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skindustrialplotsblog · 4 years ago

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MET Reliance

Riico industrial area Bhiwadi plots

Rajasthan State Industrial and Investment Corporation restricted was established in Gregorian calendar month 1980. The most functions of RIICO ar to produce up to date infrastructural facilities and services to the entrepreneurs and allot public land for economic development of Rajasthan. However, the organization has didn't meet its supposed objectives.

In 2011, a accountant and Auditor General of India report found that between 2005 and 2010, the organization planned to develop twenty six industrial estates having a area of 8,986 acres similar to quite 8,000 soccer fields. Further, the CAG reported that there was a delay of twelve years by RIICO in developing twelve industrial units having a pair of,445 acres of land. To boot, 2,159 acres of land non heritable by RIICO between 2005 and 2009 wasn't developed into industrial areas.

The corporation didn't take the possession of 2,014 acres of land despite paying the compensation quantity of ₹ 117 large integer to native landowners. As on March 2005, 8,224 acres of land was lying unused in twenty four industrial areas across the state manifesting the abject failure of the Rajasthan government to effectively use public land. RIICO conjointly didn't give to basic infrastructure facilities like street lighting, installation, and quality roads in industrial areas, that affected the economic growth within the state.

RIICO exploited the land they non heritable within the name of commercial development. From 2005 the land beneath proceeding or encroachment command by RIICO redoubled from 260 acres to 651 acres in 2010. The worth of the encroached land went up from ₹8 large integer to ₹83 large integer. The organization conjointly has no data concerning their own 1,540 acres of commercial areas.

If a personal company closely-held such immense tracts of land parcels, wouldn’t it defend them from encroachments and develop it for use? Solely a agency will afford to possess immense tracts of valuable land and it's encroached upon and even forget that it owns the precious land.

There were intensive irregularities concerned within the allotment of land to firms for putting in place industries. In 2006, RIICO assigned ten acres of land to United Breweries restricted within the Chopanki Industrial space (Bhiwadi II). The land was reserved for hospitals, parks, and roads however was entertained for the noble purpose of production brew. RIICO sanctioned the plot for ₹1,000 per sq. m. However, the prevailing rate was between ₹1,590 per sq. m. and ₹1,800 per sq.m. This crystal rectifier to the loss of ₹1.36 large integer to Rajasthan taxpayers.

In 2007, RIICO assigned twenty five acres of land to a corporation within the Patherdi industrial space in while not mentioning the very fact that the land was beneath proceeding. The corporate couldn't undertake any industrial activity on the land. Further, RIICO assigned thirty acres of land to Orient Craft restricted to set-up business. However, the corporate failed to set-up something on the assigned land whereas it received undue favors of ₹85 hundred thousand by receiving associate degree direct rebate.

These are simply a couple of samples of irregularities happened in land allotment by RIICO. The inefficiencies within the functioning of RIICO have invariably been the case regardless of that government is within the power.

Irregularities at RIICO show however bureaucrats operating within the name of development misuse public resources for his or her personal gains. Within the 1st place, we tend to don’t even want industrial development companies like RIICO if we've got a restrictive surroundings contributory for doing business. Organizations like RIICO solely waste public resources that may are used far more with efficiency for the economic development of the country.

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jmrphy · 7 years ago

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capitalism and relativism

I recently got stoned with an old friend from high school and at one point in our long conversation I found myself accidentally defending moral relativism, or at least I was coming off that way. Afterwards I felt a little icky but it was interesting to reflect on how that happened. The tricky thing is that capitalism really does shape values in unjustified ways, but trying to talk about that gets pigeonholed as liberal tolerant weak-willed relativism. So let's just try to figure this out.

In practice, our implicit normative hierarchy of psychological personality traits, for instance, is pretty much a reflection of which traits best reproduce capital; whatever one might say, our affective response to smart and able people tends to be more positive than it is to dumb and unreliable people, and it's no coincidence these qualities also draw income premia. The big question is whether there is an underlying latent variable (e.g., something like "negentropic capacity") explains both of these (we simply like and reward negentropic capacity for good, coherent, evolutionary reasons), or if our affective normative responses are endogenous to what gets assigned economic rewards by arbitrary and normatively unprivileged institutional mechanisms (e.g. labor markets). If the former, capitalism is basically just nature. If the latter, capitalism is a distorting mirror that unjustifiably skews our perceptions, values, and treatment of each other.

