Hello, As of a few days ago, my cursor in Word cannot keep up with my arrow keys. For example, if I press the right-arrow key five times, it takes Word a few seconds for the cursor to get there.
This is particularly annoying if I want to say, highlight a sentence using arrow keys alone, as it takes Word much longer than usual to accurately move the cursor. It does not occur with my work PC, which has Windows 10 Enterprise.
The computers otherwise have similar specs former is Lenovo; latter is Dell. Edit 2: Reiterating some points made below, the problem occurs in Word only. Powerpoint, Excel, etc. Edit 3: The problem only occurs when multiple docs are open. Others have noted that disabling the Mendeley add-in fixes the issue. I confirmed this worked for me as well. Disabling Mendeley is not a satisfactory solution for me, however, as I rely on Mendeley for reference management.
I also have been experiencing odd cursor behavior in Office Word. When holding an arrow key, it is slow to respond, and then suddenly jumps several words. When selecting to the left with the arrow key SHIFT-Left, hold , it initially selects to the RIGHT and then jumps to the left, selects some text leftward and then jumps right and then jumps back left, jumping and selecting to the right and then left of the initial cursor position as it slowly progresses leftward.
The language setting is English, with left-to-right paragraphs. The behavior with single arrow presses is as expected. It is only pressing and holding the arrow key that is causing problems. Outlook is not affected, and it seems neither are PowerPoint or Excel. Create an edited text from scratch while using templates. With several customized elements like color, shadows, effects, etc change your document into a visual-styled graphic. To boost and automate workflow import charts from MS Excel and take the support of macros.
To make the important passage more visible automatically highlight it. Office suite enables you to integrate online and share files in real-time with one click, invite your friends for editing as well as commenting, without giving preference to language. To make your document more proficient MS Word gives you references of information, tables of figures and experts, commentaries, and checked letter errors with grammar collection tools. An auto stored tool also helps you to save your document without clicking the save button.
Write more confidently with the help of spelling, grammar, and stylish writing suggestions. Reading View tool enables you to read your docs, letters, or scripts.
MS Word offers Resume Assistant for creating a persuasive resume. It is powered by LinkedIn where you can find millions of job listings, apply, and contact. If the user provides a trusted species tree, then they must designate the root of that tree. Finally, the user launches the analysis and the results are displayed in the Tree Explorer window see fig.
Tree Explorer window with gene duplications marked with closed blue diamonds and speciation events, if a trusted species tree is provided, are identified by open red diamonds see fig. We have now upgraded the Timetree Wizard similar to the wizard shown in fig.
This wizard accepts Newick formatted tree files, assists users in defining the outgroup s on which the tree will be rooted, and allows users to set divergence time calibration constraints. Setting time constraints in order to calibrate the final timetree is optional in the RelTime method Tamura et al. If no calibrations are used, M ega7 will produce relative divergence times for nodes, which are useful for determining the ordering and spacing of divergence events in species and gene family trees.
It is important to note that M ega 7 does not use calibrations that are present in the clade containing the outgroup s , because that would require an assumption of equal rates of evolution between the ingroup and outgroup sequences, which cannot be tested.
For this reason, timetrees displayed in the Tree Explorer have the outgroup cluster compressed and grayed out by default to promote correct scientific analysis and interpretation. In the Tree Explorer , users will be able to display another set of numbers at internal tree nodes that correspond to the proportion of positions in the alignment where there is at least one sequence with an unambiguous nucleotide or amino acid in both the descendent lineages; see figure 5 in Filipski et al.
This metric is referred to as minimum data coverage and is useful in exposing nodes in the tree that lack sufficient data to make reliable phylogenetic inferences. For example, when the minimum data coverage is zero for a node, then the time elapsed on the branch connecting this node with its descendant node will always be of zero, because zero substitutions will be mapped to that branch Filipski et al.
This means that divergence times for such nodes would be underestimated. Such branches will also have very low statistical confidence when inferring the phylogenetic tree. So, it is always good to examine this metric for all nodes in the tree. These upgrades make the seventh version of M ega more versatile than previous versions. For Microsoft Windows, the bit M ega is made available with Graphical User Interface and as a command line program intended for use in high-throughput and scripted analysis.
