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Rules are widely used in everyday life to organize actions and thoughts in accordance with our internal goals. At the simplest level, single rules can be used to link individual sensory stimuli to their appropriate responses. However, most tasks are more complex and require the concurrent application of multiple rules. Experiments on humans and monkeys have shown the involvement of a frontoparietal network in rule representation. Yet, a fundamental issue still needs to be clarified: Is the neural representation of multiple rules compositional, that is, built on the neural representation of their simple constituent rules? Subjects were asked to remember and apply either simple or compound rules. Multivariate decoding analyses were applied to functional magnetic resonance imaging data. Both ventrolateral frontal and lateral parietal cortex were involved in compound representation. Most importantly, we were able to decode the compound rules by training classifiers only on the simple rules they were composed of. This shows that the code used to store rule information in prefrontal cortex is compositional. Compositional coding in rule representation suggests that it might be possible to decode other complex action plans by learning the neural patterns of the known composing elements.
A major challenge for human cognition is to successfully pursue relevant goals in a continuously changing environment. This implies the ability to select and implement the rules allowing to appropriately deal with the current situation (Passingham 1993; Shallice and Burgess 1996; Wise and Murray 2000; Duncan 2001; Miller and Cohen 2001; Bunge and Wallis 2007). For example, people, at a dinner party, can easily select and implement a simple rule, such as "if Jake prepared pizza, I should not eat it." Humans, however, can also successfully manage the situations in which set of rules more complex than that are required. A common occurrence is the concurrent application of more than one simple rule so that multiple rules need to be combined to form "compound rules." Even in the case of the dinner party, it might be not only important to know that you should avoid Jake's pizza but also that you should definitively try to get Ann's cake twice.
In recent years, several studies have been devoted to the understanding of which neural structures are involved in representation of rules. Neurophysiological studies on monkeys have shown neurons representing active rules in a wide frontoparietal network including prefrontal cortex, orbitofrontal cortex, striatum, and parietal cortex (Hoshi et al. 1998; White and Wise 1999; Asaad et al. 2000; Wallis et al. 2001; Wallis and Miller 2003; Stoet and Snyder 2004; Genovesio et al. 2005; Muhammad et al. 2006). Neuroimaging studies on humans emphasized the role of both the ventrolateral prefrontal cortex and the superior parietal cortex in rule representation and implementation (Sakai and Passingham 2003, 2006; Bunge et al. 2003; Cavina-Pratesi et al. 2006; Reverberi et al. 2007; Bengtsson et al. 2009; Bode and Haynes 2009; Woolgar et al. 2011; Reverberi et al. 2010).
Despite the evidence already available, it has still remained unclear how the human brain encodes rules consisting of multiple simple rules in working memory. Here, we aimed to investigate how the human brain represents such compound rule sets (Fig. 1). Most importantly, we were interested whether the encoding of rules in the human brain follows the principle of "compositionality": Can the neural code for rules consisting of multiple simple rules be decomposed into the neural representation of the constituent simple rules? For example, is the neural pattern representing a rule composed by 2 simple rules such as 1) "if Jake's pizza then do not eat" and 2) "if Ann's cake eat twice" (the rule set for "parties at Jake's") equal to the combination of the neural patterns of the 2 simple component rules taken alone? Or does the brain use a new independent representation for the compound rules as studies on monkeys seem to suggest (Warden and Miller 2007, 2010; Sigala et al. 2008)? Answering these questions would provide key evidence on how different brain areas encode more complex sets of instructions and on how this impact our overt behavior. Importantly, any evidence for compositionality would provide an explanation for how the brain codes the vast number of different rules required for everyday behavior. On this point, however, it is important to realize that the ways in which 2 or more simple rules can be combined together in a compound rule exceed the one we considered in the present study (e.g., Koechlin et al. 2003; Badre et al. 2009). For example, one might be required to apply the 2 simple rules composing a compound rule in a specific order (first Rule 2 and then Rule 1) or to apply them depending on the status of another, hierarchically higher, layer of rules. Whether the brain uses a compositional code in all these cases was not directly tested in the present study, which represent a first step in that direction. Nevertheless, a principle of parsimony would suggest considering the evidence provided on the use of a compositional code as applicable to other composing rules.
The precise format of neural representations in the human brain has been difficult to address due to limitations in neuroimaging methodology. Most conventional brain imaging techniques analyze activity averaged or smoothed across extended regions of cortex. This has lead to an "activation-based view" of the human brain, where different modules are differentially involved in different processes, independent of the specific contents being processed. In contrast, the complementary "representational view" of the human brain attempts to identify the nature of neural representations of different types of items (e.g., visual stimuli, rules). Several experiments suggest that most neural representations follow a distributed code that is based on the specific profile of conjoint activation of a larger population of neurons (Schoenbaum and Eichenbaum 1995; van Duuren et al. 2008, 2009; Kahnt et al. 2010). It has recently emerged that functional magnetic resonance imaging (fMRI) can to some degree be used to access information coded in such distributed populations, possibly even when they are encoded at a finer scale than the resolution of the measurement grid (Passingham 1993; Kamitani and Tong 2005; Haynes and Rees 2006; Norman et al. 2006; Swisher et al. 2007; Pereira et al. 2009; Kamitani and Sawahata 2010; Kriegeskorte et al. 2010; Op de Beeck 2010, but see also Op de Beeck 2010). Multivariate pattern recognition has been successfully used to extract information from fine-grained multivoxel patterns in fMRI data. For example, it has been shown that it is possible to decode low-level visual features (Haynes and Rees 2005; Kamitani and Tong 2005) but also more abstract high-level representations, such as future intentions (Haynes and Rees 2005; Haynes et al. 2007) or concepts (Polyn et al. 2005; Mitchell et al. 2008) from the fine-grained spatial activity patterns in human cortex.
In the present experiment, we used fMRI in combination with multivariate pattern recognition. Specifically, we assessed whether the neural representation of compound rules can be predicted from the neural representation of their simple constituent rules. For this, our paradigm used 2 types of rule sets, "simple" and "compound." The compound rule sets are formed by adding 2 simple rules (Fig. 2). If the brain uses a compositional code in representing compound rule sets, it should be possible to use the relevant simple rules to adjudicate which of 2 alternative compound rules is currently represented.
Materials and Methods
Participants and Experimental Procedure
Thirteen healthy subjects (average age 27 years; 9 males) participated in the experiment. All participants gave written informed consent. The study was approved by the local ethics committee. All participants were right-handed, had normal or corrected to normal vision, no neurological history, and no structural brain abnormalities. Our task required subjects to retrieve, maintain, and then apply a set of conditional rules to a series of target stimuli (Fig. 2). The cues and their associated rules had been learned by the subjects in separate training sessions (see below). The assignment of cues was randomized across subjects, so that each subject had relied on different cue-rule associations. At the beginning of each trial, a cue was presented for 500 ms at the center of the screen. The cue informed the subjects of the rule for the current trial. A delay of variable duration (3500–9500 ms) followed the cue presentation. Finally, 2 target images were presented for 750 ms. Subjects had to apply the current rules to the target stimuli and derive the correct response as fast as possible. Subjects were informed that they had to respond before the targets disappeared. The short time available during target presentation forced the subjects to retrieve the relevant rule set as soon as the cue was presented. Six rule sets belonging to 2 different classes were used (Fig. 2, Supplementary Fig. S1). "Single rules" consisted of a unique conditional rule linking one visual stimulus (house or face) to one specific action (left or right button press). Compound rules consisted of a "pair of single rules" to be applied at the same time. Four simple rules (S1–S4) and 2 compound rules (C1–C2) were used, that is, S1: "If there is a house press left"; S2: "If there is a face press right"; S3: "If there is a face press left"; and S4: "If there is a house press right." The 2 compound rules were C1: S1 + S2, C2: S3 + S4. During scanning, subjects performed 288 trials divided into 6 runs. In each run, 48 trials, 8 per each rule set, were administered in random order. Twelve cues coding for 6 different rules were used, so that 2 visually unrelated cues corresponded to the same rule. As targets, we used gray-scale images of faces (F), houses (H), or unrelated objects (O) presented in pairs (Kriegeskorte et al. 2003). Six target pairs were considered: FO, FH, HO, HF, OF, and OH (the first letter refers to the target image presented on the left of the screen). The distribution of target pairs was equal across rule sets so that in each run, each target pair was presented once for every single rule set and twice for every compound rule set. Given the type of rules and targets allowed in the task, 3 responses could be appropriate: left, right, or no button press. At most one answer had to be produced: In case both left and right button presses were compatible with the target and the active rule, subjects could choose any of the 2.
