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My Confession for Today

Proceed to Basket. View basket. Continue shopping. Title: sharpened out existence. Results 1 - 13 of United Kingdom. Seriously, if you're gonna do anything, do it with verve, intention, and the appropriate amount of pageantry. Apr 16, Taylor Fisher rated it really liked it. I approached this book with a bit of trepidation due to being a mechanical pencil man. By the time I had gotten to the advice on mechanical pencils presented I was already convinced to pick up the classical wooden pencils of my youth.

View all 4 comments. Oct 03, Ava marked it as to-read. Dec 06, Snotchocheez rated it really liked it. I really needed a palate-cleanser, and David Rees penner of the long-running on-line strip Get Your War On truly delivered with this How-To pencil sharpening guide. What's so great about it is you're never quite sure just how serious to take Rees: is he really that obsessed over pencils and the art of sharpening them?

Well, yeah, he just might be! Step-by-step instructions replete with author photos demonstrate exactly how to get your point on: from the appropriate finger- and arm-stretching p I really needed a palate-cleanser, and David Rees penner of the long-running on-line strip Get Your War On truly delivered with this How-To pencil sharpening guide. Step-by-step instructions replete with author photos demonstrate exactly how to get your point on: from the appropriate finger- and arm-stretching pre-sharpening techniques, to the appropriate gear be it a simple single-blade sharpener or a full-bore double-cylindered wall mount hand crank , Rees has got you covered.

Electric sharpeners get short-shrift in this how-to, alas. As do mechanical pencils, obviously. But if you ever find the need to sharpen your 2 whilst standing under a waterfall, or entertain your cubicle-mates with Celebrity Pencil Sharpening impressions, this guide is invaluable! View all 3 comments. Jul 17, Ryan Chapman rated it it was amazing Shelves: fiction , favorites-of One of the smartest and funniest books to appear in recent memory, as brilliant a high-wire literary performance as any of the well-reviewed debut novels populating the Times Book Review.

It's a testament to this book's originality that it escaped most review coverage, as How to Sharpen Pencils seems to operate completely outside of the publishing industry. I kind of wish I were in grad school just so I could devote a month to studying the book. I'd pair it with another recent work, Mark Leyner' One of the smartest and funniest books to appear in recent memory, as brilliant a high-wire literary performance as any of the well-reviewed debut novels populating the Times Book Review. I'd pair it with another recent work, Mark Leyner's The Sugar Frosted Nutsack , for creating a new kind of structure for humor writing, one utterly current and fresh.

Rees's conceit improbably holds up for most of the book, only to explode in the last chapter "How to Sharpen Pencils With Your Mind" which simultaneously subverts and strengthens the entire endeavor. Feb 05, February Four rated it it was amazing. Turn your brain off, and this is a fantastically written manual on how to sharpen pencils with thoughts on mechanical pencils and electric sharpeners, as well as the pitfalls of being an artisanal pencil sharpener.

Turn your brain on, and this is a fantastically written manual on how to sharpen pencils with thoughts on mechanical pencils and electric sharpeners, as well as the pitfalls of being an artisanal pencil sharpener. Satire never had it so good. May 19, James Williams rated it really liked it. There is a point in a middle-class existence where one looks around at all of the chintzy mass-produced garbage which so thoroughly fills our life and wonders -- desperately -- if there can't be something just a bit more refined.

Something just a bit more real. And so we turn to good whisk[e]ys and wines. Or we turn to German sports cars that we can't really afford. Or we build a woodshop in the garage and slowly drive ourselves mad chasing the craftsmanship that our grandfathers were unable to There is a point in a middle-class existence where one looks around at all of the chintzy mass-produced garbage which so thoroughly fills our life and wonders -- desperately -- if there can't be something just a bit more refined.

Or we build a woodshop in the garage and slowly drive ourselves mad chasing the craftsmanship that our grandfathers were unable to pass on to us through our ill-gotten haze of wasted Saturdays filled with nothing more than pop-rocks and cartoons. One place that I have turned to fill this hole in my life is well-made writing instruments.

