text:
|Publication number
|Publication type
|Application number
|Publication date
|Sep 8, 1998
|Filing date
|May 6, 1996
|Priority date
|May 6, 1996
|Fee status
|Publication number
|Jean-claude Junqua
|Original Assignee
|Matsushita Electric Ind Co Ltd
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (8), Referenced by (70), Classifications (14), Legal Events (6)
|External Links: USPTO, USPTO Assignment, Espacenet
The present invention relates generally to continuous speech recognition. More particularly, the invention relates to a divergence clustering technique that achieves a low complexity and yet high accuracy speech model. The method allows the size (complexity) of the model set to be controlled before the models are built and the method ensures that each model is well trained.
Stochastic modeling forms the basis of a class of speech recognizers, in which speech is modeled statistically and the recognition process involves a decision-making process in which the average loss per decision is as small as possible. One such stochastic or statistical approach uses the Hidden Markov Model (HMM) as the basic topology for representing speech within the computer. Each speech utterance (such as each phoneme) is modeled as a sequence of separate states, with identified transitions between those states. To each state is associated a probability distribution of all possible output symbols--the symbols being represented by suitable labels. Specifically, each state is represented by a probability distribution representing the probability of producing a certain emission of speech frame or feature vector when a transition from this state occurs. In addition, each identified transition between states is represented as a probability of transition from that state to the other designated state.
In constructing a speech recognizer using a statistical model such as a Hidden Markov Model, it is customary to start with a definition of the topology. Specifically, the topology defines the number of HMM states that will be used to model each subword, which state-to-state transitions will be allowed and the number of Gaussian densities used to represent each state. There is a strong temporal constraint in speech, thus left to right HMMs are generally used. The number of Gaussian densities used per state affects the accuracy with which the model represents the actual speech data.
After the topology has been determined the model is then trained, using instances of actual speech for which the output symbols are known in advance. The model is trained to arrive at statistically determined parameters (e.g. mean and variance) based on the training data set. In this regard, the designer may select to use single Gaussian density representation for each state. However, to more accurately model a complex waveform often several Gaussian densities are combined, as a Gaussian mixture density, for a more accurate representation. In addition, each set of Gaussian parameters can be weighted. The decision to use Gaussian mixture densities in place of single Gaussian densities involves tradeoffs. Although more accurate, Gaussian mixture density models are considerably larger, requiring significantly greater memory allocation and processing cost.
As a pattern recognition problem, continuous speech recognition is very challenging. For one reason, phonemes are pronounced differently in different contexts. The preceding and following phonemes (neighboring phonemes) affect the sound of a given phoneme. For example, the "w" phoneme in the triphone "ow-w+ih" sounds different than the "w" in the triphone "ah-w+ah." In the preceding example, each triphone comprising three neighboring phonemes has been spelled using simplified multi-letter spellings (a succession of three arpabet symbols) similar to those used in the pronunciation key of a dictionary.
To take into account that phonemes sound different in different contexts, a triphone model may be employed in the speech recognizer. However, this also greatly increases the complexity of the model. The American English sound system can be represented by 47 phonemes. Thus to model each phoneme in a triphone context, there are 47�47�47 (over 100,000) different triphone combinations that must be modeled. The phoneme "a" would require 47�47 (over 2,200) individual HMM models in such a triphone representation (assuming any sound can follow any other sound). Not only does this take a lot of storage space and computational processing power, but it is frequently difficult to train each of the models--the training database may not have enough examples of a given triphone combination to yield reliable results.
Currently there is a need to reduce the complexity in statistical models used for speech recognition. Ideally, complexity of the model should be accomplished in a way that minimally affects accuracy of the model. Clustering is a technique used to reduce model complexity.
There are various technqiues for clustering. Some of these techniques use examples or training data to compute the degree of closeness between models; other techniques do not. Some techniques compute the degree of closeness using the parameters of the Hidden Markov Model (e.g., mean and variance). Where there is no a priori knowledge upon which to base the clustering technique, the models may be clustered using statistical data representing their differences. However, such a statistical approach is completely blind--there is no effective way to verify that the clusters are reasonably built.
One form of clustering is described in D'Orta, Paolo et al., "Phoneme Classification for Real Time Speech Recognition of Italian," ICASSP-87, pp. 81-84. D'Orta describes a conventional clustering technique that employs statistical processing to simplify the HMM models. HMM models having similar parameters are grouped together into a cluster, with the cluster then being replaced by a single HMM model to represent all previous models grouped with that cluster. Cluster grouping is performed based on a distance calculation.
The problem with this clustering technique is that poorly trained (i.e. unreliable) models can be indiscriminately included along with the well-trained ones. This degrades reliability, and some resulting clusters may be too poorly trained to support more accurate mixture density models. Moreover, if clustering is repeatedly performed using such statistical processing, there is no guarantee that the small set ultimately achieved will give a good representation of all phonemes in all different contexts. Some phonemes may be well modeled, while others may be poorly modeled and the system designer has little or no control over this.
Another clustering technique is described in Wong, M. "Clustering Triphones by Phonological Mapping," ICSLP-94, pp. 1939-1942. This reference describes how phonologic knowledge can be used to cluster phonemes of the same class (i.e. cluster all "a's" together). This technique depends on a priori phonetic similarity and not on actual training data. Thus this technique depends on fixed a priori knowledge. However, it is difficult to describe what happens in speech with a set of rules.
The present invention overcomes some of the shortcomings of prior clustering techniques. The method of the invention yields a small set of HMMs (low complexity). The size of the set is controlled by the system designer before the models are built. However, unlike conventional statistical clustering, the method ensures that each HMM model is the result of adequate examples of training data, to ensure that each model is well trained. Because each of the models is well trained, Gaussian mixture densities can be used to represent the states of each model for improved accuracy. Contrast this with conventional statistical clustering, where use of higher order Gaussian mixture densities on poorly trained clusters will not improve accuracy: an accurate view of an inadequately trained model does not yield an adequately trained model.
The method for deriving clusters according to the invention is particularly useful for deriving clusters of subwords, such as phonemes. Although the present implementation concentrates on the subwords within a given word unit, the same techniques can be employed to modeling subwords across word boundaries.
By way of summary, the method involves these steps. First, the training data is labeled or segmented to identify the beginning and ending of all subwords. A label or token is associated with each. In the presently preferred embodiment the subwords are phonemes, modeled as triphones to include the left and right phoneme contexts.
Next, HMM models are constructed for all triphone combinations, with the center HMM state of all phonemes of the same class being tied. Thus, for example, all HMM models for the triphones of class "w" are constructed so that the center HMM state is common to all members of the class. After state tying the HMM parameters are recomputed.
Next, a clustering operation is performed by first determining the number of models desired in the end result. Then, on a class-by-class basis, the HMM models are marked or otherwise segregated into those that are sufficiently trained (good) and those that are insufficiently trained (bad). Sufficiency of training is dictated by the number of examples of the particular triphone as represented in the training data. Bad models are merged with the nearest good model and the resulting clusters are each retrained using all training data for that cluster, including any training data from the bad model. Thus although a poorly trained model does not have enough examples of training data to be considered reliable, the training data associated with that model is nevertheless used when training the new cluster to which that model has been assigned.
After all bad models have been eliminated, the remaining clusters are regrouped (based on closeness) and retrained through a series of iterations designed to achieve the desired number of clusters. Specifically, the clustering process is guided so that the final number of clusters in each class is restricted to be between a lower limit (Nmin) and an upper limit (Nmax).
Finally, after the desired number of models has been achieved through repeatedly performing the preceding steps, the number of mixture components may be increased. This will increase the accuracy of the model.
Unlike conventional clustering techniques, the present invention achieves a controlled model size while ensuring that each phoneme is represented by sufficient training data. This being the case, the number of mixture components can be readily increased, to increase accuracy of the model, with confidence that the increased accuracy models are still well trained.
For a more complete understanding of the invention, its objects and advantages, refer to the following specification and to the accompanying drawings.
FIG. 1 is an overview of the method of the invention according to the presently preferred embodiment;
FIG. 2 is a flowchart further describing the Label Data step;
FIG. 3 is a diagram explaining the triphone concept;
FIG. 4 is a state diagram illustrating the State Tying step;
FIG. 5 is a data flow diagram comparing the well-trained and poorly-trained models;
FIG. 6 is a flowchart diagram illustrating the Clustering step;
FIG. 7 is a graph showing an analysis of the left context-dependent distribution models;
FIG. 8 is a flowchart illustrating the Accuracy Increasing step; and
FIG. 9 is a flowchart diagram depicting an alternate embodiment of the clustering step.
Referring to FIG. 1, an overview of the presently preferred method will be presented. In studying this overview, it should be kept in mind that the method constructs a well-trained, low complexity model that may be used in computer-implemented or machine-implemented speech recognizers. The technique for generating the model in accordance with the invention is, itself, a computer-implemented technique. In constructing the speech model (phoneme models) a training database is used. The presently preferred embodiment was constructed using the TIMIT database, representing some 3,696 carefully selected sentences for which all phonemes are labeled, with the beginning and ending of each noted. The TIMIT database is available from Linguistic Data Consortium LDC-9351. Although carefully constructed and painstakingly labeled by hand, the TIMIT database is still relatively small and does not contain enough examples of each phoneme in each different triphone context to ensure good training for the over 100,000 models. This shortcoming is solved by the present invention.
The first step in constructing the low complexity model is to process the input test speech data 10 by segmenting it into subword units according to neighboring unit context. In the presently preferred embodiment the subword units are phonemes and a triphone model is employed that reflects both left context and right context phoneme neighbors. Of course, the principles of the invention can be applied to other word or subword modeling topologies.
The first step of the method, depicted at step 12, is to label the training speech data to identify the beginning and ending of all subwords. This is depicted diagrammatically at 14, illustrating spectrograms of two allophones of phonemes "w" and "ah," in their respective triphone contexts. Note that the beginning and ending of each phoneme is identified and labeled. In FIG. 1 the beginning and ending of each phoneme is shown by braces each with an associated label 16 and 18. Taking label 18 as an example, the "ah" phoneme is described in a triphone context, the left context being "w" and the right context being "n."