Training was delivered in 2 separate sessions lasting about 1 h each. In the first session, subjects learnt the correspondence between the 12 cues and the 6 rules. Subjects were first instructed to read and learn a first set of 6 cues and the corresponding rules. Following this, a testing procedure started: subjects were presented with a cue and, after a delay of 3 s, required to "write" the corresponding rule. Writing was performed by clicking with the mouse on the relevant basic components of the target rules, presented on a computer screen along with distractors. The basic components were for example: "If there is a house," or "then press right," or "If there is a face." Feedback was provided in case of incorrect answers. This training phase ended when the subjects reached 100% accuracy over the last 15 trials they performed. After having completed this training phase, a second set of 6 cues was presented for learning. In the following testing procedure, the whole cue set (12 cues) was checked. Again, this training phase ended when the subject reached perfect accuracy over the last 15 trials. In the second session, the training procedure was similar to the procedure used for the fMRI experiment. Two notable differences were present. First, feedback was delivered on incorrect trials. Second, the delay between cue presentation and target presentation was long at the beginning (6.5 s), but it progressively reduced following the improvement of the performance. The training procedure stopped when subject attained 100% accuracy in the last 15 trials with a short delay (1.5 s). The whole training procedure assured that subjects would perform the task during fMRI scanning with high proficiency.
Image Acquisition
Functional imaging was conducted on a 3-T Siemens Trio (Erlangen, Germany) scanner equipped with a 12-channel head coil. In each of the 6 scanning sessions, 305 -weighted gradient-echo echo-planar images (EPI) containing 32 slices (3 mm thick) separated by a gap of 0.75 mm were acquired. Imaging parameters were as follows: repetition time 2000 ms, echo time 30 ms, flip angle 90°, matrix size 64 × 64, and a field of view of 192 mm, thus yielding an in-plane voxel resolution of 3 mm2, resulting in a voxel size of 3 by 3 by 3.75 mm.
Data Analysis
Functional data were analyzed using SPM8. Preprocessing included rigid-body transformation (realignment) and slice timing. Normalization was applied after completion of the decoding analyses.
Analysis 1: Decoding Visual Cues
A finite impulse response (FIR) model was applied to the realigned and slice-timed images (Henson 2004). Each condition was modeled using 16 time bins of 2 s. Four conditions were modeled in this analysis: They corresponded to the 4 different cues coding for the 2 compound rules. Only correct trials were used for the estimation of the parameters. The time of onset was the presentation of the cue. A decoding analysis using a "searchlight" approach was then applied to the first 4 time bins (Kriegeskorte et al. 2006). In this way, we could estimate in which time bin the information about the cue was first available (Bode and Haynes 2009). The following procedure was repeated for each time bin (Supplementary Fig. S2): A spherical cluster centered on a voxel vi and with a radius of 4 voxels was defined. The FIR parameters for the voxels included in the sphere vi were then extracted. They were then transformed into n-dimensional pattern vectors for each condition and for each run. Twenty-four vectors (6 runs × 4 conditions) were available for each subject. These vectors represented the spatial response patterns to the 4 conditions from the cluster of voxels vi. These pattern vectors were divided into 2 sets. Two independent decoding analyses were applied to each vector set. Each set contained 12 vectors, corresponding to 2 of the 4 original conditions. The 12 vectors in one set belonged from trials on which the same compound rule was active: Six vectors corresponded to a cue and 6 vectors corresponded to the other cue introducing the same compound rule. A linear support vector pattern classifier (Muller et al. 2001) with a fixed regularization parameter C = 1 was used. In each set, pattern classifiers were trained to distinguish between pattern vectors coding for the 2 alternative cues. A 6-fold cross-validation procedure was implemented to assess the performance of the 2 classifiers. Each classifier was trained on pattern vectors of 5 of 6 fMRI runs to identify patterns corresponding to each cue. The classifier was then tested on the data from the fMRI run not used for training. This procedure was repeated 6 times, with each run acting as the independent test data set once. The average accuracy of all 6 iterations was assigned to the central voxel of the cluster vi. Classification accuracy significantly above chance implied that the local cluster of voxels spatially encoded cue information. By repeating this procedure for every voxel in the brain, a 3D map of decoding accuracies for each position could be created. The same procedure was independently applied to each time bin. The accuracy maps were normalized using the parameters estimated during preprocessing. The accuracy maps were then submitted to a second-level analysis in SPM8, to test whether the decoding accuracy for voxel differed from chance level (50%) across all subjects at group level. In particular, we subtracted 0.5 to each accuracy map so that the chance level (50%) was represented in the modified maps by zero. The modified accuracy maps for each of the 4 time bins were entered in an analysis of variance (ANOVA) analysis in SPM8. Any significant positive deviation from 0 was interpreted as showing the presence of information in the voxel(s). First, each time bin was tested independently in order to identify in which time bin the information about the cue was first available. The information was already available in occipital cortex starting from the second time bin (Only for exploratory purposes, we performed also a time-resolved analysis for decoding of compound rules. If a relatively liberal statistical threshold is allowed (P < 0.005), information is already present in lateral frontal cortex from the second time bin, as it is the case for cue decoding. Presumably, the temporal resolution of our experiment was not sensitive enough to reveal the short delays (about 1 s) between the cue presentation and the rule representation). Such starting point was then used to define the relevant time window for the analyses devoted to assess where in the brain the information is available. Thus, for all the following analyses, we considered the time window between the second and the fourth time bin, starting from cue presentation. This time window would effectively cover the cue phase and the delay phase. On the contrary, it should effectively discard activity related to target presentation. The target appeared at variable delays from the cue presentation: Only in trials with the shortest delay (one-third of the cases), the target could have been included in the time window we considered. The combined effect of the substantial jitter in delay duration and the presence of target processing in only 1 of 3 trials considered should have effectively prevented the target-related activations to contribute to the estimation of the used FIR time bins. We considered the main effect of accuracy as significantly different from chance at P < 0.05, corrected for multiple comparisons at cluster level.
Analysis 2: Decoding Compound Rules
The analysis of compound rules largely followed the first analysis, the main difference consisting on how the same pattern vectors were arranged in the training and testing set. Multivariate pattern classification was used to assess whether information about the compound rules was encoded in the spatial response patterns. A pattern classifier was trained on 2 of 4 conditions. Each of the 2 training conditions corresponded to 1 of the 2 alternative compound rules and associated cues. The classifier was then applied to an independent test data set comprising the 2 conditions not included in the training data set. Each of the 2 conditions of the test data set corresponded to 1 of the 2 alternative compound rules, just as in the training data set. Critically, however, the cues associated with the compound rules in the test data set were different from the cues in the training data set. In this way, it was possible to identify patterns of brain activation encoding "specifically" for the "meaning" of the cue—that is, for the compound rules—and not for the cue visual features. This procedure was repeated 4 times for all the possible combinations of training and testing sets. All relevant time bins (i.e., from the second to the fourth time bin) were considered in the same decoding analysis. This means that all time bins from 2 to 4 belonging from the same condition were considered as equivalent instances of the same class. This is a plausible assumption given that the same representation (i.e., the specific rule active in the trial) needs to be maintained during the whole time window (Bengtsson et al. 2009). As in the preceding analysis, the 4 accuracy maps per subject were submitted to a second-level ANOVA in SPM8, to test whether the decoding accuracy differed from the chance level (50%) at group level.
Analysis 3: Decoding Compound Rules from Simple Rules—Compositionality Analysis
Thirty-six pattern vectors for each time bin were extracted from the FIR parameter estimates corresponding to the 6 conditions (4 single and 2 compound rules) and the 6 fMRI runs. Four related multivariate analyses were run, using 4 different pairs of training data sets. Namely, the training set of the first multivariate analysis comprised pattern vectors belonging to the simple rules (Fig. 2): S1 and S3; in the second multivariate analysis, we used the pattern vectors belonging to S1 and S4; in the third, S2 and S3; and finally in the fourth, S2 and S4. For all 4 multivariate analyses, the pattern vectors of the 2 compound rules were used as test data set. In this way, 4 accuracy maps for each subject could be obtained. These accuracy maps estimated how well the information contained in the vector patterns of single rules allowed to classify pattern vectors of the compound rules. As in the preceding decoding analysis, all relevant time bins were considered in the same decoding analysis. The normalized accuracy maps were finally submitted to a second-level analysis to test whether the decoding accuracy for each voxel significantly differed from chance level (50%).