There is much joy and humanity to be found in placing the tip of a fountain pen to a good sheet of paper or in turning a perfectly-crafted wooden pencil in a fine German single-blade sharpener. And it's this experience which is the subject of this book which is at the same time a reference book, a how-to guide, and a meditative spiritual tract. Because sharpening a pencil is not just about moving as quickly as possible from "a yellow stick" to "a thing one can mark paper with".

It is about that, true. The functionality of a well-sharpened pencil is key. But it's also about the texture of the paint under your fingertips. It's also about the heft of the pencil in your hand. It's also about the smell of the freshly released cedar as you slowly remove everything that isn't a sharpened pencil. Sharpening a pencil is a full-sense task. And, as such, it is a task that should be taken up with the utmost care lest you waste another moment on this planet without actually seeing any of it.

While instructional, this book is also very funny with charts and footnotes lightening the mood on almost every page.

Sharpened Out Of Existence

While I ultimately disagree with Mr. Rees' assessment of those tools, he made his argument passionately and persuasively. I think it's also important to note the design of the physical book as well. It is a classic work that leans strongly on Futura. Every chapter heading, every sub-heading stands out as something worth remarking upon. I normally read electronic books but in this case, I highly recommend purchasing a paperback to hold in your hand. It is a worthwhile exercise and experience.

Sep 11, Diane rated it really liked it. I am eternally grateful to David Rees for, dare I say, penciling this book. I am known as the sole mechanical pencil sharpener aficionado at my school. Students delight in the opportunity to hand crank their pencils sharp at my wall mounted sharpener. I am very proud to declare that when the PTO gave every teacher a brand new electric sharpener that I didn't even let it in the room. I donated it to the teacher work room. The sledge hammer was a bit too harsh for my tastes. But the great news is t I am eternally grateful to David Rees for, dare I say, penciling this book.

But the great news is that all the teachers know they can donate their dead electric sharpeners to me for my engineering students to demolish when they quit working. So all is well and relatively balanced in my pencil sharpening world. David Rees - You make me think. You make me laugh. You make me wish that a few bits were a bit "cleaner" for my middle school classroom so I could put this book on my book shelf. I am emboldened to bring your book in for a teacher read aloud when I find the need to teach "how to write instructions.

May 14, Bryan Hall rated it liked it. A straight-faced look at how to sharpen pencils, using a variety of different sharpeners the machines , as well as a good bit of the philosophy of a pencil sharpener the person. I'm all for ironic examinations of pedestrian subjects that pretend that they are wildly interesting, but when they're not you have to throw in a few more jokes.

The first half of this book is the serious technical writing that it pretends to be, and shows that Rees truly A straight-faced look at how to sharpen pencils, using a variety of different sharpeners the machines , as well as a good bit of the philosophy of a pencil sharpener the person. The first half of this book is the serious technical writing that it pretends to be, and shows that Rees truly cares about this and isn't just making a quick hipster joke book He lightens the mood in places, just not nearly enough. By the time he runs out of actual pencil-sharpening material, he starts making it ridiculous for the second half of the book with celebrity impression pencil sharpening, trick sharpening behind the back, etc.

I came in expecting to like this, having seen Rees perform some of it on-stage with John Hodgman and Wyatt Cenac, but it just didn't work for me nearly enough. Jun 15, Tracey Baptiste rated it it was ok. As soon as I read about this book, I pre-ordered it for my Nook. A few days later it was on my device, and I started reading. The premise is great, but there's only so far you can stretch a joke. By the middle of the book I was completely bored, which is why it took me so long to finish it. It became treadmill reading, so I only got a few pages in at the gym, and there were lots of days I didn't bother to read it, and watched t.

I'm glad I didn't buy it in physical form. If you're rea As soon as I read about this book, I pre-ordered it for my Nook. If you're really interested, borrow it from the library. It's not a keeper. We also noticed that the fully connected layers DNN6—8 had more pronounced positive feature gains than the convolutional layers. One possible cause for this result is the training scheme of the decoders that only used natural nonblurred images. This could have biased the output features to those resembling natural images.