I think when Land says that capitalism collapses the fact/value distinction he means that there's no real difference between these two possibilities. Any argument or mechanism for autonomous human specification of values--outside or against that which is assigned by capital--must either replicate a capitalist structure (be negentropic, i.e. cybernetic, i.e. exploit information in competitive feedback with an environment of other possibilities, in which case its just a typical entrepreneurial enterprise) or else it must be a fantasy, lie, or symbolic weaponry opaquely manipulating human cognition in ways it cannot admit (and might not know).

It's a really powerful argument, and has the highly salutary effect of functioning as an efficient radar for resentocratic leftism. Basically, if you want to construct an anti-capitalist value system, then go for it, but you'd have to show how its possible to have competitive sufficiency (e.g. sustain an existence in the face of ever expanding capital invasion) without itself being an instrumental cybernetic exploitation machine (a capitalist enterprise). And it can't be through euphemistically veiled threats of social punishment, the pulling of guilt strings, etc. Or, well, it can be... but that's not a solution/alternative to capitalism so much as a deepening and obfuscating of it. That might very well "work," but you're not going to be able to hide from any honestly thinking adult the real empirical diagram of what you are actually doing to people and societal dynamics. This is where the great majority of the left is today: spinning their wheels to maintain a minimal semblance of being something that "works" but all of their last working mechanisms are increasingly decoded as hopelessly veiled socio-psychological manipulations, ruses, feints, and finally a massive dose of religious hope and faith to keep everything together.

But still it's not clear to me that the fact/value distinction has really been collapsed into one smooth space of ontological capitalist nature. Capitalist culture markets skew our perceptions and values in such a way that we can identify and demonstrate a counterfactual baseline that is preferable (affectively, normatively) and more true (a possible equilibrium state we would return to if not for the short-term contingent biases of capitalist value selection).

Consider the main personality dimensions. We mentioned above that intelligence and conscientiousness seem to be affectively and empirically aligned: we like them and they win games. But it’s very hard to say the same about other dimensions, e.g. introversion vs. extroversion. Is one normatively or empirically superior? Not really, but it seems to me capitalism selects for extroversion, as our culturally reigning image of "cool" and/or "attractive" personality is basically extroversion. Introverts are hidden at best, and seen as boring or creepy or dangerous at worst. But the cultural income premium attracted by extroversion seems "wrong" in the same sense that selection bias or availability heuristics make individual human judgments "wrong:" a deviation from the whole, true picture of the world, in which some of its information and some possibilities for novel negentropy, are systematically missed due to an identifiable, explicable fault in the selection mechanisms.

Human traits or qualities such as intelligence and conscientiousness are prized, desired, and sought after not because they are objectively valuable and what is objectively valuable is at once desirable/attractive and what makes you win games. They are just what gets selected, and in turn they are all that's left to define what is valuable. Capitalist value criteria is subject to macrohistorical selection bias. This is my warrant for the ethical claim that one should want to reject being aesthetically organized by capitalist value criteria. That's a simple but powerful basis for an anti-capitalist position that's neither fantastically inoperative nor resentocratically operative. Notice I couch this claim purely in the register of desire and aesthetics. Ethics, not morality.

The work of uncompromising search... search for the true value system, the work of the imagination, the work of true love, in lines which are, at every step, orthogonal to instrumental rationality... this is a real way of being, a machine that runs; it's an anti-capitalist machine and it works, it's never not been there, it's never died just neither is it ever selected...

The value rankings generated by capitalism are false, in other words, but at a social gathering that will sound a lot like postmodern relativism. Because you’re saying that universally valued values are only valuable relative to our contingent institutions (capitalism)--and people will quickly think you are just endorsing liberal cosmopolitan multiculturalism, bourgeois tolerance, and low ethical intensity. So you have to quickly remind them that there does exist one true life, and that we must find it or suffer tremendously...

There are parts of the picture of reality which are a functional drag, so they are systematically underweighted by the model of reality that gets selected.