During scanning, subjects applied the relevant rules to the target with a high accuracy. The average proportion of correct responses was 94% (standard deviation [SD] = 3.3%). Subjects were also very fast in generating the responses: The average reaction time from target appearance was 505 ms (SD = 35 ms). Such short reaction times, combined with the high accuracy, confirm that subjects represented the relevant rule sets during the delay before target presentation. Overall, we performed 3 main sets of decoding analyses, 2 of them exploring the neural representation of cues and rules and the last addressing the neural code of rule representation. Three accessory analyses were also performed to further specify our main findings and to constrain their interpretation.
First, we investigated that brain regions represent the cues themselves irrespectively of their meaning (Fig. 3 and Supplementary Table S1). Multivariate pattern classifiers were trained and tested on instances of cues that had the same meaning. In this way, only the visual features specific to each individual cue would be the source of a successful classification. We were able to decode cue identity from the left inferior occipital gyrus (Brodmann Area [BA] 18, P < 0.05 corrected, peak accuracy 60%) and from the right middle occipital gyrus (BA 19, P < 0.05 corrected, peak accuracy 60%). Furthermore, it was possible to decode cue identity from a region in the left superior parietal lobe (BA 7, P < 0.05 corrected, peak accuracy 60%).
Next, we investigated where in the brain the active compound rule is represented. To identify brain areas specifically coding for the rule, it is necessary to control for other correlated dimensions of both the stimuli and the task. First of all, it is important to disentangle between the cue (e.g., its visual features) and its meaning (i.e., the rule). Second, it is necessary to disentangle the representation of the rule linking between stimulus and action from the representation of stimuli and actions alone. The experimental paradigm and the implemented decoding analysis were devised to comply with the above requirements. First, each of the rules could be instructed by 1 of 2 visually different cues. In this way, we could train the pattern classifiers on rule instances that were introduced by 1 of the 2 cues with the same meaning, and then test on other instances of the same rules, but introduced by the alternative different cue (Supplementary Figs. S1 and S2). Second, for this analysis, we considered compound rules only. Either compound rule is constituted by the same basic elements. In both compound rules right and left index presses, houses and faces are involved. The only feature changing between the 2 compound rules is the rule linking the basic elements (Fig. 2). This also assures that the same cognitive resources (e.g., working memory) are needed to represent the 2 compound rules. We were able to decode the identity of the active compound rule from local activation patterns in the left parietal lobe (BA 7/40, P < 0.05 corrected, peak accuracy 59%) and in the right inferior frontal lobe (mainly BA 47, P < 0.05 corrected, peak accuracy 57%). There was a partial overlap of the brain areas representing the superficial features of the cues and the brain areas representing the rules in the left parietal lobe, with the area related to rule representation peaking more laterally than the area related to cue representation (Supplementary Fig. S3). In contrast, cue identity information could not be detected in the right lateral frontal cortex even allowing very liberal statistical threshold (P < 0.01, uncorrected for multiple comparisons).
The preceding analyses showed that areas in the right frontal and left parietal represent compound rules. However, which code do those brain areas use to represent compound rules? Do they use a "compositional code" (Fig. 1) so that the neural coding of a conditional rule such as "if there is a face press left" is the same when the rule is represented alone (simple rule) and when the same rule is part of a wider rule set (compound rule)? We trained multivariate pattern classifiers on pairs of simple rules, each of which was always part of a different compound rule. The performance of the classifiers trained on simple rules was then tested on the activation patterns of the 2 compound rules. Decoding of compound rules by information extracted from single rules was only possible in the right lateral inferior frontal cortex (mainly BA 47 and 46, P < 0.05 corrected, peak accuracy 56%). This is the same lateral frontal area that was shown to be informative in the preceding "independent" analysis on rule decoding. The overlap between the 2 is large: namely, 60 of a total of 104 voxels in the compositionality analysis overlap with those in the rule analysis (Supplementary Fig. S3). By contrast, it was not possible to detect rule information with the compositionality procedure in the left parietal region even allowing very liberal statistical threshold (P < 0.01, uncorrected).
If a compositional code is used in right lateral prefrontal cortex for representing single and compound rules, it should be possible not only to decode compound rules from single rules but also to decode single rules from activity patterns estimated on compound rules. We refer to this analysis as the "inverse compositionality analysis" to distinguish it from the main compositionality analysis described above. Convergence of findings between the indirect and the main compositionality analysis would further corroborate the claim of compositionality of the neural code. We trained a multivariate pattern classifier on the pair of compound rules available (i.e., C1 vs. C2). The classifier was then tested on the activation patterns of the 4 relevant pairs of single rules, namely S1 versus S3, S1 versus S4, S2 versus S3, and S2 versus S4. Decoding of single rules by information extracted from compound rules was possible in the right lateral inferior frontal cortex (P < 0.05 corrected, peak accuracy 55%, Fig. 4). This is the same region found in the main analyses on compositionality and on compound rule decoding. No brain region showed a difference in accuracy when the indirect and the main compositionality decoding performance were compared.
To further assess the relative performance of the 3 main decoding analyses considered in this study (cue, rule and compositionality), we ran a "region of interest (ROI) analysis" (Fig. 5). We considered as ROI the 2 brain areas, inferior right frontal and left parietal, which were involved in rule representation (Fig. 3). We then computed, per each subject, the average accuracy attained by the 3 classifiers across all voxels in the ROI. Those values were then submitted to a one-sample t-test analysis. Notice that the selection criteria of the ROI, based uniquely on the rule representation analysis, are independent from the compositionality and cue representation analyses. Thus, no risks of circular analyses are introduced when only the latter are considered. The findings in the ROI analysis confirm the whole-brain analyses (Fig. 5). In particular, information on cue identity is represented in the parietal lobe but not in the frontal lobe (parietal: t12 = 1.95, P = 0.038; frontal: t12 = −0.948, P = 0.82). The opposite is true for the compositionality analysis (parietal: t12 = 0.47, P = 0.32; frontal: t12 = 3.29, P = 0.003). The interaction information-type (cue vs. composition) × brain area (parietal vs. frontal) was significant (repeated measures ANOVA: F1,12 = 7.24, P = 0.02), meaning that the information represented in the 2 areas was different.
In the main compositionality analysis, we showed that it was possible to decode compound rules by information extracted from single rules in the right lateral inferior frontal cortex. We interpreted this finding as showing that the right lateral inferior frontal cortex uses a compositional code to represent compound rules. However, an alternative hypothesis could explain this finding. During training, the subjects might have created strong associative links between the simple rules and the compound rules in which these are embedded, so that whenever a simple rule is represented, the associated compound rule is automatically retrieved and represented as well. For example, when subjects are required to represent the simple rule 1 (i.e., S1, see Fig. 2), they not only do that but they also automatically represent the related compound rule 1 (C1). This would explain our findings without assuming the compositionality of the neural code. Decoding in right lateral inferior frontal cortex, which is also involved in compound rule representation, would be possible because subjects represent compound rules even when they are only required to represent simple rules.
We devised an accessory analysis to assess this hypothesis. We chose an approach that makes opposite predictions depending on whether the decoding uses information about simple rules or about associated compound rules. To understand this, take for example the following classifier:
TRAINING: S1 (house, left) versus S4 (house, right).
When applying this classifier to the remaining 2 simple rules:
TEST: S3 (face, left) versus S2 (face, right).
Two different outcomes are possible:
The classifier learns a fact about the 2 simple rules that is the different motor response between S1 and S4 and classifies S3 and S2 according to their motor responses. In that case, it would classify S3 like S1 and S2 like S4.
The classifier learns the compound rules associated with each of the simple rules (C1 for S1 and C2 for S4) and classifies S3 and S2 according to the compound rules associated with them. In that case it would classify S3 like S4 and S2 like S1.
Please note that the 2 predictions are exactly opposite. Given how we implemented the decoding analysis, if the classifier learns information on simple rules, this would lead to a performance significantly higher than chance (i.e., >50%). By contrast, if the classifier learns information on the associated compound rules, then the performance should be significantly lower than chance (i.e., <50%). There are in total 4 different combinations of simple rules that can be tested this way (train S1–S4, test S3–S2; train S3–S2, test S1–S4; train S1–S3, test S2–S4; train S2–S4, test S1–S3). The Figure 6 shows the average performance of the above-mentioned analyses. No brain region showed a performance of the classifiers reliably lower than chance. On the contrary, we found higher than chance accuracy in several brain regions, including the right lateral prefrontal region, which we already observed in the compositionality analysis. Overall, this analysis does not support the hypothesis that the compositionality analysis reflects the automatic representation of compound rules associated to simple rules.