In this case, the features could be correlated to any natural image features. We investigated this possibility by measuring the content specificity of the predicted features. This was then compared with the mean correlation of the same predicted features, but with the original features of different images. This measure provided information on how tightly the predicted features were associated with the presented stimulus content, as opposed to natural images in general. Content specificity of decoded features with blurred images.

Different images correlation indicates the mean of correlations of the same predicted features with original image features of different images. The mean correlation is shown for different DNN layers. As mentioned above, the DNN model used in this study implements hierarchical processing that is synonymous with that happening in the visual cortex. Previous studies have shown homology between the features of the DNNs and the representations in the visual cortex Cadieu et al. To this point, we have shown the results of features predicted from the collection of all denoted visual areas VC.

We further investigated the separate visual areas of the lower, intermediate, and higher visual areas, to examine the homology between the feature gain and the visual cortex hierarchy Fig. We showed that the feature gain also shows similar homologies to the visual hierarchy, in that we could observe that shallower DNN layers showed larger feature gain from the lower visual areas V1—3 , while deeper DNN layers showed larger feature gain from the higher visual areas LOC, FFA, and PPA.

However, DNN1 did not show significantly positive feature gains. These results imply that feature gain also follows the same visual homology in the visual cortex areas, and that the top-down effect is more pronounced in higher visual areas. Feature gain across visual areas. Feature gain for features predicted from different visual areas. In the previous analyses, the data from different experimental conditions were pooled together. We then further investigated the difference between the category-prior and no-prior conditions.

We compared the feature gain means grouped according to the experimental condition category-prior vs. This result indicates that addition of prior information enhances top-down modulation, thereby causing an increase in feature gain. This implies augmented sharpening of neural representations. Effect of category prior. Feature gain for features predicted from different visual areas grouped by experimental condition category-prior vs. This result, however, pooled both correctly and incorrectly reported results.

When considering behavioral data, there are considerable differences between category-prior and no-prior conditions. The category-prior condition was characterized by a higher number of correct responses of total instances for 5 subjects compared with the no-prior condition 92 of total instances for 5 subjects. However, in the category-prior condition, the task was to choose 1 of 5 categories. In some cases when the stimulus was highly degraded, the best guess response by the subjects could be random.

To attempt to curb this problem, we could use the certainty level as an indicator of correctness, especially for the category prior. We found from the behavioral results that nearly all the trials labeled as certain were also correctly recognized category-prior: of certain trials were correct; no-prior: 57 of 70 certain trials were correct. This further supports the observation that adding priors aids recognition.

We further analyzed our data by grouping it according to both experimental condition category-prior and no-prior and recognition performance correct and incorrect. We show the results of the mean feature gain over subject means for each DNN layer in Fig. From these results, we notice that the effect of recognition leads to a very faint enhancement in the feature gain. Effect of behavioral performance. Legends include the total number of occurrences of each response across subjects.

We also analyzed our data by grouping it according to certainty level certain and uncertain. We show the results of mean feature gain over subject means for each DNN layer of this analysis in Fig. For the category-prior condition, when an image was recognized with certainty, we found significant enhancement in feature gain in DNN5, while for the no-prior condition, significant enhancement was found in DNN1 and 7.

Effect of confidence level.

Questions to Answer

From the results of Figs. However, we also found a considerable feature gain even without recognition that indicates a sharpening effect not guided by subjective recognition.

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This could be caused by a lower-level sharpening associated with local similarity or object component sharpening that could be common across different objects such as body parts in animals. In this study, we have demonstrated sharpening of the neural representations of blurred visual stimuli. This sharpening can assist the visual system in achieving successful prediction. It originates from endogenous processing elicited by top-down projections or recurrent connections or both in the visual cortex.

Compared with pure-feedforward behavior, the neural representations of blurred images tended to be biased toward those of corresponding original images, although the original images had not yet been viewed. This sharpening effect was also found to follow a visual hierarchy similar to that in the visual cortex. We found that this sharpening was content-specific, and not just due to a natural image bias. It was also shown to be boosted by giving category information to the subject before stimulus viewing.