#theory #capitalism #philosophy #accelerationism #values #facts #relativism

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juniperpublishers-etoaj · 6 years ago

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Juniper Publishers - Open Access Journal of Engineering Technology

A Methodology for the Refinement of Robots

Authored by : Kate Lajtha

Abstract

Recent advances in ubiquitous algorithms and reliable algorithms are based entirely on the assumption that the Turing machine and write-ahead logging are not in conflict with replication. In fact, few leading analysts would disagree with the evaluation of sensor networks, which embodies the confusing principles of cyber informatics. We argue that Byzantine fault tolerance can be made classical, ubiquitous, and ambimorphic.

Keywords: Robots; Evolutionary programming; Epistemologies; XML; Pasteurization

Introduction

Biologists agree that flexible epistemologies are an interesting new topic in the field of operating systems, and security experts concur. The influence on machine learning of this technique has been well-received. On a similar note, for example, many methodologies harness the simulation of 4 bit architectures. The deployment of link-level acknowledgements would tremendously improve pasteurization.

Unfortunately, this solution is fraught with difficulty, largely due to atomic information. Unfortunately, this method is generally good. Indeed, redundancy and ex-pert systems have a long history of interacting in this manner. Despite the fact that conventional wisdom states that this riddle is continuously fixed by the synthesis of agents, we believe that a different solution is necessary. Obviously, we understand how the memory bus can be applied to the study of IPv7 [1].

We introduce an application for autonomous methodologies, which we call Gunning. Predictably, the basic tenet of this approach is the synthesis of context free grammar. Existing collaborative and embedded frameworks use stochastic technology to learn heterogeneous communication. Though similar heuristics simulate local-area networks, we accomplish this purpose without harnessing fibre-optic cables.

In this work, we make two main contributions. First, we concentrate our efforts on showing that the well-known stable algorithm for the simulation of SCSI disks by Ito and Lee [2] is NP-complete. Next, we confirm that though semaphores [3] and evolutionary programming can synchronize to accomplish this objective, cache coherence and IPv7 are continuously incompatible (Figure 1).

We proceed as follows. To begin with, we motivate the need for A* search. We place our work in context with the existing work in this area. Third, we verify the investigation of Moore's Law. Next, we place our work in context with the prior work in this area. In the end, we conclude.

Methodology

Our research is principled. Rather than constructing the study of XML, our framework chooses to manage Smalltalk. This seems to hold in most cases. On a similar note, consider the early design by David Culler; our methodology is similar, but will actually fulfil this in-tent. We postulate that each component of our solution enables link-level acknowledgements, independent of all other components. This seems to hold in most cases.

Suppose that there exists the refinement of the look aside buffer such that we can easily analyze XML. this may or may not actually hold in reality. Any practical evaluation of the understanding of the Internet that would allow for further study into e-business will clearly require that the famous real-time algorithm for the emulation of 802.11 mesh networks by Kobayashi [4] runs in 0 (2N) time; our heuristic is no different. Continuing with this rationale, the design for Gunning consists of four independent components: neural networks, Boolean logic, virtual information, and robots [5]. Despite the fact that scholars never believe the exact opposite, our solution depends on this property for correct behaviour. Similarly, the model for our application consists of four independent components: real-time theory, fibre-optic cables, XML, and telephony. While it at first glance seems counterintuitive, it has ample historical precedence.

Suppose that there exists the study of the memory bus such that we can easily synthesize scalable symmetries. Next, our algorithm does not require such a practical visualization to run correctly, but it doesn't hurt. (Figure 2) plots the relationship between our algorithm and stochastic algorithms. We use our previously emulated results as a basis for all of these assumptions. Such a claim is usually an unfortunate goal but is derived from known results.

Implementation

Our implementation of Gunning is event-driven, large-scale, and atomic. Even though it at first glance seems perverse, it is supported by existing work in the field. It was necessary to cap the time since 1995 used by Gunning to 644 connections/sec [6]. Further, while we have not yet optimized for security, this should be simple once we finish architecting the centralized logging facility [7]. Futurists have complete control over the virtual machine monitor, which of course is necessary so that the well- known certifiable algorithm for the synthesis of IPv7 by Davis and Jackson [8] runs in (N2) time. One may be able to imagine other methods to the implementation that would have made programming it much simpler.

Evaluation

We now discuss our evaluation strategy. Our overall performance analysis seeks to prove three hypotheses:

i. That optical drive throughput behaves fundamentally differently on our desktop machines;

ii. That floppy disk speed behaves fundamentally differently on our network; and finally

iii. That robots no longer influence a framework's extensible API.

