part time faculty training programs.
- Offer 10 full time training programs (schedule to accommodate faculty schedules)
- Offer 6 part time training programs
- Evaluate training programs
- Select Teaching Fellow for SSARPG in FY 2011.
Who: FGD staff, GA, Planning Committee members, Advising Fellows, all SPSU faculty
Phase 4: Follow-up and Institutionalization (July 2010 – June 2011)
- Work with Advising Fellows to continue to improve and deliver SSARPG.
- Implement Advising Training in all new faculty orientations (part time and full time)
- Continue to update and improve advising resources and the Advising website
- Collect, measure, analyze, and report results/improvements.
- Inform and advise the Dean's Council semi-annually.
Who: FGD staff, GA, 1 Teaching Fellow (The annual Teaching Fellows format has changed over the years and the cost has ranged between $6,000 and $20,000. For FY 2011 we will be requesting 10 $1,000 stipends. The Teaching Fellow noted above is one of those 10.)
SSARPG Budget
FY 2010 - Phases 1-3
Staffing: 1 GA for spring 2010 - $2,000; consultant(s) - $4,500; $32,650
Advising Fellows stipend - $12,900; Part-time faculty incentives - $11,250*
Materials (paper, DVDs, site license, etc.) purchase $5,500
Printing/copying, binding, etc. $2,250
FY 2011 – Phase 4
Staffing: GA for year - $5,500; Teaching Fellow - $1,000 $6,500
Materials purchase $1,500
Printing/copying, binding, etc. $1,250
* Ideally all full time faculty will be required to participate and part time faculty can elect to participate and receive a $150 stipend. Why do we want to train part time faculty? For many students the only faculty they come into contact with in any given semester are part timers. Having part time faculty trained means they will be aware of advising recommendations and the resources available. Another option would be to require part time faculty to participate and thereby eliminate the $11,250 expense for stipends.


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Maryland may be one of the wealthiest states, but hunger runs deep. It affects old and young alike, but it can be solved by connecting all who are eligible to the federal nutrition programs. We compile and analyze the latest data to show the need and the solution. Our rich library of resources will help your education, advocacy, and outreach efforts as we work together to end hunger and promote well-being.
Maryland Hunger Solutions advocates for policies, programs and resources that fight hunger and improve the well-being of Marylanders. Through our Paper Apple Campaign, we are gathering messages from Marylanders on how to create a healthy and hunger-free state. By participating in the Campaign, you can tell our elected officials that protecting and investing in nutrition programs is essential to creating a healthy, hunger-free Maryland.
We are actively engaged in connecting eligible Marylanders with federal nutrition program benefits and available state and local resources. Whether it's a county fair, senior expo, back-to-school night, or conference, our staff will come to you with information on applying for benefits from programs like food stamps and school meals and accessing healthy, affordable food.
By engaging partners across the state, we are removing barriers to nutrition programs, increasing outreach, and expanding access to affordable and healthy food – all steps that will get us closer to our mutual goal of ending hunger. Maryland Hunger Solutions collaborates with public and private partners through various coalitions, partnerships, and state and local food policy workgroups.
Join Us
in the fight against hunger by signing up for our newsletter, distributing materials, advocating, or hosting a food program
In Focus
Final extension: Deadline to submit MFANN Fighting Hunger in Maryland Conference Proposals is Monday August 3rd
In order to accommodate requests for additional time to submit MFANN conference proposals, we are offering a final extension to our RFP cycle to ensure a robust conference program. All proposals must be submitted by Monday, August 3rd. Fighting Hunger in Maryland: Reframing Hunger is the fifth annual conference presented by the Maryland Food Access and Nutrition Network (MFANN). This year's conference will be held on Tuesday October 27th at the University of Maryland SMC Center (621 West Lombard Street) in Baltimore. Download the conference RFP. For more information or to discuss potential panel topics, contact Rachel Tucker at 410 528 0021 x23.