The command line version of M ega7 is now available in native cross-platform applications for Linux and Mac OS X also. Many other laboratory members and beta testers provided invaluable feedback and bug reports.
Edgar RC. Muscle: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 5 : Google Scholar. Prospects for building large timetrees using molecular data with incomplete gene coverage among species. Mol Biol Evol 31 : — Tree of life reveals clock-like speciation and diversification. The page has step by step information about how to use the tool and create a recovery drive.
Its download page is not clear and the free version hasn't been updated in a long time. Choose the user account where you want to reset the password. After acquiring the risk of deleting recovery drive, three methods on how to do it provided.
The laptop came loaded with a Windows 10 volume licensed version which overwrote the preinstalled software including HP Recovery Manager. Windows 8. You can see three options in the main page. If your HP computer has some issues that you want to restore it to factory settings, turn on HP computer and immediately press the Esc key to display the Startup Menu, and then Download Windows 8. DiskGenius Pro. If your drive is not empty, a pre-formatting process is suggested. Tap or click Update and recovery, and then tap or click Windows Update.
Quickly resize, extend and split partitions without data loss to make the best use of hard drive capacity. The partition is there but just empty. It may let you create DVD's for a full recovery but Windows 8. Windows File Recovery can help recover your personal data. Download the latest version here.
Operating System. A progress bar shows you how long it will take to remove Recovery Manager. Use the media creation tool aprx. The recovery partition on selected products only serves to boot computer into OEM system recovery by pressing F11 key after a computer start. Other versions. Disconnect all peripheral devices, except for the monitor, keyboard, mouse, and power cord.
Following an attack, you must restore AD first before anything else. Step 4. The first thing you can do is reset HP laptop password. Is there any way to create it as a recovery partition again? Please help, I am a bit tense about this.
This feature needs to be enabled on some systems in BIOS. A free download, safe, secure and tested for viruses and malware by LO4D. The surface maps shown in Figure 4E and accompanying animation Video 1 suggest that classifier coefficients fluctuate more over time in anterior than posterior temporal cortex.
The possibility that coefficients fluctuate more in anterior than posterior regions thus suggests that the anterior regions may show greater dynamic change in how they encode semantic information over time, as was also observed in the simulations.
To test whether this qualitative observation is statistically reliable, we measured the variability of change VoC in classifier coefficients at each electrode. This yielded, for each electrode, a series of deltas indicating the magnitude and direction of coefficient change from one 50 ms time window to the next.
We then computed, separately for each electrode, the variance of non-zero changes across this series. For electrodes whose coefficients change only a little, or change consistently in the same direction e. We have combined computational modeling, multivariate pattern classification, and human ECoG to better understand how ventral temporal cortex encodes animacy information about visually-presented stimuli.
In simulation we showed that similar phenomena arise in a deep, interactive neuro-semantic model, producing a characteristic decoding signature: classifiers perform well in the time-window when they were trained, but generalize over a narrow time envelope that widens as the system settles.
This pattern was only observed in a model combining distributed representation, interactive processing, and a deep architecture see Appendix.
This proposal resolves a long-standing puzzle. Convergent methods have established the centrality of vATL for semantic memory, including studies of semantic impairment Julie et al.
Yet multivariate approaches to discovering neuro-semantic representations rarely identify the vATL, instead revealing semantic structure more posteriorly Bruffaerts et al.
One prominent study suggested that semantic representations may tile the entire cortex except for the vATL Huth et al. Setting aside significant technical challenges of successful neuroimaging of this region Binney et al.
Thus the widespread null result may arise precisely because semantic representations in vATL are distributed and dynamic. Several fMRI studies have reported category effects in the mid-posterior fusiform, with animate items eliciting greater activation on the lateral bank and inanimate items eliciting greater activation on the medial bank Martin and Chao, ; Anzellotti et al. In our analysis, these are regions where a stable, feature-like code arises, with animate items signaled by positive voltages on the lateral aspect and negative voltages on the medial aspect.