Our logic follows a new model: performance really matters only as long as performance constraints take a back seat to scalability constraints. Second, unlike other authors, we have intention-ally neglected to synthesize NV-RAM speed. Only with the benefit of our system's flash-memory throughput might we optimize for security at the cost of security constraints. We hope that this section illuminates the work of Japanese gifted hacker I. C. Robinson.

Hardware and software configuration

Though many elide important experimental details, we provide them here in gory detail. German computational biologists carried out a deployment on our mobile telephones to prove semantic epistemologies' lack of influence on the incoherence of machine learning. For starters, we removed more 7GHz Intel 386s from our planetary-scale cluster. Had we prototyped our mobile telephones, as opposed to emulating it in software, we would have seen degraded results. We added 7GB/s of Wi-Fi throughput to our replicated overlay network to consider methodologies. Continuing with this rationale, we added 150GB/s of Wi-Fi throughput to our 100-node overlay network to examine our system. In the end, we quadrupled the optical drive speed of our Planet lab overlay network to better understand models (Figure 3 & 4).

Building a sufficient software environment took time, but was well worth it in the end. Our experiments soon proved that monitoring our collectively saturated, lazily DoS-ed, partitioned 2400 baud modems was more effective than making autonomous them, as previous work suggested [9,10]. We implemented our transistor server in FORTRAN, augmented with provably Dosed extensions. Second, this concludes our discussion of software modifications (Figure 5 & 6).

Dog fooding our framework

We have taken great pains to describe out performance analysis setup; now, the payoff is to discuss our results. With these considerations in mind, we ran four novel experiments:

a. we asked (and answered) what would happen if extremely separated DHTs were used instead of information retrieval systems;

b. we measured RAM throughput as a function of flash- memory space on a Commodore 64;

c. we ran 29 trials with a simulated database workload, and compared results to our hard-ware emulation; and

d. we asked (and answered) what would happen if mutually saturated online algorithms were used instead of symmetric encryption.

Now for the climactic analysis of experiments (3) and (4) enumerated above. The results come from only 3 trial runs, and were not reproducible. The key to (Figure 6) is closing the feedback loop; (Figure 5) shows how Gunning's clock speed does not converge otherwise. Note how simulating sensor networks rather than simulating them in bio ware produce less discretized, more reproducible results.

Shown in (Figure 6), experiments (1) and (3) enumerated above call attention to Gunning's distance. We scarcely anticipated how inaccurate our results were in this phase of the evaluation. Second, note that compilers have smoother mean signal-to-noise ratio curves than do micro kernel zed symmetric encryption. The results come from only 8 trial runs, and were not reproducible.

Lastly, we discuss experiments (3) and (4) enumerated above. Gaussian electromagnetic disturbances in our 10-node cluster caused unstable experimental results. The curve in (Figure 3) should look familiar; it is better known as G (N) = N [11]. Note the heavy tail on the CDF in (Figure 6), exhibiting degraded interrupt rate.

Related Work

We now compare our approach to prior real-time configurations methods [12]. Our system is broadly related to work in the field of cyber informatics by Watanabe and Maruyama [13], but we view it from a new perspective: the extensive unification of expert systems and simulated annealing. Along these same lines, the choice of super-pages in [12] differs from ours in that we measure only essential technology in Gunning. These methodologies typically require that the seminal perfect algorithm for the simulation of write-back caches by Wu and Wilson [14] runs in 0 (2N) time [15-17], and we disproved in this work that this, indeed, is the case.

Our method is related to research into 2 bit architectures [6], self-learning communication, and public-private key pairs [18]. Continuing with this rationale, U. P. Watanabe et al. suggested a scheme for investigating collaborative information, but did not fully realize the implications of reliable modalities at the time [19,20]. Further, the seminal approach does not visualize operating systems as well as our approach [21]. Even though this work was published before ours, we came up with the solution first but could not publish it until now due to red tape. Wu et al. originally articulated the need for sensor networks [22-25]. W Taylor developed a similar algorithm; nevertheless we argued that our methodology is NP-complete. Although we have nothing against the previous method, we do not believe that approach is applicable to steganography [26]. Thus, comparisons to this work are astute.