In this study, we used fMRI in combination with multivariate pattern recognition to understand where and, most importantly, how the brain represents and organizes "multiple rules" active at the same time in working memory. Specifically, we were interested whether the encoding of rules in the human brain follows the principle of compositionality (Fig. 1). We found that both lateral parietal and inferior lateral frontal cortex are involved in representing compound rules. However, the 2 areas use a different neural code to represent the same information: while lateral parietal cortex codes simple and compound rules by using an independent coding, inferolateral prefrontal cortex uses a compositional code.
Representation of Compound Rules: Location
Two brain areas were found to store the information on the currently active compound rules: the inferolateral prefrontal cortex and the lateral parietal cortex. The information on compound rules could not be attributed to irrelevant low-level features of the stimuli or to different cognitive resources (e.g., working memory) involved for representation. Instead, the information was specifically related to the core meaning of the rule: the representation of the link between a triggering condition (e.g., seeing a house) to its consequences (e.g., pressing the left button).
Overall, our findings are compatible with and further specify the growing evidence on the representation of task sets and rules. Studies on monkeys have shown that, besides other brain structures, both prefrontal cortex and lateral parietal cortex are involved in the representation of the current task (Asaad et al. 2000; Hoshi et al. 2000; Bussey et al. 2001; Wallis et al. 2001; Wallis and Miller 2003; Stoet and Snyder 2004, 2009; Genovesio et al. 2005; Buckley et al. 2009; White and Wise 1999). Neuroimaging evidence on humans confirmed that both ventrolateral prefrontal cortex and lateral parietal cortex were active while maintaining rules in working memory (Bunge et al. 2003; Sakai and Passingham 2003, 2006; Rowe et al. 2007; Bengtsson et al. 2009), particularly when rules as complex as compound rules are considered (Bunge and Zelazo 2006). Compared with evidence on the monkey brain, our study showed that the brain areas involved in the temporary representation of rules are more confined in humans. This might be because we did not compare the representations of tasks, which involve different cognitive operations (e.g., a spatial task vs. an object matching task). Instead, our study focused on the representation of 2 alternative compound rules involving exactly the same cognitive operations (house and face recognition, left and right button presses, and rule processing). The only difference between the examined conditions is how the same cognitive operations are organized or connected by arbitrary rules. Previous neuroimaging studies are compatible with the findings presented here (Bunge et al. 2003; Sakai and Passingham 2003, 2006; Bunge and Zelazo 2006; Rowe et al. 2007; Bengtsson et al. 2009). Our study goes beyond this previous work by examining whether brain regions involved in rule processing encode information about the "specific" rules that are currently active. A recent study (Bode and Haynes 2009) filled this gap by applying multivariate decoding techniques. However, in that study, it was not possible to determine whether the information about the active task was due to the representation of the cue or the rules with which the cues were associated. Here, we solved the ambiguity by showing that both the lateral parietal and ventrolateral frontal areas encode information on the active rule that cannot be explained by the concurrent encoding of the visual features of the associated cues.
Our findings also test one of the major tenets of representation theories on prefrontal lobe function (Dehaene et al. 1998; Duncan 2001; Miller and Cohen 2001). These theories hold that prefrontal cortex acts as a temporary store only for those facts that are "relevant" for the course of thinking or action, in this way biasing the activity of lower level brain areas. Following this hypothesis, prefrontal cortex should represent which rule is active but not which specific cue was presented, that is, decoding should be at chance for low-level visual features. This is indeed the pattern we found in inferior lateral frontal cortex. This is in agreement with a previous study using multivariate analysis techniques applied to a visual categorization task showing that lateral prefrontal cortex flexibly changes the information represented following task needs (Li et al. 2007). Furthermore, it has been claimed that also lateral parietal cortex is part of an extended network of brain areas supporting the temporary storage of relevant information (Dehaene et al. 1998; Duncan 2001; Assad 2003). Interestingly, however, the lateral parietal cortex stored both relevant (the rule) and irrelevant (the cue identity) information. Is parietal cortex a nonspecific store bound to maintain any kind of information, relevant or not? Not necessarily. Cue identity is actually relevant for an accessory, but critical, task: the assignment of the meaning to an identified cue. One might speculate that the parietal cortex is not involved in the main task but only in the enabling accessory task of translating the visual cues into their meaning. This would explain why both cues and rules are represented in parietal cortex. Furthermore, it may also explain why the peaks in the accuracy maps for cue and rule decoding are not fully overlapping (Fig. 3), with the former being more medial than the latter. Overall, this hypothesis is consistent with other studies (Bunge et al. 2002; Brass and von Cramon 2004; Cavina-Pratesi et al. 2006; Gail and Andersen 2006; Bode and Haynes 2009).
Representation of Compound Rules: Compositionality
Once we established where the brain represents the active compound rules, our further goal was to understand how the brain encodes them. A straightforward prediction of the compositionality hypothesis is that it should be possible to adjudicate which compound rule a subject is representing by only relying on the knowledge of the neural patterns associated with each composing simple rule. We found that this was indeed possible in the right ventrolateral frontal cortex, the very same area in which we found information on compound rules (Fig. 3, Supplementary Fig. S3).
These findings are interesting under several respects. First, by generating results very similar to our previous independent analyses, they replicate the finding that the right lateral frontal cortex is involved in compound rule representation. Second, they show that the right ventrolateral frontal cortex uses a compositional code to temporarily store multiple rules. Thus, the same code is used in this brain area to represent simple rules irrespective of the fact that they are represented alone or as part of a more complex rule. This conclusion was also confirmed by the assessment of a possible alternative interpretation, the "automatic association hypothesis," which could be discarded (Fig. 6).
To date, limited evidence is available on how representations of multiple items are organized in working memory. In a recent paper, Li et al. (2007) applied multivariate decoding to simple and "complex" rules, similarly to the present experiment. The simple rule was categorization of a test movie based on spatial similarity to the prototype, and the complex rule was categorization based on spatial and temporal similarity to the prototype. They showed that prefrontal cortex is involved in the representation of both rule types. Since they employed only one simple rule and one complex rule, they could not explore the relation between the neural representations of specific contents of the 2 rule types. Most neurophysiological studies focused on the preliminary question of how neurons represent individual memory items, so that only one study explored an issue related to ours (Warden and Miller 2007, 2010). Warden and Miller found evidence for independent coding in monkey prefrontal cortex. They had monkeys remember 2 objects sequentially presented. Two delays were introduced between the first and the second object and after the second object. They found that the spiking activity of single neurons after the presentation of the first object did not relate in a straightforward way to the spiking activity of the same single neurons after the presentation of the second object. Thus, at least for a simple short-term memory task, the monkey brain seems to use an independent code in lateral prefrontal cortex: The neural code of exactly the same object changes depending on the context (first vs. second delay). Further evidence is needed to establish whether this is a real cross species difference, whether it is related to the different scale explored by the 2 techniques, or whether it is related to differences in the specific task at hand. In favor of the last interpretation, we may notice that the representation of the first object has been explored in 2 different task phases (first and second delay). The use of an independent code in this case would be consistent with recent evidence showing that different task phases of the same trial are associated with approximately orthogonal patterns of activity across the population of neurons in the lateral prefrontal cortex (Sigala et al. 2008). Conversely, in our task, we tested the "same" task phase belonging to different trials. A further element to consider is that in our task, the use of a compositional code is beneficial for performance. A compositional code not only allows the brain to temporarily store the multiple rules involved in the active rule set but also to keep track of the similarity relations of the currently active rules with the other rules used in the task. This in turn allows exploiting the expertise acquired in managing the simple rules to boost the performance of the compound rules.
Please note that, as shown by the cross-classification procedure between different cue sets, the rule representations we identified are cue invariant and thus have a certain degree of abstraction. However, cue invariance, in our study, does not imply the use of a compositional code. One area could use a unique code to represent a specific compound rule, irrespectively to the triggering cue, and still it could use a noncompositional code. This seems to be the case of the lateral parietal area.
In previous fMRI studies, it has been proven difficult to functionally dissociate lateral prefrontal and parietal cortices (e.g., Brass and von Cramon 2002, 2004; Crone et al. 2006; but see also Montojo and Courtney 2008 for previous positive evidence; Rowe et al. 2008; Stoet and Snyder 2009). In this study, the use of a compositional code was observed in the prefrontal but not in the parietal cortex. This difference across the 2 brain areas was also formally confirmed by the ROI analysis. This corroborates our hypothesis about their distinct, even though strongly related functional roles. We argued that the parietal cortex is mainly involved in translating the cues into their specific meaning. If this were the case, then the "parietal task" could be effectively redescribed as the application of a different rule set compared with the main (frontal) task. This "accessory" set would map each cue to the appropriate rule, such as, for example, "If cue 1, then rule 3." Critically, in this accessory rule set, no whole-part relation is present. None of the accessory rules can be derived from the combination of any other since each of them contains a different cue. The representation of such rules, not being compositional at the conceptual level, should not be compositional at the neural level as well, even in brain areas that use compositional coding as a general organizing principle.