New Report Finds that Participation in Summer Meals in Maryland Increased in 2014
Maryland made significant strides in its efforts to reach more low-income children with summer meals in 2014, according to Hunger Doesn't Take A Vacation, a report released annually by the Food Research and Action Center (FRAC). 59.705 low-income children were served summer meals on an average day in July 2013, an increase of 17.3 percent from last year. Summer meals reached 21.6 students for every 100 low-income children who got regular school year school meals in the 2013-2014 school year, outpacing that national rate of 16.2:100. More...
Governor Hogan Signs New Law to Reduce Child Hunger
We are thrilled to announce that Governor Hogan signed The Hunger-Free Schools Act of 2015 into law today. The Hunger-Free Schools Act of 2015 increases access to the Community Eligibility Provision – federal funding that allows high-poverty schools to provide breakfast and lunch free to all students. Community eligibility increases access to healthy school meals, improves learning, and reduces food insecurity. More...
Survey Finds 14 Percent of Households in Maryland Struggle to Afford Food
One in seven people – 14 percent of respondents – in Maryland reported in 2014 they struggled to afford enough food for their households, according to a new report released by the Food Research and Action Center (FRAC). More...
Baltimore Mayor Stephanie Rawlings-Blake on Hear the Maryland Crunch!
Video: U.S. Congressman Chris Van Hollen on Hear the Maryland Crunch!
Tell Congress to Keep SNAP Strong
Add your name to FRAC's petition and let Congress know that you support SNAP. SNAP has extraordinary strengths. It reduces hunger and food insecurity, lifts people out of poverty, improves nutrition and health, boosts employment, responds quickly in times of disaster or recession, supports local economies, and improves well-being.
Celebrate Maryland Day, March 25, with Maryland Hunger Solutions and thousands of other Marylanders by crunching into a juicy apple!
Hear the Maryland Crunch is a fun state-wide, synchronized apple crunch event that aims to reduce child hunger by increasing access to School Breakfast Programs. Children who participate in School Breakfast programs are healthier and less likely to be late to or absent from school. Schools can increase access to breakfast by adopting successful models such as Breakfast in the Classroom and the Community Eligibility Provision. A new tool that allows high-poverty schools to serve all students breakfast and lunch at no charge, the Community Eligibility Provision creates Hunger-Free Schools and improves learning. See Maryland's eligible schools.
To participate in Hear the Maryland Crunch, crunch into an apple and share a photo of your best crunch face on Facebook. Vote for your favorite photos to win one of many exciting prizes!
NBA Team Honors Michael J. Wilson and MDHS
The Washington Wizards honored Michael J. Wilson and MDHS at their February 9 home game against the Orlando Magic. See Michael J. Wilson's interview on #WizMagic American Express Homecourt Advantage (interview starts at 3:39).
Reducing Childhood Hunger with the School Breakfast Program: Maryland's Report Card
More than half of all low-income Maryland students who participate in school lunch also participate in school breakfast, according to a new report released today by Maryland Hunger Solutions. The report gave an A+ to Kent, Somerset, Dorchester, and Garrett counties, while the state received a C+ in overall breakfast participation during the 2012-2013 school year. More...
Feeding More Marylanders with Stronger SNAP Policies - Policy Brief with Maryland Center on Economic Policy
Beyond the benefit to Maryland's families, children, and seniors, FSP is also a great resource for local businesses. For every $5 in new SNAP/FSP benefits, it generates $9 in economic activity for participating grocery stores. More...
Thirteen Percent of Maryland Households Struggling with Hunger
One in eight households in Maryland struggled with hunger, on average, over the years 2011-2013, according to new data released today by the U.S. Department of Agriculture (USDA) Economic Research Service in its annual report on food insecurity. More...
Maryland Hunger Solutions and Dozens of Partner Organizations Urge the Maryland Congressional Delegation to Cosponsor Summer Meals Act of 2014
Advocates and anti-hunger organizations from across the state of Maryland joined together this week to urge Maryland's Congressional delegation to cosponsor the Summer Meals Act of 2014, a bipartisan bill that would strengthen, protect, and expand access to the Summer Nutrition Programs. More...