The next step is to construct Hidden Markov Models for each of the subword units (phonemes). In the presently preferred embodiment a three-state Hidden Markov Model is adopted. This is not intended as a limitation of the invention, as suitable models containing a greater or fewer number of states could be employed. In FIG. 1 two three-state Hidden Markov Models have been depicted at 20 and 22 for illustration purposes. Given that American English can be represented by 47 phonemes, and given that each phoneme may have 47 left and 47 right contexts, the total number of models to represent all phonemes in triphone context exceeds 100,000 (47�47�47) assuming any sound can follow any other sound.
To reduce the number of parameters to estimate, the next step (Step 24) is to tie the middle HMM states for subwords of the same class. It is reasonable to do this because only the left and right sides of the phoneme may be influenced by coarticulation. This is depicted diagrammatically by the rectangle 26 in FIG. 1. Thus all center states of phoneme "w" are of the same class, regardless of right and left context.
Next, in Step 28 the models are clustered according to HMM similarity. As illustrated at 30, models are classified as either well trained (good) or poorly trained (bad) by comparing the number of training examples to a predetermined threshold. All models having training examples fewer than the threshold are classified as bad and all models having training examples greater than the threshold are classified as good. After this classification, all bad models are assigned to the nearest good model, thereby eliminating the bad models. If the number of models obtained after classification is not between predetermined lower and upper limits, Nmin and Nmax, then the threshold is adjusted up or down to adjust the number of models before bad models are assigned to good models.
After clustering, the number of HMM Gaussian mixture components may be increased for greater accuracy (Step 30). To illustrate the concept diagrammatically, a complex probability distribution 32 is depicted as being modeled by a mixture of three Gaussian curves 34, 36 and 38.
The result is a set of generalized subword units in the form of a computer-readable or machine-readable database 40 that may be used for subsequent speech recognition.
Turning to FIG. 2, the data labeling Step 12 is shown in detail. For purposes of illustrating the inventive techniques in the more general case, it will be assumed that the seed models are to be constructed from a database of prelabeled speech (prelabeled database) that may not necessarily be the same as that provided by the database to use during clustering (task database). Thus, in FIG. 2, the task database is depicted at 42 and the prelabeled database is depicted at 44. As previously discussed, the presently preferred embodiment was constructed using the TIMIT database that comprises numerous (but by no means complete) examples of speech already segmented and labeled.
The present invention segments and labels the task database 42 by finding subword segment boundaries in the task database using seed models obtained from the prelabeled database 44. This step is depicted at 46. After segmentation, each subword is assigned a label (Step 48) to identify each subword in its triphone context. Labeling is performed using a predetermined subword context schema (such as a triphone schema) depicted at 50. The result of Step 12 is a task database to which beginning and ending pointers and labels have been added.
By way of further illustration of the presently preferred triphone context schema, FIG. 3 shows an exemplary subword or phoneme 52 that is bounded by neighboring left subword 54 and right subword 56. In the corresponding label 58, the subword 52 is represented by the symbol "ah" and the left and right subwords 54 and 56 are represented by the symbols "w" and "n," respectively.
After the task database has been segmented and labeled (Step 12) the context-independent phonemes are trained on this database. Then state tying is next performed (Step 24) as illustrated more fully in FIG. 4 and triphone models are trained. In FIG. 4 the individual models of a single class are represented as individual points within rectangle 60. Rectangle 60 thus represents all HMMs for phonemes of the same class. As illustrated at 62 and 64, each of the models comprises a plurality of Hidden Markov Model states according to the predefined topology. In FIG. 4 the Hidden Markov Model state diagrams have been illustrated for two contexts of the phoneme "ah," namely j-ah+n and w-ah+n.
State tying is performed by reconfiguring each of the models so that the center state S2 is shared in common or "tied." Thus, as depicted at 66, the center state S2 is tied for the two illustrated contexts j-ah+n and w-ah+n.
Unlike conventional statistical clustering techniques, the present invention treats well-trained models and poorly-trained models differently. This concept is illustrated in FIG. 5. In FIG. 5 the task database 42 can be expected to represent some subwords well and other subwords poorly, depending on how many speech examples of the particular subword occur in the task database. If, as illustrated at 68, a particular subword is represented many times in the task database, then a well-trained model 70 is produced. On the other hand, if a particular subword contains few examples in the database, as depicted at 72, a poorly trained model 74 is produced. The present invention populates or trains each model using the task database that was segmented and labeled in Step 12. For each phoneme model trained, a score is maintained to indicate the number of training instances or examples that were used to train that phoneme model. The invention is thus able to discriminate between good models and bad models based on the number of training instances. This may be accomplished by comparing with a predetermined threshold. In this way, the present invention produces a speech model comprising a controlled number of subword models that are each well trained.
The clustering Step 28 of the present invention is shown in detail in FIG. 6 beginning at 76, the first step is to establish the number of Hidden Markov Models that are desired in the recognizer. This is shown at Step 78. In the presently preferred embodiment the method directs or steers the clustering steps so that the size or complexity of the model is controlled to be between Nmin and Nmax models per class. The ability to dictate the size and thereby control the complexity prior to training while simultaneously controlling the quality of each cluster is an advantage. In conventional statistical clustering techniques the system designer has no way to readily control the quality of the models in each class, as the quality of the models is strictly dictated by the statistical distribution of the data and the number of training examples which the designer has no control over.
FIG. 7 shows the relationship between the number of models remaining as a function of threshold on the TIMIT database. Note that as the number of examples per model increases the number of models corresponding to this number of examples also increases, as indicated by the table included in FIG. 7. The curve of FIG. 7 can be used to determine how to set the appropriate threshold to approximately achieve the number of models desired in the resultant speech model 40.
Proceeding with the clustering method, after the desired number of HMMs is established in Step 78, the method next identifies all HMMs that are well trained (good) and all HMMs that are poorly trained (bad). This is shown in Step 80 and diagrammatically depicted at 82. Depicted in the inset figure at 82 is a representation of all models (shown as individual dots) that are segregated into good and bad categories. The threshold is then adjusted for each class to be between Nmin and Nmax. If Nmin cannot be achieved, all the examples are used to represent that class (Step 83). If the task database is large enough Nmin will usually be achieved.
Next, each of the poorly-trained models is merged with the closest well-trained model in Step 84. This procedure is diagrammatically illustrated at 86 and is performed by merging the examples or instances of each poorly-trained HMM with the examples of the closest well-trained HMM. This is done for all subword classes and then the models are retrained. The presently preferred embodiment uses a weighted divergence computation to ascertain distance. This is indicated at 88. The weighted divergence computation is performed according to equation 1 presented below. In the equation, two models Mi and Mj and a set of NC examples are described. For each model Mi the subset of its training examples is represented by the list of indices Ii. Aik is the kth training occurence in Equation 1 P<Ai.sbsb.k |Mi > is the isolated emission probability of Aik by the modeli M and N is its number of occurences. In Equation 1 note the weighting factor 1/Ni +Nj is applied to the divergence computation. The weighting factor has the effect of giving greater emphasis to models that are well trained, while giving less emphasis to models that are poorly trained. To compute the distance between all models in a class according to Equation 1 the technique described subsequently as "Summing Simplification" may be used. ##EQU1##
Finally, in Step 104 the process is repeated for the next subword class. The process ultimately ends when all classes are processed as described above. Then the models are retrained.
The result, illustrated diagrammatically at 106, is a plurality of clusters for each subword class, with each cluster having between Nmin and Nmax HMMs.
An alternate embodiment for performing the clustering step 28 is illustrated in FIG. 9. It is similar to the procedure illustrated in FIG. 6, except that the threshold adjusting step is not employed as at Step 83 of FIG. 6. Instead, after the poorly-trained clusters are merged with the closest well-trained ones in Step 84, the procedure performs a check at Step 90 to determine whether the current number of good HMMs is already lower than the specified maximum value Nmax. If the number of models is already below the maximum, then control branches immediately to Step 104. At Step 104 the processing for the current subword is complete and the foregoing process is repeated (beginning at Step 76) for the next subword class. The procedure continues in this fashion until all of the subwords have been processed. When all have been processed the clustering procedure 28 ends.
Assuming the number of good HMMs is not below the maximum threshold at Step 90, then a further clustering procedure is performed to reduce the number of HMMs. At this point it will be understood that all of the HMMs represent well-trained models, the poorly-trained ones having been eliminated at Step 84. Although all models at Step 92 are good ones, there will still be some that are based on fewer training examples than others. By sorting the list of HMMs in order of the number of training examples, the HMM having the fewest examples may be readily identified as depicted at Step 92 and further graphically illustrated at 94. Next, in Step 96, as graphically depicted at 98, the HMM that was selected as having the fewest examples is then compared with each of the remaining HMMs, to determine which of its neighbors is the closest. Again, the weighted divergence computation is used (as indicated at 100) according to Equation 1 presented above. The HMM having fewest examples is then merged with its closest neighbor by merging its examples with that of the closest neighbor. In so doing, the number of good HMMs is reduced by 1.
Next, at Step 102, the number of good HMMs is compared with the maximum threshold value previously set as Nmax. If the current number exceeds Nmax, then the procedure branches back to Step 92 for further reduction of the size of the set. Each pass through the loop (Steps 92 and 96) reduces the number of models by one model.
Finally, when the number of good HMMs is below the maximum threshold Nmax, control branches out of the loop to Step 104. As previously noted, the entire procedure beginning at Step 76 is repeated for each subword class, until all subword classes have been processed to eliminate the poorly-trained models and to consolidate the well-trained ones to achieve the desired size (desired complexity). Then the models are retrained. The result, diagrammatically illustrated at 106, is a plurality of clusters for each subword class, with each cluster having between Nmin and Nmax HMMs.
Regardless of whether the procedure outlined in FIG. 6 or the procedure outlined in FIG. 9 has been used, the model for each subword class is optimally trained (given the limited data that may be available) and constrained to a model size dictated in advance by the designer through selection of the Nmin and Nmax parameters. If desired, the model can simply be used at this point in a speech recognizer. However, if higher accuracy is desired, the number of Gaussian densities to represent each model may be increased, as shown in Step 30 (FIG. 1). FIG. 8 presents a more detailed explanation of Step 30 beginning at Step 104. For each subword HMM generated by Step 104 (FIG. 6) the number of Gaussian densities used in the model may be increased (Step 110). Then, each model is retrained (Step 112) to increase the model's accuracy. The retraining step uses all examples (all training data instances) associated with that model. The result is a more accurate mixture density model based on the additional training data.