As mentioned in the introduction, the ways in which 2 or more simple rules can be combined together in a more complex rule set exceed the one we considered in the present study. Further studies are thus needed to directly test whether a compositional code is used also in those cases, as it could be extrapolated from the presented evidence. Most interestingly, further studies are warranted to understand how the brain encodes the different ways of combining the same set of simple rules.
Overall, the findings on cue decoding, rule decoding, and compositionality analyses suggest that the lateral parietal cortex is involved in translating the cues into their meaning, that is, the currently active rules. Only the latter information is then made available to the inferolateral frontal cortex, which will maintain it during delay, and supposedly, will be also involved in its final implementation (Buckley et al. 2009).
Our study shows for the first time that the code used to temporarily store rule information in lateral prefrontal cortex (but not in parietal cortex) is compositional. Further evidence is needed to understand in which other contexts, for which material and in which brain areas this type of code is used. The definition of the code used by a brain area in relation to a specific task will also represent a further powerful tool to better characterize the functional role of brain regions, even when they are part of highly integrated and interconnected networks. Finally, our findings also address an important question related to "mental state decoding" (Haynes and Rees 2006). It suggests that it might be possible to decode other complex compound thoughts by learning the neural patterns of the known composing elements.
Supplementary Material
Supplementary material can be found at: http://www.cercor.oxfordjournals.org/.
Bernstein Computational Neuroscience Program of the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung Grant 01GQ0411).
We thank Alessandro Treves and Jakob Heinzle for useful comments on our findings and analyses. Authors Contributions: C.R. and J.D.H. designed the research; C.R. and K.G. performed research; C.R. analyzed data; and C.R. and J.D.H. wrote the paper. Conflict of Interest : None declared.


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Jellybean Benitez's Five Classic Diva Jams, Playing the Vagabond This Saturday
John "Jellybean" Benitez is inseparable from the history of popular dance music. The pioneering Nuyorican DJ-producer got his start spinning records at some of New York City's most seminal nightclubs, including the legendary Studio 54 and Paradise Garage.
And since the early '80s, he's put his Midas touch on countless club bangers. Perhaps most important was his role as producer for some of the most iconic pop divas of the last three decades, from Madonna to Debbie Harry.
With his command of Latin freestyle and house, Jellybean is certainly a favorite in Miami, which is why the Vagabond has kept him coming back for his Ain't Nuthin But A House Party monthly residence for a couple years now.
Catch him there on Saturday. And in the meantime, brush up on his stellar discography with Crossfade's top five Jellybean-produced diva jams.
1. Madonna's "Holiday"
2. Whitney Houston's "Love Will Save The Day"
3. Debbie Harry's "Feel The Spin"
4. La India's "Dancing on the Fire"
5. Jellybean Benitez featuring Niki Harris' "What's It Gonna Be"
Upcoming Events
The Hands of Liberace starring Phillip Fortenberry
TicketsThu., Feb. 23, 8:00pm
Lucinda Williams
TicketsFri., Feb. 24, 8:00pm
WDNA Miami Downtown Jazz Festival
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TicketsSat., Feb. 25, 8:00pm
Dennis Deyoung: the Music of Styx
TicketsSat., Feb. 25, 8:00pm
Jellybean Benitez with Vagabond residents. The Vagabond, 30 NE 14th St., Miami. The party starts at 10 p.m. Call 305-379-0508 or visit thevagabondmiami.com.
Follow Crossfade on Facebook and Twitter @Crossfade_SFL.
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30 NE 14th St.
Miami, FL 33132
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subset: commoncrawl


text:
Eye Scope for iPhone 4 or 4-5
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Eye Scope for iPhone 4 or 4-5 Product Description
An ingenious zoom lens that fits your iPhone! This amazing iPhone zoom lens clickson to your existing iPhone camera andgives youpicture detail you never thought possible. Mobile phone photography is constantly improving and now rivals or even exceeds the quality of many small digital cameras. The one thing that mobile phone cameras can fall down on is the level of optical zoom available. This clever add-on is a simple solution to the problem and gives your iPhone camera an 8x optical zoom ability while retaining great picture quality. Simply attach the adjustable universal bracket to your iPhone, and slot in the lens whenever you want to use it to take close-up pictures. The bracket also has an adaptor on its base, allowing you to attach it to a tripod or monopod stand for extra stability. The iPhone Zoom Lens also includes a tripod for stability, a case, strap, and high quality lenses. It's small enough to carry in your pocket and makes your iPhone evenmore functional. Compatible versions available for 3G, 4G and 4/5 iPhones.
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Upper Dells Boat Tours
Beginning Friday, May 22, 2015 and running through Monday, September 7, 2015, Wilderness Resort guests will get more with their stay! Any registered guest will receive a free child's ticket (ages 4-11) to ride the Original Wisconsin Ducks and/or the Upper Dells Boat Tours when they purchase an adult ticket. This is a savings of $13.35!
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Description : Sculpt your ultimate model for your perfect summer look in this dress-up and avatar-creation game in one. Mix and match haircuts, face shapes and features, make-up, and (of course) a huge closet of summer clothes!
Tags :
My Summer Avatar, My Summer Avatar online games, free My Summer Avatar games, free games, free online My Summer Avatar games, My Summer Avatar game, free My Summer Avatar,
Category : Skill » Make Up
Total Play : 8300
Link Game : My Summer Avatar


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Great news that the Agar Trust is giving young musicians the support to make that journey for their music making. Best wishes to the Trust and to all the award winners.
Dame Evelyn Glennie, Percussionist.
Creating music is one of life's greatest joys. Get on that bus and go for it.
Sally Beamish, Composer.
There are huge numbers of talented young musicians in rural Scotland who face enormous challenges to the development of that talent. The Agar Trust's focus on supporting these youngsters, across all genres of music, is inspiring and invaluable.
Dougie Pincock, Piper.
|October 2016:
After two years of operation we have made awards to over 100 young people wishing to take up musical education beyond their local area.
It is an astonishing response, and we have been delighted to hear from you all!
this map of where you all come from.
|To see the full list of award winners, click
|The next deadline for applications will be 26 March 2017.


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I will be your Travel guide in Bangladesh
About This Gig
Hi. I am sabbir .I am study in B.SC. in computer science . I was born and brought up in Bangladesh and have been living here all my life and have visited Bangladesh extensively. So I guess that gives me enough expertise and experience to plan a trip/tour for you in Bangladesh.
.I will provide This services :
- Itinerary Planning
- Budget Holidays
- Travel Advice
- Guide you about safe way to travel in Bangladesh
- Your safety
- Luxury Trip
- ·Rents Cabs
- Do Railway Bookings
- ·Finding Cheapest Flights
- Find person to travel with you if needed
If you have any special requests let me know and I'll accommodate it for you. :)
Each of our work is customized and specific to your
requirement. We understand your requirements in detail and only then start
working on your tour / holiday.
We ensure you are 100% satisfied with our work.
We settle for nothing lesser then a customer who is Overly Satisfied.
Please inbox me before order .
Thank you.


text:
Give Now
" You cannot run, writing prose. You can run while writing poetry."
Alexandria Hollett's poem "Blessed Are The Persecuted: An Open Letter To The Indiana Legislature" was a lifetime in the making.
Readings from the oral history "Voices from Chernobyl" by 2015 Nobel laureate Svetlana Alexievich.
The first of the two-part summer series finale, on the literature of the Chernobyl nuclear catastrophe.
This week: the costs of longing for an ideal society.
Ben Debus reads "The Wife's Lament" and "The Domina in Her Garden."
Jill Koran reads "Icarus Bounced," "Apostles Creed," and the Reflections: X, Q, and N.
Paul Asta reads "Where You Left Her," "Timeline In Which I Do Not Exist," "A Chorus of Static," and more.


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Category page
808pages onAdd New Page
this wiki
this wiki
In Drakensang every character has 8 different Attributes. These Attributes influence how good (or bad) the character deals with a variety of tasks and thus have effect on the outcome of Talent Tests, Base Attack Values, Base Parry Values, Base Ranged Values, Special Abilities, Vitality, Endurance, Astral Energy and Attribute checks.