Baltimore Partnership to End Childhood Hunger and Maryland Hunger Solutions Call on Congress to Allow Three Meals Per Day in Summer Nutrition Programs
The Baltimore Partnership to End Childhood Hunger, Maryland Hunger Solutions, and Sharon Baptist Church will join parents, care takers, faith leaders, teachers, program administrators, students, and residents of Baltimore on Saturday to urge elected officials and leaders in Maryland to allow summer meals sites to serve three meals a day. Currently, a child can receive three federally funded meals each day during the school year through the School Breakfast Program, the National School Lunch Program, and the At-Risk Afterschool Meals Program. During the summer, sites may only serve two meals through the Summer Food Service Program. More...
MEDIA ADVISORY: Washington County Public Schools to be Presented with Hunger-Free School Awards
Twelve Washington County Public Schools will be recognized on Tuesday June 10, 10:30 a.m. at Salem Elementary School for becoming "Hunger-Free" by electing the Community Eligibility Provision, a new tool created in the Healthy, Hunger-Free Schools Act of 2010. More (pdf).
Oppose Changes to Healthy School Meal Standards and the WIC Food Package
Congress is currently considering a roll-back of the healthy school meal standards that have added more whole grains, fruits, and vegetables to school meals, while also limiting sodium and fats. In addition to the proposed changes to school meal standards, the inclusion of fresh, white potatoes in the WIC (Women, Infants, and Children) food package threatens the nutritional and scientific integrity of the WIC program by allowing Congress, not nutritionists, to decide what's best for the health of mothers and young children. Let Congress know that you support the health of our nation's children and the school meal standards in the Healthy, Hunger Free Kids Act of 2010. More...
New Report Finds that Participation in Summer Meals in Maryland Increased in 2013
Maryland made significant strides in its efforts to reach more low-income children with summer meals in 2013, according to Hunger Doesn't Take A Vacation (pdf), a report released annually by the Food Research and Action Center (FRAC). 50,902 low-income children were served summer meals on an average day in July 2013, an increase of 7.4 percent from last year. Summer meals reached 19 students for every 100 low-income children who got regular school year school meals in the 2012-2013 school year, outpacing that national rate of 15.1:100. More...
Maryland Alliance for the Poor Releases Its Biennial Maryland Poverty Profiles
The Maryland Alliance for the Poor (MAP) publicly released its 2014 Maryland Poverty Profiles (pdf), which highlight key indicators reflecting the realities of poverty in Maryland and each of its 24 local jurisdictions.
Oppose Overriding the WIC Food Package
Due to pressure from particular food industry interests, Congress is currently considering a bill that will legislatively change the WIC food package to include fresh white potatoes, an item that has not been recommended by the team of nutritionists and scientists at the IOM who carefully studied what is needed to supplement the diets of mothers and young children. Changing the WIC food package by legislative fiat – simply because they have the political muscle to do so, irrespective of the nutritional needs of mothers and children — should not be encouraged. Please consider signing on to the letter below to let your voice be heard in Washington. Follow this link for more information...
Thousands Join Maryland Hunger Solutions to Hear the Maryland Crunch!
Thousands of students, teachers and leaders across the state of Maryland joined Maryland Hunger Solutions in Hear the Maryland Crunch, a state-wide, synchronized bite into an apple to raise awareness about the importance of school breakfast in the fight against childhood hunger.More...


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Outreach Services, Youth Services, and other WCCLS support programs.
Washington County Cooperative Library Services accepts cash and check donations to support the various programs it administers. If you would like to make a donation but do not have a particular library you wish to support, this is an option for you.
Please note in your donation if there is a special designation you would like to make, i.e. for Outreach to Homebound, youth programming, etc. It is also possible to designate WCCLS as a recipient through United Way or other employer payroll deductions.
Write checks to: WCCLS
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