Although increasing the number of mixture components may not be necessary in some implementations (depending on the accuracy required), the implications of this step are still quite important. Had the increased number of mixture components been used at the outset of the model building process, a great deal of storage space and processor energy would need to have been expended, possibly without benefit; and it may not be possible to train the models due to the inadequate number of examples. Increasing the number of mixture components used to represent a poorly trained model gives little benefit, as the higher accuracy of the mixture density model would be largely wasted on the poor training set. Moreover, without a meaningful way to reduce the number of clusters, the resulting mixture density models would be too large and cumbersome for efficient use by a speech recognizer. The impact of this would be felt each time the recognizer is used due to the large number of parameters needed to represent the speech model.
In contrast, the present invention efficiently reduces the size of the overall model, eliminating poorly trained models in the process, and increasing the instances of training data for those models that are retained. Thereafter, a higher number of mixture components can be used without unduly increasing the size of the overall speech model and a more robust model results, because the models are better trained.
While the invention has been illustrated and described in its presently preferred form, it will be understood that the invention is capable of certain modification without departing from the spirit of the invention as set forth in the appended claims.
Let's call {Mi } the set of bad models and {Mj } the set of good models. For each divergence measure d(Mi Mj) we need the following emission probabilities:
{log P<Ai.sbsb.k |Mi >}i.sbsb.k.sub.εI.sbsb.i, {log P<Aj.sbsb.k |Mj >}j.sbsb.k.sub.εI.sbsb.j and {log P<Aj.sbsb.k |Mi >j.sbsb.k.sub.εI.sbsb.j.
Let's assume we have a total of N models and Nt examples, of which P bad models with Ntb examples and (N-P) good ones with Ntg examples. Then we need: Nt+P*Ntg+(N-P)*Ntb emission probabilities to process the whole clustering. But we can only compute for each model ##EQU2## and the matrix ##EQU3## (the same thing for Mj with Bj and Bji). Then the divergence between two models can simply be written as: ##EQU4## Therefore with the same assumptions, for N models, P bad ones and (N-P) good ones we need only: N+2*P*(N-P) emission probabilities to process the whole clustering.
|Cited Patent
|Filing date
|Publication date
|Mar 21, 1985
|Nov 8, 1988
|American Telephone And Telegraph Company, At&T Bell Laboratories
|Hidden Markov model speech recognition arrangement
|Apr 3, 1987
|Feb 7, 1989
|Dragon Systems, Inc.
|Speech recognition method
|Mar 12, 1987
|May 9, 1989
|International Business Machines Corporation
|Speech recognition method
|Oct 25, 1989
|Dec 24, 1991
|Xerox Corporation
|Character and phoneme recognition based on probability clustering
|Mar 21, 1991
|Feb 22, 1994
|Mitsubishi Denki Kabushiki Kaisha
|Pattern representation model training apparatus
|Jun 1, 1993
|Sep 12, 1995
|Matsushita Electric Industrial Co., Ltd.
|Training module for estimating mixture Gaussian densities for speech unit models in speech recognition systems
|Dec 21, 1992
|Mar 26, 1996
|Oki Electric Industry Co., Ltd.
|Speech recognition method and system using triphones, diphones, and phonemes
|Apr 12, 1994
|Jan 28, 1997
|Xerox Corporation
|Method of speaker clustering for unknown speakers in conversational audio data
|Citing Patent
|Filing date
|Publication date
|Apr 15, 1998
|Oct 31, 2000
|Microsoft Corporation
|Dynamically configurable acoustic model for speech recognition system
|Aug 3, 1998
|Oct 16, 2001
|Xerox Corporation
|System and method for recognizing user-specified pen-based gestures using hidden markov models
|Nov 6, 1998
|Nov 27, 2001
|Lernout & Hauspie Speech Products N.V.
|Method and apparatus of hierarchically organizing an acoustic model for speech recognition and adaptation of the model to unseen domains
|Apr 30, 1999
|Apr 9, 2002
|Julian Odell
|Speech recognition system and method
|Oct 7, 1998
|Apr 23, 2002
|Masoud Loghmani
|Voice-optimized database system and method of using same
|Mar 11, 1999
|Jun 11, 2002
|Fuji Xerox Co., Ltd.
|Methods and apparatuses for segmenting an audio-visual recording using image similarity searching and audio speaker recognition
|Apr 20, 1999
|Oct 8, 2002
|Matsushita Electrical Industrial Co., Ltd.
|Speech recognition training for small hardware devices
|Aug 25, 2000
|Jul 29, 2003
|Intel Corporation
|Selective merging of segments separated in response to a break in an utterance
|Jul 7, 2000
|Mar 15, 2005
|Koninklijke Philips Electronics N.V.
|Speech recognition device with transfer means
|Aug 16, 2001
|Sep 6, 2005
|Sony Electronics Inc.
|Retraining and updating speech models for speech recognition
|Oct 7, 1998
|Sep 6, 2005
|Masoud Loghmani
|Telephony-data application interface apparatus and method for multi-modal access to data applications
|Feb 8, 2002
|Sep 20, 2005
|Masoud S Loghmani
|Multi-modal, multi-path user interface for simultaneous access to internet data over multiple media
|Apr 5, 2001
|Oct 25, 2005
|Verizon Corporate Services Group Inc.
|Systems and methods for implementing segmentation in speech recognition systems
|Mar 17, 2003
|Apr 4, 2006
|Rensselaer Polytechnic Institute
|System for reconstruction of symbols in a sequence
|Feb 8, 2002
|Apr 3, 2007
|Logic Tree Corporation
|System for providing multi-phased, multi-modal access to content through voice and data devices
|Oct 29, 2002
|Oct 16, 2007
|Logic Tree Corporation
|Multi-modal voice-enabled content access and delivery system
|Mar 11, 2002
|Oct 14, 2008
|Nec Corporation
|Speech recognition apparatus
|Dec 10, 2004
|Dec 15, 2009
|Microsoft Corporation
|System and method for identifying semantic intent from acoustic information
|Aug 27, 2003
|Feb 2, 2010
|Microsoft Corp.
|System and method for fast on-line learning of transformed hidden Markov models
|Sep 23, 2006
|Mar 16, 2010
|Microsoft Corporation
|Stabilization of objects within a video sequence
|Sep 23, 2006
|Jul 6, 2010
|Microsoft Corporation
|Modeling reflections within an image sequence
|Sep 23, 2006
|Jul 6, 2010
|Microsoft Corporation
|Modeling variable illumination in an image sequence
|Aug 10, 2006
|Oct 5, 2010
|Louisiana Tech University Foundation, Inc.
|Method and apparatus for choosing and evaluating sample size for biometric training process
|Apr 20, 2004
|Oct 19, 2010
|France Telecom
|Voice recognition method and system based on the contexual modeling of voice units
|Aug 25, 2010
|Jul 26, 2011
|Louisiana Tech University Foundation, Inc.
|Method and apparatus to relate biometric samples to target FAR and FRR with predetermined confidence levels
|Feb 22, 2007
|Aug 16, 2011
|Logic Tree Corporation
|System for providing multi-phased, multi-modal access to content through voice and data devices
|Sep 16, 2008
|Mar 27, 2012
|Microsoft Corporation
|Parameter clustering and sharing for variable-parameter hidden markov models
|Sep 16, 2008
|Apr 17, 2012
|Microsoft Corporation
|Piecewise-based variable-parameter Hidden Markov Models and the training thereof
|Nov 5, 2010
|Apr 16, 2013
|Fujitsu Limited
|Speech recognition dictionary creating support device, computer readable medium storing processing program, and processing method
|May 19, 2011
|Dec 3, 2013
|Louisiana Tech University Foundation, Inc.
|Method and apparatus to relate biometric samples to target FAR and FRR with predetermined confidence levels
|Dec 8, 2009
|Dec 3, 2013
|At&T Intellectual Property I, L.P.
|System and method for training adaptation-specific acoustic models for automatic speech recognition
|May 21, 2010
|May 6, 2014
|Sony Computer Entertainment Inc.
|Robustness to environmental changes of a context dependent speech recognizer
|Nov 1, 2013
|Jun 23, 2015
|Louisiana Tech University Foundation, Inc.
|Method and apparatus to relate biometric samples to target FAR and FRR with predetermined confidence levels
|Oct 25, 2013
|Sep 1, 2015
|TCL Research America Inc.
|Object identification system and method
|Dec 28, 2012
|Jan 5, 2016
|Konica Minolta Laboratory U.S.A., Inc.
|Document image compression method and its application in document authentication
|Nov 25, 2015
|Jan 10, 2017
|Konica Minolta Laboratory U.S.A., Inc.
|Document image compression method and its application in document authentication
|May 23, 2001
|Jul 4, 2002
|Lee Victor Wai Leung
|Computer-implemented intelligent speech model partitioning method and system
|Mar 11, 2002
|Dec 5, 2002
|Nec Corporation
|Speech recognition apparatus
|Aug 16, 2001
|Feb 20, 2003
|Sony Corporation
|Retraining and updating speech models for speech recognition
|Mar 17, 2003
|Sep 23, 2004
|Rensselaer Polytechnic Institute
|System for reconstruction of symbols in a sequence
|Aug 27, 2003
|Mar 3, 2005
|Nebojsa Jojic
|System and method for fast on-line learning of transformed hidden Markov models
|Jun 3, 2005
|Feb 2, 2006
|Masoud Loghmani
|Telephony-data application interface apparatus and method for multi-modal access to data applications
|Dec 10, 2004
|Jun 15, 2006
|Microsoft Corporation
|System and method for identifying semantic intent from acoustic information
|Dec 16, 2004
|Jun 22, 2006
|Sony Corporation
|System and method for tying variance vectors for speech recognition
|Sep 23, 2006
|Jan 25, 2007
|Microsoft Corporation
|Modeling reflections within an image sequence
|Sep 23, 2006
|Feb 1, 2007
|Microsoft Corporation
|Modeling variable illumination in an image sequence
|Sep 23, 2006
|May 10, 2007
|Microsoft Corporation
|Stabilization of objects within a video sequence
|Feb 22, 2007
|Jul 26, 2007
|Masoud Loghmani
|System for providing multi-phased, multi-modal access to content through voice and data devices
|May 4, 2006
|Nov 8, 2007
|Texas Instruments, Incorporated
|System and method for generating heterogeneously tied gaussian mixture models for automatic speech recognition acoustic models
|Apr 20, 2004
|Nov 22, 2007
|France Telecom
|Voice Recognition Method And System Based On The Contexual Modeling Of Voice Units
|Aug 10, 2006
|Feb 14, 2008
|Phoha Vir V
|Method and apparatus for choosing and evaluating sample size for biometric training process
|Mar 30, 2007
|Oct 2, 2008
|Microsoft Corporation
|Minimum divergence based discriminative training for pattern recognition
|Sep 16, 2008
|Mar 18, 2010
|Microsoft Corporation
|Piecewise-based variable -parameter hidden markov models and the training thereof
|Sep 16, 2008
|Mar 18, 2010
|Microsoft Corporation
|Parameter clustering and sharing for variable-parameter hidden markov models
|Aug 25, 2010
|Dec 16, 2010
|Louisiana Tech University Foundation, Inc.
|Method and apparatus for choosing and evaluating sample size for biometric training process
|Nov 5, 2010
|May 19, 2011
|Fujitsu Limited
|Speech recognition dictionary creating support device, computer readable medium storing processing program, and processing method
|Dec 8, 2009
|Jun 9, 2011
|At&T Intellectual Property I, L.P.
|System and method for training adaptation-specific acoustic models for automatic speech recognition
|May 19, 2011
|Sep 15, 2011
|Louisiana Tech University Foundation, Inc.
|Method and apparatus to relate biometric samples to target far and frr with predetermined confidence levels
|Aug 6, 2012
|Jan 2, 2014
|Voice Lab Sp. Z O.O.
|Method for adjusting discrete model complexity in an automatic speech recognition system
|Dec 28, 2012
|Jul 3, 2014
|Yibin TIAN
|Document image compression method and its application in document authentication
|Oct 25, 2013
|Apr 30, 2015
|TCL Research America Inc.
|Object identification system and method
|Nov 25, 2015
|Mar 17, 2016
|Konica Minolta Laboratory U.S.A., Inc.
|Document image compression method and its application in document authentication
|Apr 19, 2011
|May 23, 2012
|Anchor model adaptation device, integrated circuit, AV (audio video) device, online self-adaptation method, and program therefor
|Apr 19, 2011
|Apr 23, 2014
|Reference model adaptation device, integrated circuit, AV (audio video) device
|Jan 28, 2011
|Sep 4, 2013
|Robustness to environmental changes of a context dependent speech recognizer
|Jan 28, 2011
|Jul 13, 2016
|Jan 21, 2003
|Aug 5, 2004
|Infineon Technologies Ag
|Training of a Hidden Markov Model using training data vectors and a nearest neighbor clustering method based on condition parameters used to describe the Hidden Markov Model
|Mar 11, 2002
|Sep 18, 2002
|Nec Corporation
|Speech recognition apparatus
|Mar 29, 1999
|Oct 21, 1999
|Microsoft Corporation
|Dynamically configurable acoustic model for speech recognition systems
|Apr 20, 2004
|Nov 24, 2005
|France Telecom
|Voice recognition method and system based on the contextual modelling of voice units
|U.S. Classification
|International Classification
|Cooperative Classification
|European Classification
|May 6, 1996
Owner name: MATSUSHITA ELECTRIC INDUSTRIAL CO., LTD., JAPAN
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:JUNQUA, JEAN-CLAUDE;REEL/FRAME:008017/0027
Effective date: 19960503
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|Fee payment
Year of fee payment: 4
|Jun 25, 2002
|Certificate of correction
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|Maintenance fee reminder mailed
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|Lapse for failure to pay maintenance fees
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|Expired due to failure to pay maintenance fee
Effective date: 20060908