These 8 different Attributes are:
- Courage (CO)
- Cleverness (CL)
- Intuition (IN)
- Charisma (CH)
- Dexterity (DE)
- Agility (AG)
- Constitution (CN)
- Strength (ST)
Attributes can be increased, decreased or restored by means of Spells or Objects.
During the game, these Attributes can be raised by means of Experience Points, but every Profession has a maximum value for each Attribute.
"The ability to react quickly and resolutely in critical situations, but also daring, and...Courage
"The skillful use of the hands and fingers, good hand-eye coordination, especially in intricate...Dexterity
"General physical suppleness, quick reactions and reflexes, accuracy in judging your own reach...Agility
"Stands for the availability of raw muscle power, especially the capacity to use it tactically...Strength
"This signifies intelligence and logical reasoning, the ability to analyse a situation bit by bit...Cleverness
"The ability to make sound decisions without having to think about it at length, or a swift...Intuition
"This is related to personal magnetism, eloquence and leadership qualities. The ability to make...Charisma
Pages in category "Attribute"
The following 8 pages are in this category, out of 8 total.
C cont.


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For mostly city driving, is it worth spending the extra money on a cpo Lexus hybrid suv or just get the rx350?
1 Answer
The great thing about the Toyota/Lexus hybrid system is city driving. The gas engine completely shuts off when coasting or very light acceleration. So often in stop and go traffic the engine doesn't even run. Bear in mind in weather extremes (hot and cold) the engine runs more to cool and heat but still, it does well. If you are looking at this from a purely CPO standpoint, go with the 350, you will (IMHO) spend more on the 450 with the upfront costs and calculating your benefits is sketchy at best, due to all the variables to consider. I have a 400h and have gotten as much as 36mpg for a tank of gas driving through the White Mountains in NH and VT. On this trip, I was driving around 40-50mph and not running the AC. I typically get about 26-28mpg commuting to work and 25 on the highway. The worst mileage I have gotten has been about 23mpg in the winter with the engine running a lot and some more aggressive driving. Still, I love the hybrid because for me, its about efficiency, not TCO ... when I am stuck in traffic or eating my lunch in my car, there is a value for me to not be consuming gasoline. Good luck!
Your Answer
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text:
by Jonathan FieldHere are some ways to get the "go button" working
and create a willingness to move forward freely. READ MORE
by Jonathan FieldHandled the right way, the process of teaching your horse to accept the spray from a water hose can lay the groundwork for solving future training problems. READ MORE
by Jonathan FieldIf a horse doesn't have good self-carriage at slow speeds,he will only have more trouble at faster gaits. READ MORE
by Jonathan FieldIt takes time to build a language both you and
your horse understand. But by rewarding small
steps in the right direction, you will advance your goal, and pretty soon you'll be having a great conversation. READ MORE
by Jonathan FieldTo go from "rider" to "true horseman" requires learning all that you can about horses, as well as how to show them leadership. READ MORE
Copyright 2017 by Cruz Bay Publishing, Inc. an Active Interest Media company


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Discussion in 'Current Affairs, News and Analysis' started by micksmith, Feb 25, 2009.
The heart of the site is the forum area, including:
RIP & God speed, Royal
RIP Royal. The big guy up there will get the wets in.
Condolances to Family and Friends.
A bad day for the good guys...
They don't come much braver than a 'Bootie' in a battle.
Requiescat in Pace - Hard Man.
RIP lad, and thank you.
R.I.P. Mate
Rest in Peace bootie. Condolences to your family.
RIP Bootie, Condolences to family and friends.
Take care mucker. How many more...
Per Mare Per Terram.
Rest in Peace Bootie & your 3 Rifles buddies.
Many many thanks to the brave Medics who did the CasEvac & the MERT team who attended the RGJ lads. Our thoughts are with the Royal Marine & RGJ families and friends at this difficult time.
Separate names with a comma.


text:
Entry Terms : "Training Equipment"
UMDC code : 16296
1-30 of 76 Match(es).
Ambu A/S
Ambu A/S (Finland)
Ambu A/S (Sweden)
Ambu France Sarl
Ambu GmbH
Ambu Inc
Ambu was founded in 1937 by Dr. Holger Hesse, who had come to Denmark from Germany.
Ambu Ltd
Ambu SL
Ambu srl
American Society of Health-Systems Pharmacists
ASHP is the national professional organization whose nearly 40,000 members include pharmacists, pharmacy technicians, and pharmacy students who provide patient care services in hospitals, health systems, and ambulatory clinics. ASHP is the only national organization of hospital and health-system pharmacists and has a long history of improving medication use and enhancing patient safety.
Apogee Communications Group
Director of Photography. Owner of Apogee Communications Group, a video and film production company producing award winning educational, training, marketing, medical and television programs for an international market.
Armstrong Medical Industries Inc
Armstrong Medical Industries, Inc. supplies medical carts, CPR manikins, medical education manikins and equipment for emergency, anesthesia, hospitals, EMS, and education.
Beckman Coulter Inc
Beckman Coulter develops, manufactures and markets products that simplify, automate and innovate complex biomedical testing. Our diagnostic systems are found in hospitals and other critical care settings around the world and produce information used by physicians to diagnose disease, make treatment decisions and monitor patients. Scientists use our life science research instruments to study complex biological problems including causes of disease and potential new therapies or drugs.
Buhl Electric Inc
Buhl Electric, Inc. is a leading manufacturer of Lamp Holders and other lighting components needed by the lighting industry. We offer a diverse line of products for lighting OEMs and Distribution.
Cardionics Inc
Cardionics designs and manufactures all of its own products and marketing is done on a worldwide basis.
Carolina Biological Supply Co
From our beginnings in 1927, Carolina Biological Supply Company has grown to become a leading supplier of science teaching materials. Today, from our headquarters in Burlington, North Carolina, we serve customers worldwide, including teachers, professors, home-school educators, and professionals in health and science-related fields.
Consumer Care LLC
Our products are designed to assist you as you work to provide your clients with restored function, improved mobility, and pain relief with the ultimate goal of preventing or reducing physical disability in patients suffering from injury or disease.When you purchase from Consumer Care, you get professionally designed products made by experienced craftsmen using the highest quality materials.
CooperSurgical Inc
CooperSurgical is the leading company dedicated to providing medical devices and procedure solutions that improve health care delivery to women regardless of clinical setting. Our company is fostering that position through expansion of its core businesses and introduction of advanced technology-based products which aid clinicians in the management and treatment of commonly seen conditions.
Covidien (Belgium) BVBA/SPRL
Covidien (Japan)
Covidien (US)
Covidien is a leading global healthcare products company that creates innovative medical solutions for better patient outcomes and delivers value through clinical leadership and excellence.
Covidien Canada
Covidien Deutschland GmbH
Covidien France SAS
Covidien Ltd (United Kingdom)
Covidien Nellcor/Puritan Bennett
At Covidien, we're passionate about making doctors, nurses, pharmacists and other medical professionals as effective as they can be. Through ongoing collaboration with these medical professionals and healthcare organizations, we identify clinical needs and translate them into proven products and procedures. Our industry-leading brands – including Kendall, Mallinckrodt, Nellcor, Puritan-Bennett and Valleylab – are known and respected worldwide for uncompromising quality
Covidien Private Ltd (Singapore)
Covidien Pty Ltd
Covidien Switzerland Ltd
Datex-Ohmeda Inc
GE Healthcare provides transformational medical technologies and services that are shaping a new age of patient care.
Our broad expertise in medical imaging and information technologies, medical diagnostics, patient monitoring systems, drug discovery, biopharmaceutical manufacturing technologies, performance improvement and performance solutions services help our customers to deliver better care to more.
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text:
At Golden Rule, we obey the rules to live by. Our goal is to provide an amazing customer experience from the first phone call to solving the problem. You can believe we deliver respect, understanding, loyalty, expertise and service every day... on every call.Payment Options: Cash, Visa, Master Card, American Express, Discover, Personal Checks, Financing
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text:
The body of Swaran Singh, who was killed in a road accident on Thursday allegedly involving the bus of the Dabwali Transport Company owned by the Badal family, was cremated at Behrampur Zimidara village near here on Saturday.
The body was cremated after the mediators, who reportedly effected the compromise on behalf of the bus company, paid a compensation of Rs 22.5 lakh to the family. Swaran's son Parvinder Singh lit the pyre.
The post-mortem was conducted by a board of doctors at the Rupnagar civil hospital. According to its report, the death was caused by internal injuries.
The cremation was slightly delayed after a few members of the sangharsh committee formed to spearhead the agitation on the issue did not allow the body to be taken for cremation, demanding that the copy of the FIR registered against the bus driver be shown to them and the promise for providing a government job to one of the family members must be given in writing.