Since the location and direction of the code are more stable in these regions within and across subjects, the signal remains detectable even with the spatial and temporal averaging that occurs in univariate fMRI. These conclusions rest partly on the analysis of classifier weights. Four lines of evidence suggest not. First, we fit classifiers to only the anterior electrodes in each participant and observed reliable decoding—indeed, performance was equally reliable for classifiers trained on anterior-only and posterior-only datasets A Second, an earlier study applied searchlight representational similarity analysis RSA to the same data Chen et al.
Neither result could obtain if vATL simply subtracted out correlated noise from more posterior areas. Third, the observed pattern of a stable code posteriorly and fluctuating code anteriorly was predicted by the current simulations, using a model validated against neuropsychological, anatomical, and brain-imaging results in prior work Patterson et al. Fourth, the critical importance of ATL for semantic representation has been established by the broad range of converging evidence cited previously.
Prior studies applying temporal generalization to MEG data in visual semantic tasks uniformly report a very narrow and unchanging band of temporal generalization Carlson et al. Our results differ from the MEG pattern, and indeed from most other work applying the temporal generalization approach King and Dehaene, , in showing a gradual widening of the temporal generalization window.
This phenomenon does not arise from the autocorrelational structure of the data itself—the window of time over which an electrode reliably predicts its own future state does not grow wider with stimulus processing A Instead the widening must reflect an increasingly stable representational code.
The simulation explains why the pattern arises in anterior temporal cortex: hub representations in vATL change rapidly early on due to interactions with modality-specific representations throughout cortex, but these changes slow as the full activation pattern emerges across network components.
We have focused on decoding a broad, binary semantic distinction that is a common focus of much work in this area—specifically, whether an image depicts an animate or an inanimate item. Animacy is a useful starting point because it is not transparently captured by low-level perceptual structure; in our stimuli, for instance, low-level visual similarity as expressed by Chamfer matching does not reliably distinguish the animate and inanimate items see Materials and Methods.
Nevertheless it remains possible that decoders in the current work exploit some other property that happens to be confounded with animacy.
Whether this is the case or not, the preceding arguments suggest that the relevant information is expressed in a distributed, dynamically-changing neural code. Further evidence for semantic structure could involve decoding the graded within- and between-domain conceptual similarities existing amongst the stimuli. The question of how to fit such a decoder is, however, somewhat complex, with no standard solution.
Common unsupervised methods like representational similarity analysis—where one computes the correlation between neural and target dissimilarity matrices—don't fit the bill, because such correlations can yield a positive result even if the signal or target truly encodes just a binary label as shown, e.
Pereira et al. Ideally one wants a multivariate decoding model that can be fit to all data within a given time window without feature preselection as in the current paper , but which predicts the embedding of stimuli within a multidimensional semantic space instead of a simple binary classification. Oswal et al. We have taken the voltage measured at an electrode as a neural analog of unit activation in an artificial neural network model.
It remains unclear, however, how the processing units in a neural network are best related to the signals measured by ECoG. Voltages measured at a surface electrode can be influenced by firing of nearby neurons but also by the activity of incoming synapses from neural populations at varying distances from the recording sites. Many ECoG studies decompose voltage time-series into time-frequency spectrograms, which indicate the power of oscillations arising in the signal at different temporal frequencies.
Power in the gamma band is often thought to reflect spiking activity of local neurons Merker, , and thus might provide a better indication of the activity of neurons within vATL proper. We have not undertaken such an analysis for two reasons. First, our goal was to assess whether the neural code for animacy has the distributed and dynamic properties that arise within a well-studied neural network model of semantic cognition.
Such models do not exhibit oscillating behavior, making the model analog to frequency bands unclear. Second, Prior ECoG work has shown that, while object- and person-naming does elicit increased gamma for more perceptual areas like posterior fusiform , in anterior temporal regions it significantly alters beta-band power Abel et al. Similarly, Tsuchiya et al. Thus we have left the decoding of time-frequency information in these signals, and their connection to hypothesized information processing mechanisms in neural network models, to future work.