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Well, so much for the grand Superbowl Pre-week that I had planned. I currently have two cookbooks and a watch to review that I didn't have a chance to write about last week.
What happened?
My husband wound up in the hospital with pancreatitis and pneumonia. He spent three days there on a clear liquid diet with an IV stuck in his arm. Thankfully he's home now, and okay, but it interrupted my work week and now I have a TON of catching up to do.
However, I did learn that hospital food is good. I had some excellent General Tso's Chicken from the cafeteria for lunch one day that was better than the stuff I order from Hunan of Stow. And I also ordered in a room service "guest" meal one night. For $5 I wound up with meat lasagna, grilled vegetables (zucchini, squash, mushrooms and onions), a salad with ranch dressing, a roll with butter, plus a piece of cherry pie and some vanilla ice cream. Oink. Leave it to me to find tasty food even in a hospital.
So, coming up this week I have a bunch of book reviews and some pre-Valentine's Day posts, along with multiple updates on my kittens and some other stuff. It's going to be crazy!

subset: commoncrawl


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Inspire ME is a weekly series of speakers and presentations sharing insight on everything from space exploration to human performance, student experience to internship opportunities, engineering research to engineering practice. Join us each week to hear from local and national researchers, industry professionals, instructors and students.
- August 26 – Get Involved in ME!
MBE Faculty and Students
- September 2 – Engineering Your Own Path
Mike Francis, Payette Brewing Company
- September 9 – Engineering Licensure and Ethics – What's It All About?
Keith Simila, Executive Director, Idaho Board of Professional Engineers and Professional Land Surveyors
- September 16 – Interaction of Angiogenic Microvessels with the Extracellular Matrix
Dr. Jeff Weiss, University of Utah
- September 23 – Career Search 101: Search and Discover
Alex Gutierrez and Leandra Aburusa, Boise State University
- September 30 – Career Search 101: Winning the Prize
Alex Gutierrez and Leandra Aburusa, Boise State University
- October 7 – Sunset for a CAREER grant: Short-term wind forecasting over complex terrain for grid integration
Dr. Inanc Senocak, Boise State University
- October 10 – Geometric Control of Multi-Agent Systems and Robotic Manipulation
Dr. Aykut C. Satici, Massachusetts Institute of Technology
- October 14 – Materials Modeling and Experiments to Extend the Applicability of Advanced Manufacturing of Materials under Extreme Environments
Dr. Jonghyun Lee, University of Massachusetts – Amherst
- October 17 – Protecting critical components of unmanned spacecraft from orbital debris impacts
Dr. Aleksandr Cherniaev, University of Manitoba
- October 21 – Novel Smart Vibration Isolation and Flexible Micropump Systems Utilizing Magnetorheological Elastomers
Dr. Majid Behrooz, University of Nevada – Reno
- October 24 – Mesoscale Modeling of Microstructural Evolution
Dr. Mahmood Mamivand, University of Wisconsin – Madison
- October 28 – Hydrogen as a Clean Energy Carrier: Removing Barriers to Large-Scale Hydrogen Distribution
Dr. Robert Amaro, Colorado School of Mines
- November 4 – Structural Polymer/Nanoparticle Composites: Processing, Structure, Properties, and Performance
Dr. Kenan Song, Massachusetts Institute of Technology
- November 7 – Motion Control, Planning and Manipulation of Nanowires under Electric-Fields in Fluid Suspension
Kaiyan Yu, Rutgers University
- November 11 – No seminar
- November 18 – DeepPIV: Measuring small-scale fluid motion in the deep sea
Kakani Katija, Monterey Bay Aquarium
- November 25 – Thanksgiving break
- December 2 – Turbulence Modeling with Large-eddy Simulation: An Introduction and an Application in Wind Engineering
Rey Deleon, University of Idaho
- December 9 – Nuclear Energy Enabling Technologies Program: An Overview of Advanced Sensors and Instrumentation
Bruce Hallbert, Idaho National Laboratory
- December 16 – Finals
Check out our past seminars! Go to the MBE Facebook page and click on Notes.


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Shipping & Returns
ShippingWe offer free priority shipping to anywhere in the US. We also offer international priority shipping at a base price through USPS. For international customers only*: You may order from one to four items for the same shipping cost (as long as the combined weight of the package is under 4 lbs or 1.8 kg). International shipping rates vary from $20 to $25.
The posted shipping time frame is contingent upon credit card approval and may be delayed should we experience difficulties in obtaining authorization. We will provide shipping confirmation along with the package tracking number.
These shipping terms are accepted by you by placing an order with us.
Luxuryia is not responsible for any loss or liability or damage that may result from leaving your package at the shipping address. Luxuryia is also not responsible for any international shipping delays caused by the customs clearance process.
Returns & Refunds Policy
We accept returns within thirty (30) days of the date listed on your receipt for most items. Items eligible for refund can't be worn, damaged, washed, or altered in any way; and all original tags must be attached.
Returns for online store credit will be processed free of charge. If you'd like a refund to your original payment method, a shipping and handling processing fee of $6.49 will be deducted from the amount to be refunded.
We will refund the full amount that has been charged by us for an order that you stop or cancel before fulfillment.
We do not accept exchanges at this time.


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The stuff was very nice and helpfull, the wireless internet is for free althogh it didn't work in the 4th floor room that we got. the room was clean and nice.


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Bakers Dog TreatsBrowse our great range of Bakers Dog Treats from Hyperdrug the oldest pet and equine pharmacy online. Buy Bakers Dog Treats at great prices with great customer service.
2 delicious chews with a soft meaty centre packed wirh omega 3 & 6 to help maintain healthy skin & a glossy coat.
£1.29 Incl. Vat
Chewy sticks with a soft, meaty centre with chicken is a delicious complementary pet food for medium sized adult dogs between 10-25kg, with a specially shaped outer to promote good oral health and reduce tartar build-up.
£1.29 Incl. Vat
Bakers Sizzlers Dog Treat Bacon Flavour 120g. Sizzlers is a chewy tasty dog treat rashers with the irresistible flavour of bacon and your dog will find them doggylicious!
Item #: BAKERSSIZ
(3 reviews)
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These mini treats are bursting with meaty flavour your adult dog will love, whether they enjoy it as a tasty snack or a training reward.
£0.95 Incl. Vat
Bakers Allsorts are treats bursting with meaty flavour and in a variety of shapes your dog will love. These delicious treats are so good your dog will do anything for them. Doggylicious!
Incl. Vat
Bakers Dental Delicious - helps to keep clean teeth the delicious way!
Specially shaped chewy outer to help keep teeth clean and gums healthy and a soft meaty filling to help keep him coming back for more! Tasty cleaning action!
£1.29 Incl. Vat
These chewy treats with chicken are designed specially for large dogs over 25kg, Just one chew a day can help support their joint mobility thanks to added glucosamine.
£2.29 Incl. Vat
Bakers Dental Delicious Dog Treat Chicken Large 270g. Dental Delicious is a dog treat for large dogs 25kg and over and helps to keep clean teeth the delicious way.
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Bakers Rewards are the delicious treats you can reward your dog with every day. Feed them whole or break them easily into pieces.
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Bakers Whirlers are delicious cheese and bacon flavoured treats. These twisty sticks are chewy and bursting with the irresistible flavour of bacon and cheese that adult dogs will love!
Item #: BAKERSALSW
(1 reviews)
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Bakers Mini Bones are delicious treats made with chicken. They're low in fat but still bursting with meaty flavour, making them utterly irresistible!
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£0.95 Incl. Vat
Bakers Jumbo Meaty Twist is an extra twisty, chewy bone packed with a delicious meaty centre, designed for dogs over 25kg. It's even bigger than the normal Meaty Twist for extra satisfaction, right down to the last chomp!.
£1.29 Incl. Vat