Congress general secretary Ambika Soni visited the bereaved family and attended the funeral. Bahujan Samaj Party general secretary and MP Narinder Kashyap and Punjab unit president Avtar Singh Karimpuri also met the family.
Chamkaur Sahib MLA Charanjit Singh Channi, Kharar MLA Jagmohan Singh Kang, District Congress Committee president Harbhag Singh Saini, former president Amarjit Singh Saini, Zila Parishad member Harpreet Singh Basant, Chamkaur Sahib market committee chairman Harnek Singh Bhura, former Punjab Youth Congress president Bhupesh Sharma and Punjab Congress (OBC dept) chairman Gurinder Pal Singh Billa were among those present on the occasion.


text:
Is Crayons Academy incorrect or missing key business information? Update your Hotfrog listing today.
Contact details
Are these details correct?
Hotfrog INDIA provides information regarding Crayons Academy in Pondicherry Tamil Nadu. Crayons Academy is located at 25 3rd Floor JNStreet, Pondicherry Tamil Nadu 605 001 and provides Abacus services. Contact them on (+91) 9786500986 or by visiting them on their website http://www.crayonsacademy.in.
Is Crayons Academy in 25 3rd Floor JNStreet, Pondicherry Tamil Nadu 605 001 your business? Claim your listing and attract more leads by adding more content, photos and other business details.
We have more Abacus services in Pondicherry Tamil Nadu available on Hotfrog INDIA. You can update your search for Pondicherry Abacus by location, keyword or service options.
PARNI PUTHOR Pondicherry Tamil Nadu 602101
No 100 Jawaharlal Nehru Street Pondicherry Tamil Nadu 605001
70A 1st cross Pondicherry Tamil Nadu 605010
12 Anthoniar Koil street Thiruvalluvar Nagar Pondicherry Tamil Nadu 605003
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WikiLeaks Julian Assange has taken another blow that his supporters say was done under pressure from the Pentagon. Time magazine ignored the will of voters and selected Mark Zucerkberg as its Man of the Year.
Given that Facebook has been around for years, and already has been the subject of a movie, it is difficult to see why Zuckerberg's accomplishments are being recognized now.
Assange, on the other hand, is sitting in a London jail. His lawyers say it is because he has riled most of the world's major governments.
The vast majority of blogs in reaction to the decision were negative. The choice was not explained in the article announcing the selection of Zuckerberg.
This choice isn't likely to expand Time's dwindling influence.
Assange got 382,000 votes to the Turkish prime minister, who had 233,000.
Time didn't even bother to explain why it ignored the voters. In an editorial it merely said Zuckerberg, and his Farmville, poking, and viewers explaining how they change diapers, and Assange, are two sides of the same coin.
How will Donald Trump's first 100 days impact YOU? Subscribe, choose the community that you most identify with or want to learn more about and we'll send you the news that matters most once a week throughout Trump's first 100 days in office. Learn more


text:
Waterfront Home in Plantation Key(4 Reviews)
The owner has been emailed.
To book this property, you need to complete your booking through checkout on the VRBO website. We can't protect your payment if we don't know you've booked through us. Learn more
- Minimum stay:30 nights
- Sleeps:8
- Bedrooms:3
- Bathrooms:2
- Property type:House
- Internet:Yes
- Pets considered:No
- Wheel chair accessible:No
About the Property
Waterfront 3 Bedroom/2 Bathrooms With A 3rd Bathroom In Storage Room
Sleeps 8 people comfortably, Granite kitchen fully equipped with pots, pans, plates including a Teak Tiki Bar once you open the front door of the living room area. The kitchen, dining and living room is an open concept and you can enjoy waterfront views from all areas of the home. There is also a terrace with screened porch in the second floor so you can enjoy your meals outside if you wish. Also, the master bedroom in the back has a king size bed with private bathroom and walk-in closet. 2nd and 3rd bedrooms have queen size beds which share a bathroom in the hallway. We also have two portable twin beds available upon request. We have covered parking downstairs which accommodates 6 vehicles and a hammock and tiki bar downstairs where you can relax with family and friends. The house has internet with Xfinity and we also have available a barbeque.
When you're ready to book, here are some tips to keep in mind.
Paying with a credit card on VRBO protects your payment for up to $10,000 against listing fraud. We recommend completing your booking on VRBO.Learn more
Member since: 2013
Property Type
- House
- 2000 sq. ft.
Accommodation Type
- Vacation Rental
- Guests Provide Their Own Meals
Onsite Services
- Housekeeper Optional
- Long-Term Renters Welcome
- Children Welcome
- Non Smoking Only
- Pets Not Allowed
- Wheelchair Inaccessible
Bedrooms: 3 Bedrooms, Sleeps 8, Beds for 3-8
- Master Bedroom: 1 king, 2 twin/ single
- Bedroom 2: 1 queen, 2 twin/ single
- Bedroom 3: 1 queen
- Master has a King size bed with two night stands, headboard and full set of sheets, private bathroom with shower/tub combination and walk-in closet. Sliding door with easy access to terrace. Two separate rooms with queen size beds which share a bathroom with shower and sink in the hallway....moreless
Bathrooms: 2 Bathrooms
- Satellite / Cable
- Television
- Adventure
- Away From It All
- Family
- Historic
- Romantic
- Sports & Activities
- Tourist Attractions
- Churches
- Cinemas
- Duty Free Shops
- Health/Beauty Spa
- Library
- Live Theater
- Marina
- Museums
- Playground
- Recreation Center
- Restaurants
Local Services & Businesses
- ATM/Bank
- Babysitter
- Fitness Center
- Groceries
- Hospital
- Laundromat
- Massage Therapist
- Medical Services
Leisure Activities
- Antiquing
- Beachcombing
- Boating
- Eco Tourism
- Outlet Shopping
- Paddle Boating
- Scenic Drives
- Shopping
- Sight Seeing
- Walking
- Wildlife Viewing
Sports & Adventure Activities
- Basketball Court
- Cycling
- Deepsea Fishing
- Fishing
- Fly Fishing
- Freshwater Fishing
- Jet Skiing
- Kayaking
- Paragliding
- Parasailing
- Pier Fishing
- Rafting
- Roller Blading
- Sailing
- Scuba Diving Or Snorkeling
- Snorkeling
- Snorkeling/Diving
- Sound/Bay Fishing
- Surf Fishing
- Swimming
- Tennis
- Water Skiing
- Water Tubing
- Wind-Surfing
- Air Conditioning
- Clothes Dryer
- Hair Dryer
- Heating
- Internet
- Linens Provided
- Coffee Maker
- Dishes & Utensils
- Kitchen
- Microwave
- Oven
- Pantry Items
- Refrigerator
- Stove
- Toaster
- Balcony
- Deck / Patio
- Outdoor Grill
House Cleaning
- Housekeeper Optional
RatesCurrency Conversion
|Weekend Night
|Monthly *
My Standard Rate
30 night min stay
Additional information about rental rates
- Cleaning Fee $250
- Refundable Damage Deposit $1,000
After each rental period, my home will need to be cleaned, before next renter can enter premises fee is $250. There is also a one time fee of $500 for a refundable damage deposit, which will be refunded when property is checked after your rental. Upon the day of any rental, I will meet the renters at the property to give the keys and instructions on how to use the appliances.
Owner's cancellation policy:
100% refund if canceled at least 30 days before arrival date.
Don't forget your vacation protection! Get protected now
Adding our Vacation Protection services can make sure your getaway goes smoothly, no matter what. We offer Cancellation Protection and Damage Protection so you can truly relax.
Car: Necessary
Our home has a 50 foot dock with davets which you can bring your boat and be in open water within five minutes and also in the Gulf of Mexico within 15 minutes. Enjoy the best clear waters for snorkeling and diving and also get to the best fishing grounds in the keys within 15 to 20 minutes. Right around the corner of our home you have a shopping center with a Winn Dixie, Theater, McDonalds, Subway and Hospital and the best restaurants at your disposal. Come and enjoy the most peaceful home you will ever stay at!
Owner info
Year Purchased: 1981
About the owner: My name is Roberto Rodriguez and I am the person with the cap cutting the fish. I am a retired Mechanical Inspector from the City of Miami and I love to fish and enjoy the peacefulness and tranquility of my home in Plantation.
Why the Owner Chose Tavernier, FL, USA:
This property is well situated at the end of the canal, with breathtaking views of the many homes down the canal as well as the many views of the native Iguanas. This house is very special to me because I built it from scratch. Everyone that has stayed in my home has always come back to stay because of its location, and many other amenities that my home has to offer.