This indicated that adding a more specific prior leads to further sharpening of the neural representations. However, we did not find that recognition had a strong role in boosting the enhancement process. In our experimental protocol, the subjects viewed blurred stimuli in randomly organized sequences. In each sequence, different levels of blur of the same image were shown, ordered from the most blurred to the nonblurred stimulus Fig.

This ensured that subjects did not have pixel-level information.


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Conversely, the feedforward behavior demonstrated by the noisy DNN output showed an opposite tendency Fig. We computed the feature gain to investigate how the predicted DNN features deviated from pure feedforward behavior. Feature gain analysis showed that the predicted features are rather correlated with the original image features Fig. This indicates that a sharpening effect happens across the visual cortex, leading to a more natural-image-like neural representation. This effect could be caused by the nature of image degradation. Image blurring tends to conceal localized details in favor of the global shape information.

This could lead to the subject attempting to recognize the global object while ignoring localized details. If we consider the stimulus sequence from the most blurred stimulus to the original image stimulus, we could visualize the time scale of the top-down effect where deeper layers peak earlier than shallower ones.

This could be one effect of our image presentation protocol where the subject is accumulating evidence at each level starting from global shape evidence followed by localized details to confirm their concordance with the global shape evidence. This representation was also confirmed as not being due to a natural-image bias caused by the decoder training dataset, which consisted of natural unaltered images Fig. These results are in line with previous studies showing that neural representations are improved due to a top-down effect Lee and Mumford, ; Hsieh et al.

Kok et al. Gayet et al.

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The reverse hierarchy theory also suggests that top-down modulation serves to fine-tune sensory signals by means of predictions initially made using lower spatial frequency features Hochstein and Ahissar, ; Ahissar and Hochstein, Furthermore, Revina et al. Our analysis also shows that feature gain follows a similar hierarchy to the visual cortex Fig. This indicates that the sharpening process occurs in the same hierarchical processing localization as normal processing where low-level sharpening occurs in lower visual areas and enhancement of higher-level features occurs in the higher visual areas.

It could be indicative of a convergent mechanism by which bottom-up and top-down pathways are integrated into a single neural representation of the stimulus. This suggestion could be supported by previous reports on the prediction of visual features. Horikawa and Kamitani a demonstrated that visual perception and mental imagery yielded feature prediction that was homologous with that of the visual cortex hierarchy.

Horikawa and Kamitani b also showed similar results from dream-induced brain activity. Earlier studies showed strong representational similarities between the deeper layers of DNN and the brain activity in the inferior temporal cortex IT; Cadieu et al. This could be further investigated by high-resolution imaging to reveal the layered structures in the visual cortex and analyze the neural representations in each layer.

We demonstrate that top-down effects also show similar homology, thus suggesting that DNN-based methods are useful for studying visual top-down pathways, since they can reveal the localization of the sharpening by means of DNN layer feature gain. When we added a category-prior to the task, the number of competing categories for recognition decreased; thus the subjects tended to have a more directed top-down effect, due to the fewer number of competing stimuli Bar and Aminoff, This led to a higher feature gain that was especially noted in the higher layers Fig.

This further supports the idea of neural representation sharpening when given a prior describing the stimulus content, as the top-down signal would be more correlated with the correct recognition results, thus leading to a stronger feature gain. We also found that when subjects successfully recognized the image content, the feature gain in some layers predicted from lower visual areas was significantly improved.

However, this was not salient as a general trend Figs. It was expected that recognition would lead to a boost in feature gain from the sensory competition perspective, as the subject would attend to the successfully recognized object in the stimulus image, leading to a directed top-down effect Moran and Desimone, ; Kastner et al. Hsieh et al.

Our results could be justified by the findings in de-Wit et al. Revina et al. In the prediction error realm, successful prediction would lead to zero error in the higher visual areas, and thus feature gain would decrease. Our results show an opposite, albeit weak, effect, which nonetheless supports the representation-sharpening rather than prediction error hypothesis.

Thus, prediction error mechanisms do not appear to be in operation when stimuli are blurred or they could be calculated and used as the sharpening signal as proposed in Kok et al. The sharpening effect without recognition may be driven by more localized and lower-level feature mechanisms. These mechanisms would enhance features corresponding to local components of the main objects that were common across many objects e. These local enhancement effects could lead to different recognition results.