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Today was another of those too-rare days that I attack the worsening jumble of my desk in an effort to restore it to a more reasonable state of disorder. As usual while shuffling through the papers I saw two fire-engine-red, polka-dotted mushrooms looking up at me appealingly from a photograph in a magazine article. This time they stopped me. Tempted by that bright photo of fly agaric, Amanita muscaria, I took the pages to my desk and began what became a heady plunge into the inner story of mushrooms,
Nature and the Environment
Time Travel: When Tigers Terrorized the American Southwest
Blackwater Draw sits on a time warp. Located in a remote landscape where central New Mexico threatens to bump into Texas, it is near the dozens of great green clustered tiddlywinks that I had seen often from the air. Up close they were amazing disks of tall, healthy corn and milo grain, watered by huge irrigating arms that moved slowly around the field from a central pivot like the hands of a clock. In wetter times saber-toothed tigers, woolly mammoths, ancient camels – and humans – lived in this place.
Scourge of the Killer Bark Beetle
It seems only a couple of years ago that while driving around the outskirts of Santa Fe, New Mexico, I was struck by the number of dead piñon pine trees scattered among the sparse, desert-hugging piñon-juniper forest. As I drove higher the forest became thicker and the pine mortality climbed, too. Clearly, among the tree population, a kind of genocide was in progress, for from all appearances their neighboring juniper (cedar) trees and other plants were in fine condition.
What was going on here? Were the pines dying for want of water?
The Dinosaur in My Garden
It was looking at me from behind a large chunk of sandstone. An orange eye with a small flash of orange and blue skin flaring behind studied me, the crest moving slightly on its head. I threw a pebble and my own therapod, modified for the Holocene epoch, ran off in a dead-straight line toward its lair in a tall blue spruce. I heard the clack of mandibles and knew there would be another close by. I growled and it broke cover and made for the tree.
Roadrunners are not my favorite animals,
In appreciation of small creatures
What do you think of the bugs, worms, and the other creepy-crawlies that run for their lives when you pick up a damp piece of wood? Many will say ugh, not for me! but for some of us – entomologists surely, biologists and science writers probably – there are marvelous things to know about ants, spiders, centipedes, beetles, earwigs, millipedes, earthworms and their like. All have their places in the web of life, yet most of us are trained out of our appreciation for these small creatures, actually our small fellow creatures,
Life in the Barren Lands of Ethiopia: An Idea Fit for Transplanting to the West?
Today I opened my copy of Science magazine to the Letters section and learned about a wonderful idea. It was new to me, but apparently has been going on for a long, long time. In Ethiopia, the ancient Orthodox Church is actively providing sanctuary for trees and their related ecosystems in an otherwise devastated land.
Colgate University's Catherine Cardelus (email@example.com) and colleagues used their letter to publish a three-paragraph review report that explains how "the church leadership views biodiversity conservation as one of its primary stewardships." An accompanying aerial photograph proves the point by showing a circular forest that encircles a group of church buildings.


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Posts: 292
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My survey of lifetime, signed & numbered Nagel serigraphs indicates a strong upturn in values. Not all items are selling at super, "wishful thinking" prices, but over the last three years, prices and values are definitely up. Pieces that were selling for $300 - $400 a year or two ago are now being listed -- and selling -- for $1000, give or take. Black Robe just went out for $2000+ at auction and even Joan Collins, the much-maligned serigraph, is double what it was selling for a few short years ago. Hanson and various sizes of Paper Mill are at or over $1000 and the elusive Shades is currently listed on Ebay for $1,495.
Visiting at a gellary opening last night, I was fortunate to talk with several Nagel collectors who were active during the early 80s. Everything seems to confirm my thesis in the book that the serigraphs number far below their issue count. My guess -- and that's what it is, a guess -- is that less than half of all lifetime, numbered, signed serigraphs are gone, discarded, damaged or destroyed.
Rob Frankel
Author, "The Artist Who Loved Women" A biography of Patrick Nagel


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Created exclusively for our partnership with New York City's Cadillac House, The Runabout is a blend as elegant and classic as the car itself. We've brought together a harmony of fine Latin American coffees that express a year-round deliciousness and refinement. Whether you're looking for a timeless filter brew or a smooth coffee to serve as espresso, The Runabout—like its namesake, the very first Cadillac introduced—is both a game-changer and an instant classic.
All orders are roasted on Monday and Thursday and are shipped on the same day. Note that if you order Two-Day Shipping after 7am on any given day, your order will be processed *the following roast day*. We do this to ensure your coffee arrives to you as fresh as possible!
All coffee is delivered whole bean. If you need your order ground, please specify your brew method in the special instructions section.
For questions concerning your order, please email firstname.lastname@example.com.
We designed The Daily to be a balanced and approachable drip coffee, drinkable every day...
Lovingly named for our first store in New York City, the Waverly is Joe's espresso...
Always exciting as either brewed coffee or espresso, Nightcap - our signature decaf coffee -...


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Isabella is a nurse at Westchester insane asylum. She has been assigned Ashton Irwin's case and it now working with him. Against all odds she will do whatever it takes to free him. Even if it costs her, her life.
* Rewriting the ending *
_5_esohes_ Jul 23, 2016
in the description, i read westchester as winchester... i should probably stop watching supernatural😂😂
Mixer_Bubbles Aug 18, 2014
I'm 100% sure, that this is going to be an amazing book!!!!!


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Cattle For Sale
10 Pair spring calves; 8 bred cows for summer calves and 5 bred heifers for fall calves.
4 year old registered horned Hereford bull for sale. Reg. #43362185, sired by Ribeye 88X out of GKB cow Miss Showtime. BSE and Trich tested, ready to go to work.
Heifer born 10/27/2015, T2T Imogene 332 C18, reg. no 43621459, sired by a Durango son out of a Moler cow. She is bred to T2T Sam Houston Z311 C15, reg. no 43629250, an Encore son out of a Harland 408 cow.
3 yearling bulls for sale. HH Advance/Pioneer bloodline.
Hereford Bulls for sale in East Texas.
Black Herefords: Supreme/Grand Champion genetics. Open Replacement Heifers 9-12 mo. old; Bred replacement heifers 15-18 mo. old. Guaranteed bred to Reg. Black Hereford Bulls. Yearling bulls, 12-15 mo. old & 2 year-old bulls for sale.
Bulls for sale.
Four registered Hereford bulls for sale, 21-27 months. Moderate sized. Out of room, need to sell. $1,800 or b/o over $1,800. All sired by an Atlas Farms bull.
8 breeding age Hereford bulls and group of yearling Hereford heifers for sale.
2 Fall yearling and 20 spring yearling bulls for sale.
Clayton – Pegues Herefords available now in Decatur, TX. Private treaty Steers: dehorned and polled, clipped, halter broke and ready to load today.
6 yearling Line One bulls for sale.
17 coming two year old performance bulls, horned and polled, off test and ready to go. Ultrasound data. Call Donald Parrish (PX Feeders, Evant) at 254-679-1200 for feed program and pricing. For EPD and breeding info contact Paul Funk.
Registered Herefords for sale.
Hereford bulls, horned & polled, born March and April 2015. Sired by C Stockman 2059 ET, CRR About Time 743 & NJW 73S W18 Hometown 10Y, $3500 and up.
Breeding age bulls, show heifers, cow, pairs available at all times.
Consistent quality bulls available from proven P606, Logic, Online, Garrett bloodlines: 24- 2 yr. olds ready to go; 25- 14-17 month old long yearlings; 28- 8-11month old development bulls.
7 yearling bulls for sale.
1 - 4 ½ year old bull; 2 – 22 month old bulls and 1 13-month-old bull for sale. Low birth weight. All straight Anxiety 4th.
15 registered horned Hereford bulls for sale 12-23 months old
14 registered horned Hereford bulls, age 18 months for sale.
15 purebred horned bulls for sale. 2 - 2 year old & 13 yearlings.
For Sale: 6 head of 2015 spring registered horned Hereford bulls. Sired by HH Advance 1272Y ET, from the Holden Herefords in Valier, MT.
For Sale - 25 - 2 yr. old Bulls; 30 - Open Weaned Heifers.
Yearling and Two year old horned and polled Hereford Bulls, cows, pairs, show/replacement heifers for sale. Can help with delivery.
Horned Hereford bulls for sale – ready for service.
2-3 Heifers and 5 bulls for sale. NVE SC 33/26 semen tank for sale.
10 polled Hereford bulls; 10 Angus 14-16 month old. Fertility tested.
8 – 14 to 16 month old Hereford Bulls – out of Largent/Barber Bulls & 8 Fall born Polled Hereford Heifers – out of Purple Cotton.
25 Hereford cows w/Angus cross calves at side. Cows open.
2 Breeding age Hereford bulls for sale.
10 registered polled and horned bulls. Proven bloodlines P606 & THM Durango 4037. Visit www.whiteheadherefords.com.
6 registered horned & polled 2-year old Hereford bulls for sale.
4609 Airport Freeway Fort Worth TX 76117 Phone 817.831.3161 Fax 817.831.3162
Copyright © 2001 Texas Hereford Association
BreedingCattlePage.com | CattleSalePage.com | Website designed by EDJE Technologies, Inc.