The Unique Benefits at this House:
I built this home in 1980 all by myself and my dream came true when I finished it. I have been enjoying this dream home for the past 30 years and still continue. I drive almost every week to my home and enjoy the good life of the keys.
Contact us
Send email
- Primary: 7863852543
- Secondary: +1 7867156439 Daughter, Helen Rodriguez
- Click Here to see All My Rental Listings
View from the end of the dock
Renovated kitchen
Back view of house from end of Canal
This listing was first published here in 2013.
Date last modified - Sunday, February 26, 2017
Partner Offers
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- We'll back you with our Book With Confidence GuaranteeTM
- You'll get comprehensive payment protection when checking out on our website
- We're always here for you with customer service available 24/7
Read more about our Book with Confidence GuaranteeTM
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text: The Sign-Off Graveyard Shift - Vanilla NYA!I_Kluge3/14/14 9:23pmFiled to: TAYveyardNYAN ZakuGundamVanilla241EditPromoteShare to KinjaGo to permalink Welcome to the Graveyard Shift! Hosted by Kotaku's reader-run blog, TAY. A place to talk about life, video games, or anything. So feel free jump in the topic, or not ¯\_(ツ)_/¯. Be sure to check out created articles on TAY or the random blips of TAYCLASSIC. Also we have a This is for you! The Late Night / Early Bird / Office Drones/ Sickbed / All-Nighter / Old World Citizens / Future Warriors! Advertisement Image by Zaku-sensei.We sign-off with... Some vanilla! Advertisement Hey all! The weekend is once again upon us, and it time to close down and chelax(Disclaimer:Chelax is not a real word...I think). So I will make a nice sweet vanilla for all, and post to your heart content. Have a song you itching to share? Go for it. Finally got done with a project? Do tell. Feeling the blues? TAY got your back! Forgot to find someone to fill Friday nights? Tell me about it...But whatever it may be, feel free to share you game accomplishment or unwise life choices!So TALK AMONGST YOURSELVES! TAYPhotoshop contest for the Open Forum. TAY community Steam GroupThe Community GAME NIGHTS! Need to reach admins and authors?The TAYlendar, and how it WORKS.The TAY IRC Chatroom for those that GOTTA GO FAST!TuTtorial! For those wondering WHY NO ONE COMMENTS ON MAH ARTICLE!The Graveyard shift is not responsible that gawdanm white mobile suit!Reply24 repliesLeave a reply


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strategy+business magazine
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text:
333: Homeless, But Not Heartless
Entry Number: 333
Age Group: Middle Word Count – 550
Title: Homeless, But Not Heartless
I walk down the sidewalk. I see sad, pleading eyes looking up at me in desperation hidden with a hopeful smile. This poor, old man I see before me, leaning against a fence, has only his right arm. He has no other limbs. He has nothing except his shirt and pants and a little tin bowl. I want to help him. I don't care if he's not a real beggar. He needs money. He needs help. And he needs a smile.
I drop ten RMB into his little tin bowl and smile. He thanks me. I see the sadness and pleading in his eyes turn to the likeness of the kind old man I know he is on the inside. I see the desperation become one with the now wide smile in a notion of gratitude. I wave goodbye and smile again.
Outside a dirty train station, I look over and see a man and a woman lying on pieces of cardboard, trying to sleep under a stone stairway. They are right next to a sewer, that can't be very pleasant. I notice a man with a big dent in his head, trying to get some rest while everyone stares as they walk by.
Months later, I go with my family and one of our Chinese friends to visit his hometown. I am surprised as I eat a meal that our friend's family worked very hard to prepare and was very good, yet they didn't have running water to wash the dishes in. They only have a big bowl they fill with water and wash their hands in.
In that same town, we went to another house that had only an old curtain for a door and dirt floors, walls, and ceilings. Whatever the circumstances, there were always welcoming gestures, joyful eyes, and happy, smiling faces. They were grateful for what they had, however little it may be.
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The beggars I see a lot usually smile and wave. They can even muster up a kind word or two in the scorching heat, or the freezing cold.
It makes me sad, seeing these poor people in China, but it also makes me more grateful for what I have. It brings me to an awareness of how fortunate I am to have a loving family, nice clothes, good food and water daily, and a place I can call home.
Thank you, China, for teaching me the true meaning of gratitude.


text:
Wednesday, October 6, 2010
its the little things
i love almost everything that design firm, m design does. but while perusing their website the other day i realized what i loved most were their little vignettes... a zig zag bench and matching shoes to the perfect little vanity set up (with the most amazing green lamp).
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Providing Free WiFi to Rhinelander Ice Arena


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This week, Siemens Enterprise Networks LLC expanded its HiPath IP convergence product line with the launch of 12 new products ranging from a mixed configuration IP PBX-like platform for small businesses to a new softswitch for large enterprises prepared tWould you like peanut butter with your chocolate, or chocolate with your peanut butter? Thats the kind of conundrum enterprises sometimes face when trying to choose a system for converging voice and data traffic. At American Freightways Inc., there is no room for error in voice communications, so when the company began replacing traditional PBX systems with VOIP (Voice over IP) platforms at some of its older customer centers, it chose an IP product line from a vendor steeped in telephony, Siemens A.G.
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text:
Offspring Greatest Hits
Sony Music
Audio CD
Once bought, this item cannot be cancelled or returned.
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|Official Release Date
|Jun 21, 2005
|Item Code
track listing1. Can't Repeat
2. Come Out And Play (Keep 'Em Separated)
3. Self Esteem
4. Gotta Get Away
5. All I Want
6. Gone Away
7. Pretty Fly (For A White Guy)
8. Why Don't You Get A Job?
9. The Kids Aren't Alright
10. Original Prankster
11. Want You Bad
12. Defy You
13. Hit That
14. (Can't Get My) Head Around You
descriptionThe Offspring Greatest Hits album is truly that an amazing collection of hit songs, from their Epitaph Records tracks 'Come Out And Play (Keep 'Em Separated)' and 'Self Esteem' through 'Hit That' and '(Can't Get My) Head Around You'. This album even includes a brand new track recorded this year, 'Can't Repeat', which will be blasting out of radios all over the country by the time you read this. Coinciding with the band's upcoming headlining of this summer's Warped Tour, this album is a must-have for anyone who sees, has seen, or will ever see this awesome band. Also includes a hidden bonus track, 'Next to You' which follows a few seconds after the 14th track, '(Can't Get My) Head Around You'. Columbia. 2005.
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Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves 4
Unfortunately we are unable to add this recipe to your favourites, please try again later.
For the trout, season the trout fillets all over with salt and freshly ground black pepper.
Heat the butter in a frying pan over a medium to high heat. When the butter is foaming, add the trout fillets, skin-side down, and fry for 2-3 minutes, or until the skin is crisp and golden-brown and the flesh is almost cooked through.
Turn the trout fillets over and fry for a further minute, then remove the pan from the heat and set aside to rest (the residual heat from the pan will cook the trout fillets through).
Meanwhile, for the dwarf beans, peas and almonds, add 25g/1oz of the butter and all of the beans to a frying pan and heat over a medium heat. Pour in enough boiling water to just cover the beans, then return to the boil.
Reduce the heat until the mixture is simmering, then continue to simmer for 2-3 minutes, or until the beans are just tender. Drain well and set aside.
Add the peas and continue to boil for a further 1-2 minutes, or until tender.
Meanwhile, heat the remaining butter in a separate frying pan over a medium heat. When the butter is foaming, add the shallots and fry for 3-4 minutes, stirring frequently to prevent the shallots from sticking or burning, until the shallots have softened and are golden-brown and the butter has darkened in colour.
Add the drained beans and peas and the flaked almonds to the pan with the shallots in it and stir to combine. Season, to taste, with salt and freshly ground black pepper.
Meanwhile, for the salad and dressing, heat the two tablespoons of light olive oil in a frying pan over a medium heat. When the oil is hot, add the bread cubes and fry for 1-2 minutes on all sides, or until crisp and golden-brown all over. Remove the croûtons from the pan using a slotted spoon and set aside to drain on kitchen paper. Season, to taste, with salt.
In a large bowl, whisk together the mustard, vinegar and the two tablespoons of extra virgin olive oil until well combined. Add the lettuce leaves and croûtons and mix with the dressing until they are coated.
To serve, spoon the dwarf beans, peas and almonds into the centre of each of four serving plates. Place two fillets of trout on top of each serving. Pile the salad alongside.
Episode 38
BBC One