This was shown to be true for computer vision DNN-based deblurring algorithms, where the results of the enhancement process can lead to different results, according to the desired object Bansal et al. From these results, we can deduce that top-down modulation is in operation when visual input is degraded, even in the absence of a memory or expectation prior.

Previous studies have proposed that the brain makes an initial processing step using low-spatial-frequency information. This step generates predictions of the content of the image in the orbitofrontal cortex; these predictions are then used to drive the top-down modulation effect Bar and Aminoff, ; Bar et al. This top-down effect comes about in the form of sharpening of neural representations resulting from viewing degraded images.


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  • The mechanisms by which this effect materializes have been mostly overlooked in previous literature, due to the difficulty in finding a baseline for measurement. There has been more focus on the source of this top-down modulation effect than on how it materializes in the visual cortex Bar et al. As we demonstrate here, the DNN representations could offer a plausible proxy for representing brain activity and for attaining a pure-feedforward baseline that can be used for measuring top-down effects. The illustrated enhancement was shown to be affected by the presence of prior semantic information, leading to a boost in the enhancement effect that was more visible in higher-level features.

    On the contrary, successful recognition did not also cause an overall boost in neural representation enhancement. Our results contribute to the long-standing question of how top-down and recurrent pathways affect bottom-up signals to achieve successful perception, which is believed to cause the hallucinatory symptoms associated with psychological disorders such as schizophrenia when balance is disrupted Friston et al.

    Moreover, our stimulus presentation protocol could be used to test more comprehensive models of decision-making under accumulation of evidence tasks Platt and Glimcher, We have examined the question from a more general perspective of vision, which has allowed us to achieve a more comprehensive understanding of the vision process. We also thank the members of Kamitani Lab for their valuable comments on this manuscript. Decisions are customarily a result of the Reviewing Editor and the peer reviewers coming together and discussing their recommendations until a consensus is reached.

    When revisions are invited, a fact-based synthesis statement explaining their decision and outlining what is needed to prepare a revision will be listed below. The following reviewer s agreed to reveal their identity: Topi Tanskanen. The manuscript addresses an important question of top-down modulation and predictive signals in visual perception.

    Authors combine fMRI recordings and deep neural network modeling in novel ways that can be expected to stimulate discussion and further studies. There are three main issues to address, or be more cautious about. First, the distinction between prediction error and sharpening. As Reviewer 1 points out, predictive completion could be important for other types of image degradation other than blurring that is tested here. Second, as Reviewer 2 points out, can it be truly stated that the effects are due to top-down signals as opposed to recurrent e.

    If not, then perhaps it would be appropriate to revise how results are presented, including the title. Finally, multi-test statistics mentioned by Reviewer 2 are very important to address. This term has different meaning in different areas of neuroscience. Enhancement of DNN representation gain by prior category information is interesting and convincing. Also, sharpening in DNN representations even with incorrect answers is intriguing, which indicates existence of rather mechanical, subconscious sharpening. Overall, this would be a good paper advancing the visual processing study and, in general, that of environmental acquisition and world representation in brain, with a help of deep learning model, which may have potential to advance the field in new ways as a tool.

    Same image translated in x-y, rotated, or with missing or extra parts, might serve better for such evaluation since detection of differences from preregistered images could be too convoluted process for VC if blurring was the primary difference. Blurred images were shown to the subjects in a rigorous, chronological way from the most to the least and not blurred.

    Effects of such dynamic sharpening in stimulation on dynamic perception formation could be addressed. Please clarify. Related to 1 above, sharpening and error prediction may not be exclusive each other in top-down operations why not to co-operate, for example. Since rejection of the latter might not be so explicit 1 , the paper could focus on sharpening and related, and put error prediction as a side talk if preferred.

    Figure 1B could be more detailed. DNN layers on the very right could be bigger and be showing feedforward-only connection with arrows, convergent ones in first layers and fully connected ones in the last layers, somehow schematically in larger panels, for example. The present double lines for the last layer does not make visual sense or need an explanation.