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Articles about persons and governments to influence the development and growth of cryptocurrency industry.
A consensus on how to increase the blockchain network capacity has finally been reached, pushing the price of the digital currency to a new record high.
Current situation in the bitcoin network demonstrates that the cryptocurrency ecosystem may not be fully ready for mainstream adoption.
WannaCry Ransomware Infections Are Likely to Get Worse This Week with Bitcoin Income Expected to Increase
A large number of institutions and companies in about 150 countries were attacked by malware called WannaCry.
Australia is going to cut general sales tax for acquiring digital currency, aiming to help local fintech startups run their businesses in the country.
The country's Deputy Finance Minister unveiled that bitcoin may soon become a legal payment method, only a year after the Russian authorities threatened cryptocurrency users with jail time.
Bitcoin price dropped just as the SEC unveiled its decision but managed to recover quickly keeping close to $1,200 barrier.
The Securities and Exchange Commission has denied a request to launch the Winklevoss Bitcoin Trust ETF, prompting bitcoin price to fall by 35% within a day.
Denmark's police have used cryptocurrency transfers as an evidence for the second time in a row, what is likely to prevent bitcoin usage by criminals in the future.
Chinese leading trading platforms have imposed halts on withdrawals resulting in traders flocking to peer-to-peer marketplaces to continue buying and selling bitcoin.
Bitcoin future in the Asian market is arousing speculations after Chinese exchanges suspend Bitcoin withdrawals.
Bitcoin will continue expanding in the upcoming years, as more governments continue to adopt the technology, says the new infographic from the New Jersey Institute of Technology.
Chinese biggest bitcoin exchanges unveiled on Sunday they will impose trading fees, as the government is tightening control over digital currency.
The People's Bank of China found some violation of rules in the operations of major bitcoin exchanges.


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March 12, 2014
Reefers picked up the most of the three categories from March 2 through March 8, compared to the week before, adding 6.2%, for an average of $2.23 per mile, while vans increased 4% to $2.07 per mile. Flatbeds gained 2.4% for $2.15 per mile.
This happed as the number of spot market loads fell just by 0.2% while truck capacity declined 7.2%, putting further pressure on rates as shippers continue to work through a backlog of freight from weather delays, according to DAT.
Not surprisingly, load-to-truck ratios increased, with the biggest happening with reefers, adding 12.1%, followed by vans increasing 7.6% and flatbeds gaining 3.9%.
Meantime, new monthly numbers show spot market freight availability climbed 7.5 % in February from January, achieving unprecedented levels in the middle of the first quarter, typically the quietest time of the year for freight transportation. Extreme winter weather across the country disrupted normal supply chain networks, sending freight to the spot market to meet capacity needs
February load availability compared to the month before rose 9.1% for vans and 16% for flatbeds, but only 1.3% for refrigerated trailers.
Year-over-year freight volumes surged 83% during February to a new single-month record. Van freight increased 87%, flatbed freight rose 71% and reefer freight volume more than doubled with a 112% gain.
Capacity demands boosted spot market rates to atypical highs in February, especially for vans and reefers, according to DAT. This is seen in the year-over-year increase in all three major equipment types, as rates rose 23% for vans, 14% for reefers and 4.7% for flatbeds. Compared to January 2014, which had similar weather disruptions to the supply chain and unseasonal freight on the spot market, rates rose a more modest 2.7% for vans and 6.7% for reefers, but declined 1.3% for flatbeds.
We offer e-newsletters that deliver targeted news and information for the entire fleet industry.
Scott Sutarik from Geotab will answer your questions and challenges
Steve Carlson from Xantrex will answer your questions and challenges
On the Spot: Volvo's VNR as a Daycab and Sleeper [Video]
On the Spot: Ride and Drive at HDTX [Video]
Ultimate Test Drive: New Cascadia's Intelligent Powertrain [Video]
On the Spot: U.S. Xpress Max-imum Tech Competition [Video]
Michelin Offers New Regional Drive-Position Tire
Midtronics Diagnostic System Monitors Batteries
Firestone Launches All-Position Wide Base Radial Tire
Ancra Toss 'N Tie Helps Drivers Secure Cargo


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For the chief executive of the Guardian Media Group, Carolyn McCall, yesterday echoed O'Reilly's long-held view by announcing that her company is rethinking its online revenue strategy.
So The Guardian could well be on the way to doing what it previously pledged never to do by erecting a pay wall for some of its content.
Throughout my years working for The Guardian (as I still do, by writing a daily media blog), it was anathema to suggest that people accessing its website should pay for the privilege.
In so doing, it was adopting the ethos of the internet, in which everything should be freely available. There was good reason for newspapers to believe in the virtues of such freedom.
As online audiences rose, it was thought that advertisers would follow. Gradually, even if ad revenue never quite matched the sums spent on newsprint, the income would be sufficient to fund journalism.
As we knew even before the recession bit, it was something of a mirage. No newspaper, in Britain, the United States, indeed anywhere, has managed to attract sufficient advertising income to provide the kind of editorial resources that editors require for the daily production of quality journalism.
It has been a bitter pill for journalism's digital revolutionaries, and I include myself in that legion, to swallow. I am an extremely reluctant convert to the argument that is being put forcefully, seemingly week by week, by owners and editors who believe that payment is essential. Rupert Murdoch made his position clear last month. He said: "People reading news for free on the web, that's got to change."
Soon after that, we learned that managers at one of Murdoch's key titles, The Times, were looking at the feasibility of introducing paid-for content on its website.
In March, Gavin O'Reilly, who is about to take over from his father, Tony, at INM, said: "Our experience has been that the web is a financially deflationary area. I think we will see more and more content moving to subscription. We are looking at some charging structure."
Elsewhere, there are papers preparing to build new pay walls. New York Times editor Bill Keller is looking at the option despite his paper's previous unhappy experiment with charging for content. Audiences increased directly it stopped its subscription model. Ann Moore, chief executive of magazine giant Time Inc, is thinking of charging fees for people to use the websites of its most successful titles, Time and People. The New York tabloid Newsday has already announced its intention of obtaining subscription payments for access to its website.
The danger, of course, is that audiences will fall dramatically. Unless all newspaper publishers acted together to charge users, the chances are that people would flock to the free sites. Anyway, any such pan-industry action would certainly lead to investigations by competition regulators.
The problem in Britain, as McCall rightly mentioned, is that general news content is freely available on the BBC website and, given that it is funded specifically to provide licence fee-payers with its journalistic material without extra charge, it would be counterproductive for British papers to charge users. They would go off to the BBC.
Instead, what McCall advocated is charging for specialist material, such as the content on its Media Guardian website. In some ways, this echoes the subscription strategy adopted by the Wall Street Journal and the Financial Times.
Both of these papers have attracted paying audiences eager to access their journalism. But there is a difference, of course. The business sector is affluent, and the information provided by those titles is often necessary in order for business people to do their jobs more efficiently, and therefore make money.
It is hard to imagine that the output of Media Guardian would be viewed in the same light, even within journalistic circles.
But it is the fact that McCall has raised the issue in such stark terms that really matters. If The Guardian — and the Daily Telegraph, The Times and the Daily Mail — with their 20 million-plus unique users a month, cannot monetise their websites, then who can?
As McCall said: "It's crazy that we do so much to put content out there, but we don't get money for it."
Crazy, but is there a way to encourage people to pay for something they have been getting for free?
There have been various suggestions about finding painless methods. One is micro-payments, charging users a tiny sum for each page they view. The virtue of this system is that people are already used to it because it's how they pay for the use of mobile phones.
Another is to add a "newspaper surcharge" to the basic fee charged by an internet service provider. Again, it's possible that users would not notice, though some ISPs might baulk at acting as agents for publishers.
At the heart of this debate is continuing concern about the loss of copyright as Google and other aggregators "live off" material they do not originate.
McCall touched on that too, arguing that traffic driven to news websites is "no longer a fair swap because we can't really monetise traffic in the way we [used to]."
That's true, but who will be first to risk losing readers by charging for content?Reuse content


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I'm scared
The mention of her name does that. She is the most famous bear on Wall Street. Whatever you think is the scariest prediction on the solvency of the banks/the collapse of US house prices/job prospects in the finance industry, Ms Whitney will tell you it is going to be worse.
Not one of life's optimists, then?
It's not pathological. She just does a lot of research. People have been taking notice ever since October 2007, when she predicted Citigroup, the most significant bank in the US, would run out of money to pay its dividend.
Well she was right about that – and then some.
Indeed, and the plain-speaking manner of her delivery marks her out in an industry of wafflers. She's been nicknamed the Dollar Dominatrix for her stridency. She is married to a former professional wrestler, so go analyse.
Where does she come from?
When she made the Citigroup prediction, she was banking sector analyst at Oppenheimer & Co, a middling investment bank. She learnt her craft there at the knee of Steve Eisman. He wasn't famous before the crisis, either, but he is one of the heroes of the Michael Lewis book The Big Short, about the Wall Street misfits who saw the credit crisis coming and made a fortune betting against the banks.
With the economy recovering, presumably she is less bearish?
Ha ha. Absolutely not. In fact she is predicting the next financial crisis, the next $1 trillion government bailout, and a new era of economic misery.
Told you I was scared
She says local government finances in the US remind her of the banks before the crisis. She predicts a wave of bond defaults by cities that will ripple through the banking industry, while the mightiest state economies from California down will be crippled by a budget crisis.
Yikes. What can we do?
Hope she's wrong. For the first time.
- More about:
- Banking
- Citigroup
- Financial Crisis
- Housing Prices


text: Driver Plows Car Into Group Of People Adjust content blocking madjunk next random Comment Top NextComment Glendale, CA A Glendale man is facing attempted murder charges after he allegedly used his car to charge at three men - not just once but twice. The incident, which was all caught on surveillance video, happened just after midnight on July 10 at a convenience store on East Broadway in Glendale. Glendale police said Lloyd Morris, 55, drove his vehicle toward three men in the parking lot, narrowly missing them. Morris then backed up and charged at them a second time, hit one of the men and drove off. Police caught up with Morris a short while later. He is suspected of being under the influence of controlled substances. next Tags: funny | viral | videos | Video | Crazy | fun | terrifying | cool | fail | Fall | Failure | fails | laugh | rage | Crash | WTF +6 Views: 1262 Submitted: 07/27/2012 Facebook Twitter Google+ Tumblr Pinterest