A drawback of the current approach is its limited field of view: we cannot draw inferences about other parts of cortex involved in semantic processing of visually-presented images, or whether the code arising in such areas changes dynamically. The temporal generalization methods we have adopted may, however, contribute to these questions if applied to datasets collected from electrodes situated in these regions in future work.
Why should a dynamic distributed code arise specifically within the vATL? The area is situated at the top of the ventral visual stream, but also connects directly to core language areas Nobre et al. It receives direct input from smell and taste cortices Gloor, , and is intimately connected with limbic structures involved in emotion, memory, and social cognition Gloor, Hub neurons interact with a wide variety of subsystems, each encoding a different kind of structure and content, potentially pushing the hub representations in different directions over time as activity propagates in the network.
Other network components lying closer to the sensory or motor periphery connect mainly within individual modality-specific systems Binney et al. For this reason, the feature-based approach that has proven indispensable for characterizing neural representations in modality-specific cortices may be less suited to understanding the distributed and dynamic representations that arise in deeper and more broadly-connected tertiary association cortical regions.
These include regions critical for human semantic knowledge, and potentially other higher-level cognitive functions. It is a deep, fully continuous and recurrent neural network that learns associations among visual representations of objects, their names, and verbal descriptors, via a central cross-modal hub, with units and connectivity shown in Figure 2A of the main paper.
All units employed a continuous-time sigmoidal activation function with a time-constant of 0. Visual and Verbal units were given a fixed, untrainable bias of —3 that produced a low activation state without positive input. Hidden units had trainable biases. The resulting changes in unit activations propagated through visual hidden, hub, and verbal hidden units to eventually alter activation states in the verbal units.
Because the model was reciprocally connected, such downstream changes fed back to influence upstream states at each moment of simulated time, as the whole system settled to a stable state. To simulate verbal comprehension, the same process unfolded, but with positive input externally provided to verbal units.
Units updated their activation states asynchronously in permuted order on each tick of time and were permitted to settle for five time intervals a total of 20 updates during training and eight time intervals 32 updates during testing. The model environment contained visual and verbal patterns for each of 90 simulated objects, conceived as belonging to three distinct domains e.
Visual patterns were constructed to represent each item by randomly flipping the bits of a binary category prototype vector in which items from the same domain shared a few properties and items from the same category shared many. The verbal patterns were constructed by giving each item a superordinate label true of all items within a given domain animal, object, plant , a basic-level label true of all items within a category e.
These procedures, adopted from prior work Rogers et al. The model was trained with backpropagation to minimize squared error loss. Half of the training patterns involved generating verbal outputs from visual inputs, while the other half involved generating visual outputs from verbal inputs.
The model was initialized with small random weights sampled from a uniform distribution ranging from —1—1, then trained for 30, epochs in full batch mode with a learning rate of 0. For each pattern, the settling process was halted after 20 activation updates, or when all Visual and Verbal units were within 0.
For all reported simulations, the model was trained five times with different random weight initializations. After training, all models generated correct output activations i. Each model was analyzed independently, and the final results were then averaged across the five runs.
The picture-naming study was simulated by presenting visual input for each item, recording the resulting activations across the 25 hub units at each update as the model settled over 32 updates, and distorting these with uniform noise sampled from —0. As the model settles over time it gradually activates the word unit corresponding to the item name, and in this sense simulates picture naming. Just as in the ECoG data, we recorded unit activations across a fixed period of time, regardless of when the correct name unit became active.
Note that, whereas the ECoG study employed items drawn from two general semantic domains living and nonliving , the model was trained on three domains. This provided a simple model analog to the true state of affairs in which people know about more semantic kinds than just those appearing in the ECoG stimulus set. To simulate the study, the model was presented with 60 items selected equally from two of the three semantic domains—so as in the study, half the stimuli belonged to one domain and half to another.