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Essay Producing: 19 Suggestions to Improve Your Productiveness
It is actually a pretty victorious one that writing essays isn't your preferred pastimes, but professional writer service a tough job can slip in your mind and damage your ideas for the entire weekend. No picnic with buddies. No dance in the membership. Only the loneliness of the catalogue as well as your fingers slowly squeezing some chunky textual content from the computer keyboard.
The great news is that you may continue and find your weekend and social existence typically. While using the pursuing 19 technology-supported ideas, you could be relocating in the direction of concluding all of your published research in only 1 evening hours:
- Mess your office. A surprising hint, is it not? It is not necessarily what your mother stated. Folks capture for order. My postal mail across the sink time trying to find lost details and merchandise. Yet a 2015from the College or university of Groningen has unveiled shocking results. Individuals from Netherlands learned that men and women employed in disordered conditions try to reaffirm a sense of get by placing evident and well-described objectives, and finish up getting to them more quickly.
- Use a number of watches. Based upon Microsoft, surpassing it's possible to enhance your performance by from 9% to 50Percent. If it is possible, use distinct units. For instance, you may use your pc tablet for investigation along with a notebook for essay writing.
- Make use of a ranking desk (no less than every so often). A performed by Organization Insider in 2013 disclosed a tenPercent output increase, increased degrees of energy and attention resulted through a standing rather of the sitting workplace.
- Pomodoro approach. Swap work and relax for maximum productiveness. Coded from the late 1980s, this implies dividing work-flow into simple cv builders for veterans classes, generally 25 moments lengthy, used by simple splits (5-ten minutes). These times are known as &lsquopomodoros,' Italian for Andlsquotomatoes.' Francesco Cirillo, the writer of the method, applied a home area clock established as tomato for personal-handle.
- Remain properly hydrated. Inside a from the College of Eastern London, uk Institution of Mindset, members drank 3 glasses of apparent h2o prior to taking an evaluation, that they can necessary to push a handle option as soon as they saw an item on-screen. The reply effect in this particular class what food is in very least 14% more rapidly in comparison to contributors in the control team who didnt ingest something. Freshwater is very important for composing an essay that stones.
- Have fun. A existing at Loma Linda College or university in Los Angeles has proven that laughing or maybe suffering from sense of humor enhances contemplating procedures by decrease in stress chemicals. So an acceptable number resume writing service los angeles of humorous video tutorials on-line (up to quarter-hour) can assist.
- Take smashes with an individual. A bit talk throughout a ensures a 15% functionality enhance. Isnt mtss is a great should contact your friends?
- Block disruptions. Text messages and social media can hang on. If you would professional resumes writers like support hurting interruptions, hordes of professional apps really exist for you personally (Anti-Societal, Liberty LeechBlock to
note a couple of).
- Quit looking at your electronic mail. The unpredictable the outdoors of emerging messages may make looking at e-mail . Nonetheless, your lifestyle will likely be much less difficult if someone makes it strategies for review e-mail 2-3 occasions every day, rather than to evaluate it ahead of undertaking anything important in the morning.
- Sync your computer info. Dropbox, Yahoo and google Drive, Tresorit and much more will enable you to entry your docs everywhere. Forget about misplaced essays! Overlook pet dogs having your homework!
- Get rid of your internal perfectionist. Tries to make your producing excellent get time and effort. Indeed, a recent has says theres you don't need resume helps to be regarded a top college student participating in university to achieve existence.
- Commit 5-ten minutes having an inspiring read through. A stirring estimate in the superstar helps keep you heading. Should you invest above our limitations time considering one factor, youll never take actionAndrdquo (Bruce Lee).
- Make wise utilization of your . Its provide is restricted. Using the numerous temptations of today's entire world, your perseverence is often overstressed. Steer clear of adhering to a small calories diet and understanding for finals concurrently.
- Get enough sleep. Numerous research indicates that adequate (no less than 7 hrs) is very essential for school achievement, including composing essays that rock.
- Do aerobic fitness exercise. Based on facebook a 2014 from the College of Illinois, together with other health pros, normal cardio exercise exercising is wonderful for your relational recollection.
- Figure out how to contact kind. This enables you to type 50 to just one best cv editing services hundredPercent far more quickly.
- Research with a few backdrop noise. Amazingly, technology has exhibited that some history really enhances creativeness and efficiency. If your room is merely as well calm, software like or will assist fill up the spot.
- Do not have fun playing the role of unnaturally help with resume beneficial. It's normally easier to be legitimate. A current realizes that calming and wishing to find the best operates exclusively for optimists. Pessimists, on the other hand, can boost their overall performance by 30% once they think about the worst possible benefits. So, what's the most severe that could happen in the event you can't build a passable essay?
- End performing every thing yourself. Delegating obligations is key to accomplishment. You aren't alone, and getting aid in essay composing might be less difficult than you suspect.
Utilizing these little-practices, essay creating can be produced straightforward. Examine better and revel in your skyrocketing productivity.
School Task Aid: 75 Techniques for Moving with no Extreme Volume Of Effort
15 The four weeks of jan, 2016by 1 selection 8find it valuable
Fight-examined approaches for a b college task
Therefore you are designated an additional institution project, appropriate? Perhaps you have created the right path with all the five traditional stages denial, anger help with a resume, dealing, despression symptoms and acknowledgement? Right here are 75 fast ideas which will help to invigorate your producing and allow you to generate an effective grade with no excessive quantity of hard work.
These time-evaluated strategies may be the only university task help you to want:
Produce the correct situations
- Search for a quiet place exactly where no one will affect you.
- Have sufficient time, so steer clear of starting much too late into the evening.
- Bring a cup of obvious drinking water and then make specific the region you will end up used in has lots of circulation.
Focus is vital
- Get rid of all distractions prevent by far the most distracting internet sites (this kind of apps as FocusBooster, while paying attention is useful).
- Choose the best audio the most beneficial options are classical, jazz music along with other genre that's different for you.
- Have brain-improving snack food items (nuts, seed products, many fruits) on palms.
Comprehend the job
- Ensure that you know the principal objective of a career that's, decide from the start off nevertheless if you are simply being wanted to visit over, dispute, criticize or examine.
- Research any unknown words throughout the information from the project.
- Speak to your instructor if clarification is required.
Gather all of the necessities
- Find the significant things or acquire them from trustworthy friends top cv services.
- Possess a laptop, writing instruments, pencils along with a laptop prepared.
- Be comfy inside your seat, although not also secure (produce function in your bed).
- Get started with basic Google lookups (scholar.yahoo and google.com, textbooks.search engines.com are really beneficial here).
- See your university library and fully grasp on-line directories.
- Consider using crowd-tracking down platforms (, HelpaReporter or maybe your social media connections listing can be very practical).
Velocity understanding capabilities can sort out school task producing
- Use Ctrl F to appear papers instead of learning them entirely.
- Focus on abstracts, intros and findings.
- If you have to read content resume writers service completely, take into account particular speed-researching programs, for example Boba, Acceleread and ReadQuick.
Start citing options inside the start off
- Turn it into a guideline to cite options while wiki you research.
- Be familiar with rules from your suggested strategy for producing (APA, MLA and many others.).
- Generally save the links or whole textual content paperwork in order to avoid burning off them accidentally.
Stay away from plagiarism
- Use conversation spots for immediate quotations.
- Choose the writers you cite.
- Always offer sources whenever you cite essential information, for instance data and numbers.
Make efficient notices
- Publish reduce any important tips you discover.
- Publish decrease tips mainly because they mixture your mind.
- Draw imagination maps that connect tips with arrows rather of producing out complete phrases.
- Set a clock making plans to make as numerous ideas as you possibly can within a limited time.
- Start a timer and create reduce each and every idea that crosses your mind.
- Decide on a certified ideas.
- Use classic composition (Release literature evaluation methodology results talk limits verdict).
- Write 1 whole phrase for every single portion.
- Make a decision the volume of terms you should compose for each and every area.
Stay productive
- If you feel fatigued, execute some exercising to maintain your bloodstream working and sense restored.
- Take tiny breaks regularly need help with resume. By way of example, 50 minutes operating and ten minutes rest is actually a established good results formulation.
- If you feel unwilling to publish, attempt the idling approach. Sit down and do practically nothing for twenty or quarter-hour.
Create an intro
- Provide an focus connect some stunning fact, an intriguing price or perhaps a rhetorical query.
- Problem your main objectives.
- Create your theory or research concerns.
Shine a thesis assertion
- Add a thesis declaration inside the accomplish in the introduction portion a short report on your whole paper.
- Guarantee that the thesis assertion is smart, and solutions the situation, Andlsquoso what?
- Commence including some discussion within your thesis statement to display that you can to discover the cv writers online various sides from the issue.
Publish a literature review
- Split resources into teams according to start perspective chosen through the experts.
- Look for facets of your issue that other authors haven't resolved.
- Develop a sensible assurance to complete that space.
Choose method
- Decide in between quantitative or qualitative research and resume creation services no matter whether you may give a lot more concentrate on primary or second resources.
- Decide no matter if rep or comfort sampling is more suitable for your topic.
- Take into account the amount of options you'll require to be able to obtain acceptable conclusions.
Explain your results
- Provide a detailed explanation of the discoveries.
- Give only numbers.
- Stay away from expressing your opinion currently.
Discuss your conclusions
- Attract results away from your conclusions.
- Discuss the key styles.
- Identify any relationships amongst different elements of your function.
Mention possible limitations
- Go over achievable factors behind prejudice in research workers or participants.
- In case you employed ease sampling, make sure to take it up amid these limitations.
- Go over factors that might be improved in your school task.
Compose a summary
- Get started with a short review of your quest process and results.
- Avoid which include any new information and facts.
- Supply recommendations for more investigation.
Have a deserved relax.
With this point, youll forget about you desired institution project help initially.
- Prior to proofreading, take a relax.
- Call a friend.
- View some funny videos.
Use spell and sentence structure checkers
- Evaluate your grammar with Grammarly, Ginger herb root or WhiteSmoke.
- Evaluate your style with Expresso or WordRate.
- Review your punctuation with Spellcheckplus.com or Onlinecorrection.com.
- Examine every single suggestion from proofreading professional resume writers equipment.
- Examine the entire aspect again without needing these power tools.
- To have a refreshing eyesight, try studying the parts in reverse purchase, or printing your draft out rather than understanding it within the screen.
Polish formatting
- Make certain typeface, margins and spacing.
- Look at the citations and bibliography webpage.
- Make certain that every single section is integrated in the perfect place: headline page, describe, bibliography and appendices.
Submit your pieces of paper
- Publish your document.
- Be performed with it for a time.
- Chill out. Look.


text:
DWNTWN Arts Shuttle
Tuesday 12/4 until Sunday 12/9: 11am-11pm
In order to allow easy access to a variety of Art Events taking place during Art Basel Miami Beach, the City of Miami will operate a special trolley route. The cars will be connecting Midtown, Wynwood, Downtown Miami and through a secondary loop Miami Beach. Residents and visitors are encouraged to take advantage of this free system an leave your parking and traffic worries at home!


text:
Re: Change headers in outgoing email
Expand Messages
- Ali Jawad skrev den 12-12-2012 21:35:
> Thank you for the prompt reply, basically I want it to showwhy ?
> information other than what is in the hosts file,
> I do have postfix with 7 instances and I want each instance todo you have 7 wan-ips on this server ?
> show it's own IP, instead it is showing the hostname in /etc/hosts
if not, forget it :)
each instanse will use hostname it binds to, so all wan hostname must
be defined in /etc/hosts, and overwride in master.cf is still possible
with one instance, this works for incomming, but was the question for
outgoing smtp ?
more then one instance is only needed for outgoing
PS: no need to send cc to me privately aslong i am still here
Your message has been successfully submitted and would be delivered to recipients shortly.


text: Guests of this Oklahoma City hotel will enjoy many amenities, including free continental breakfast, free local calls, free weekday newspaper and free coffee. Guests will also enjoy the exercise room, indoor pool and hot tub. Business travelers will appreciate modern conveniences like high-speed Internet access, data ports and access to copy and fax services. All spacious guest rooms feature large showers and cable television. Handicap accessible and non-smoking rooms are available. Warm, attentive hospitality and clean, relaxing rooms are waiting for you at the Sleep Inn hotel in Oklahoma City. We look forward to seeing you soon.


text:
- Age / Gender:
- 27, Unspecified
- Location:
- Europe
- Joined:
- 5/24/15
- All Stats >
I don't like telling strangers on the internet things about me.
User Statshot
- Community Stats
Level 2 Blank Slate
Normal Whistle
Ranked as Civilian
Contact Info / Websites
Recent Game Medals
Chocolate Man & Cripple Boy 5 Points from Nameless Medal Stats.
Kenstar 5 Points from Girlchan in Paradise Medal Stats.
Detective Nwar 5 Points from NWAR Medal Stats.
ZONE-tan 5 Points from ZTV News Medal Stats.
Leo & Satan 5 Points from Leo & Satan Medal Stats.
Edd Gould 5 Points from Eddsworld Medal Stats.
Time Fcuk 5 Points from Time Fcuk Medal Stats.
Larry 5 Points from LARRY Medal Stats.
Pico 5 Points from Pico's School Medal Stats.
Alien Hominid 5 Points from Alien Hominid Medal Stats.
Total Medals Earned: 26 (From 3 different games.)
Latest Audio Submissions
|Fire Emblem: Fates' Theme Reochestrated
|Cinematic Song
|YGS theme orchestrated
|Cinematic Song
|Kite (Orchestrated)
|Cinematic Song
|Epic Boss Theme (Possibly Japanese)
|Video Game Loop
Latest Favorite Audio
|Snow Fight - Snowman´s Tale
|Cinematic Song
|Uberquest Main Theme
|Cinematic Song
|Solitude of the Valkyrie ~P~
|Classical Loop
|Broken Dreams
|Cinematic Song


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Well played, Apple
April 20th, 2010
Only one thing is definitely true about the headline-grabbing melodrama going on over at Gizmodo: it's all good news for Apple. It doesn't even matter whether the alleged iPhone 4.0 prototype really was left in a bar by an overserved young Apple engineer — they've garnered a huge amount of attention throughout an entire news cycle, without actually revealing anything definitive (only a new piece of hardware that may or may not be the actual iPhone 4-HD-whatever), while sowing just enough doubt to keep some of the wavering faithful from leaving the fold for one of the sexy new Android phones like the Droid Incredible (arguably worth a few lost sales between now and June). And there's still plenty left to reveal in June, including the actual screen resolution, front-facing camera (or not), and whatever else Gizmodo didn't uncover. And they get valuable early feedback on the design — whether or not they could or would ever tweak it based on such feedback — while still being able to claim it's not the final unit. And, hell, they could probably still even sue Gizmodo if they wanted, if the whole thing started to look too perfect.
Fake? Leaked? Stolen? Red herring? It doesn't even matter. Apple wins.


text:
Plant Moisture
Skye have been manufacturing plant moisture systems since 1987. These instruments are used to study the demand for water in plants by measuring the plant water potential of stress. The plant moisture potential is measured using a Scholander type pressure chamber and Skye offer two basic types – analog or digital – with choices of pressure range within each type.
Digital System
Analog System
Measures plant moisture potential directly using either the digital or low cost analog plant moisture systems from Skye Instruments.
Common features include a high quality design with user safety having the highest priority. The large sample chamber is machined from a single solid piece of high grade brass. The chamber heads have a unique multi-start thread giving easy tightening. The water potential pressure of the plant sample is read off the digital display when moisture is first seen coming from the freshly cut stem. For ease of use the HOLD feature allows the display to be frozen at the measurement point, enabling the user to concentrate on the specimen and not on the display. The system is highly portable. It can be used with small scuba diving gas bottles in the field, or with standard large gas cylinders in the laboratory. This design also ensures that the chamber can never be pressurized unless the head is secure. Each system is individually tested and certified by an independent test house, to twice its working pressure. A clear perspex shield and safety eye spectacles are provided with each system.
Product Features:
- Measures plant moisture potential directly
- Analog or digital pressure readout – large clear figures with 'hold' control
- Large chamber to accommodate various structures
- Choice of chamber heads suitable for fleshy or woody samples
- Can also be used for sap extraction
- Models available for 40, 50 and 80 bar maximum working pressure
- Available with accessories for use with nitrogen/air lines or regulators to suit 'scuba' diving air bottles


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shortyx67 Review
Love my new crystal chandelier! We recently put this up in our freshly remodeled guest bathroom and it adds the perfect amount of glamour to the space. Everyone that sees it, instantly compliments how beautiful it is. I definitely would recommend this for anyone wanting to add some sparkle to a bathroom or hallway. If you are interested in seeing more pictures of it, see it on my blog here: http://346living.com/2012/07/05/the-bathroom-reveal-2/
- How did the color on site compare with the actual product? same
- Please tell us about the quality of the product. very nice quality
- How accurate was the on site description of the product? pretty good, a better photo would be nice
Was This Review Helpful?


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BLACKSBURG, Va. — Almost nine months after having major knee surgery, Virginia Tech cornerback Antone Exum barely pondered the question before responding.
The hardest part of ordeal? It happened Jan. 26, during the hours immediately after he lay in pain on the basketball court at McComas Hall on campus, his right knee ripped apart and his future suddenly uncertain.
"That," Exum said, "was a bad day."
Saturday should be decidedly better as Exum, a second-team all-ACC selection in 2012, is set to make his season debut when No. 16 Virginia Tech takes on Duke at Lane Stadium. He missed the first seven games of the year recovering from offseason surgery to repair a torn anterior cruciate ligament, lateral and medial meniscus and a bone fracture in his right knee.
But while the first few hours turned out to be the worst, the past few weeks became the most tedious.
After months of establishing aggressive timetables for his return, he was cleared to play by doctors Sept. 30. But Exum then chose to sit out two additional games, the sort of cautious approach that caught many by surprise.
"He's come a long way in a short amount of time. He was driven," said Virginia Tech athletic trainer Keith Doolan, who oversaw Exum's rehabilitation this fall. "But he was hungry, hungry, hungry, and now it's like, 'Where is he?' "
Exum's original plan, which he groggily announced to orthopedist James Andrews in a post-surgery fog in February, was to be back in time for Virginia Tech's season opener against No. 1 Alabama. That fell by the wayside early in August when a follow-up appointment with Andrews revealed he wouldn't be back until at least the start of ACC play.
Anxious to return before that once the regular season began, Exum soon hinted on Virginia Tech's weekly radio show there was a chance he could be back in time for the Hokies' game against Marshall on Sept. 21. The next day, head athletic trainer Mike Goforth said such an arrangement was "news to him."
Finally, three days after the Hokies began ACC play with a 17-10 win at Georgia Tech, Exum went to Pensacola, Fla., for testing, and Andrews finally cleared him to play. Exum confirmed the news on his Twitter account, declaring that "THE DAYCARE IS NOW OPEN!"
But in the week of practice that followed, Exum's mind-set changed. He watched the film and didn't see the same player who led the ACC in pass break-ups a year ago and finished with a team-high five interceptions. As defensive backs coach Torrian Gray put it: "Seeing him move around, the first week you were really concerned. Two weeks ago, he was still, we didn't think there was any way he could play."
Before Virginia Tech faced North Carolina on Oct. 5, Exum felt compelled to release a statement explaining why he would not play that week despite being cleared. He admits now he even packed his travel bag in anticipation of being in the lineup for the Hokies' past two games against the Tar Heels and Pittsburgh.
"I just thought about it a little bit more and thought I was making a decision that was based on the excitement of getting cleared. I kind of was rushing things and running into things. It took me a little while to get over that," said Exum, who consulted with his family and coaches.
"I wouldn't say it was a change as far as how hard I was approaching the situation of coming back or how hard I was working, but it may have been a change in my attitude as far as I need to definitely wait until I'm 100 percent or can play at a very high level before I step back out there."
Exum's future also played into his decision-making. The fifth-year senior nearly declared for the NFL draft after last season before returning to school. But the emergence of freshman cornerbacks Brandon Facyson and Kendall Fuller ensured the Hokies didn't need Exum to hurry back.
"He's smart enough to know he can't get out there and not be 100 percent healthy. It doesn't help him, and it doesn't help the team," Gray said.
Exum remains guarded about the parts of his game that are still an issue heading into Saturday's game, although he's quick to point out "the huge strides" made since he returned to practice in full. Coach Frank Beamer said "he looked like the old Antone" during special teams drills this past week.
Exum's return is well timed, as Facyson is listed as doubtful for Saturday's game because of a concussion suffered in practice last Sunday and senior cornerback Kyle Fuller is questionable because of a groin injury.
Even so, Gray planned to give Exum the start all along, well aware it's a moment that seemed so uncertain just nine months ago.
"I think about it all the time," Exum said. "I don't really know what to expect, except me playing at a high level."