To ensure results did not reflect idiosyncrasies of one domain, we simulated the task with each pair of domains and averaged results across these. All analyses were conducted using R version 3. To visualize the trajectory of hub representations through unit activation space as a stimulus is processed, we computed a simultaneous three-component multidimensional scaling of the unit activation patterns for all 90 items at all 33 timepoints using the native R function cmdscale.
The resulting coordinates for a given item at each point in time over the course of settling were plotted as lines in a 3D space using the scatterplot3d package in R. Figure 2B shows the result for one network training run. Figure 2C shows the same trajectories in the raw data i. To simulate decoding of ECoG data, we evaluated logistic classifiers in their ability to discriminate superordinate semantic category from patterns of activity arising in the hub at each timepoint.
As explained in the main text, we assume that ECoG measures only a small proportion of all the neural populations that encode semantic information. We therefore sub-sampled the hub-unit activation patterns by selecting three units at random from the 25 hub units and using their activations to provide input to the decoder.
Classifiers were fitted using the glm function and the binomial family in R. A separate decoder was fitted at each time-point, and unit activations were mean-centered independently at each time point prior to fitting. We assessed decoder accuracy at the time-point where it was fitted using leave-one-out cross-validation, and also assessed each decoder at every other time point by using it to predict the most likely stimulus category given the activation pattern at that time point and comparing the prediction to the true label.
This process was repeated 10 times for each model with a different random sample of three hub units on each iteration. The reported results then show mean decoding accuracy averaged over the five independent network training runs, for decoders trained and tested at all 33 time points. The above procedure yielded the decoding accuracy matrix shown as a heat plot in Figure 3B. Each row of this matrix shows the mean accuracy of decoders trained at a given timepoint, when those decoders are used to predict item domain at each possible timepoint.
The diagonal shows hold-out accuracy for decoders at the same time point when they are trained, but off-diagonal elements show how the decoders fare for earlier below diagonal or later above timepoints. Decoders that perform similarly over time likely exploit similar information in the underlying representation, and so can be grouped together and their accuracy profiles averaged to provide a clearer sense of when the decoders are performing well.
To this end, we clustered the rows of the decoding accuracy matrix by computing the pairwise cosine distance between these and subjecting the resulting similarities to a hierarchical clustering algorithm using the native hclust function in R with complete agglomeration.
We cut the resulting tree to create 10 clusters, then averaged the corresponding rows of the decoding accuracy matrix to create a temporal decoding profile for each cluster lines in Figure 3C. We selected 10 clusters because this was the highest number in which each cluster beyond the first yielded a mean classification accuracy higher than the others at some point in time. Similar results were obtained for all cluster-sizes examined, however. Finally, to understand the time-window over which each cluster of decoders performs reliably better than chance, we computed a significance threshold using a one-tailed binomial probability distribution with Bonferroni correction.
Each decoder discriminates two categories from 60 items, with probability 0. The barplot in Figure 3D shows the proportion of the full time window during which each decoding cluster showed accuracy above this threshold. Eight patients with intractable partial epilepsy seven or brain tumor one originating in the left hemisphere participated in this study.
These include all left-hemisphere cases described in a previous study Chen et al. Background clinical information about each patient is summarized in Table 1.
A total of 16—24 electrodes mean 20 electrodes covered the ventral ATL in each patient. The subdural electrodes were constructed of platinum with an inter-electrode distance of 1 cm and recording diameter of 2.
ECoG recording with subdural electrodes revealed that all epilepsy patients had seizure onset zone outside the anterior fusiform region, except one patient for whom it was not possible to localize the core seizure onset region. Participants all gave written information consent to participate in the study. One hundred line drawings were obtained from previous norming studies Barry et al. A complete list of all items can be found in Chen et al.
Living and nonliving stimuli were matched on age of acquisition, visual complexity, familiarity and word frequency, and had high name agreement. Independent-sample t-tests did not reveal any significant differences between living and nonliving items for any of these variables. We computed these similarities for all pairs of images. In the resulting matrix, each image corresponds to a row-vector of similarities.
Mean accuracy across folds was 0.
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