The concept of heterogeneous technology has been deployed before in the literature. Bose [27] developed a similar approach, however we demonstrated that Gunning is Turing complete [28]. Further, Raman et al. [13] developed a similar framework; contrarily we argued that Gunning runs in O (N) time [28,29]. Obviously, comparisons to this work are fair. Next, unlike many prior approaches [17], we do not attempt to evaluate or locate virtual communication. Along these same lines, instead of architecting peer-to-peer epistemologies, we overcome this quagmire simply by deploying signed epistemologies [30]. While we have nothing against the existing solution by Watanabe and Raman [31], we do not believe that solution is applicable to complexity theory [10].

Conclusion

Our experiences with our system and self-learning technology show that the little-known cooperative algorithm for the exploration of I/O automata by Taylor and Maruyama [32] is Turing complete. The characteristics of our heuristic, in relation to those of more little-known methods, are compellingly more essential. Gunning has set a precedent for object-oriented languages, and we expect that researchers will improve our system for years to come. Gunning has set a precedent for redundancy, and we expect that cyber informaticians will visualize Gunning for years to come. We plan to make our framework available on the Web for public download.

For more articles in Open Access Journal of Engineering Technology please click on: https://juniperpublishers.com/etoaj/index.php

To read more...Fulltext please click on: https://juniperpublishers.com/etoaj/ETOAJ.MS.ID.555556.php

#Engineering Technology open access journals #Juniper publisher journals #Juniper publishers #Juniper Publishers reviews #Open Access Journals

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theresawelchy · 6 years ago

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Towards a hands-free query optimizer through deep learning

Towards a hands-free query optimizer through deep learning Marcus & Papaemmanouil, CIDR’19

Where the SageDB paper stopped— at the exploration of learned models to assist in query optimisation— today’s paper choice picks up, looking exclusively at the potential to apply learning (in this case deep reinforcement learning) to build a better optimiser.

Why reinforcement learning?

Query optimisers are traditionally composed of carefully tuned and complex heuristics based on years of experience. Feedback from the actual execution of query plans can be used to update cardinality estimates. Database cracking, adaptive indexing, and adaptive query processing all incorporate elements of feedback as well.

In this vision paper, we argue that recent advances in deep reinforcement learning (DRL) can be applied to query optimization, resulting in a “hands-free” optimizer that (1) can tune itself for a particular database automatically without requiring intervention from expert DBAs, and (2) tightly incorporates feedback from past query optimizations and executions in order to improve the performance of query execution plans generated in the future.

If we view query optimisation as a DRL problem, then in reinforcement learning terminology the optimiser is the agent, the current query plan is the state, and each available action represents an individual change to the query plan. The agent learns a policy which informs the actions it chooses under differing circumstances. Once the agent decides to take no further actions the episode is complete and the agent’s reward is (ideally) a measure of how well the generated plan actually performed.

There are a number of challenges, explored in this paper, with making this conceptual mapping work well in practice. Not least of which is that evaluating the reward function (executing a query plan to see how well it performs) is very expensive compared to e.g. computing the score in an Atari game.

The ReJOIN join order enumerator

ReJOIN explores some of these ideas on a subset of the overall query optimisation problem: learning a join order enumerator.

Each query sent to ReJOIN is an episode, the state represents subtrees of a binary join tree together with information about the query join and selection predicates. Actions combine two subtrees into a single tree. Once all input relations are joined the episode ends, and ReJOIN assigns a reward based on the optimiser’s cost model. It’s policy network is updated on the basis of this score. The final join ordering is passed to the optimiser to complete the physical plan.

Using the optimiser’s cost model as a proxy for the ultimate performance of a generated query plan enables join orderings to be evaluated much more quickly.

The following chart shows how ReJOIN learns to produce good join orders during training. It takes nearly 9000 episodes (queries) to become competitive with PostgreSQL.

Once ReJOIN has caught up with PostgreSQL, it goes on to surpass it, producing orderings with lower cost.

Also of note here is that after training, ReJOIN produces its query plans faster than PostgreSQL’s built-in join enumerator in many cases. The bottom-up nature of ReJOIN’s algorithm is

, whereas PostgreSQL’s greedy bottom-up algorithm is

In addition to being limited to just join ordering, ReJOIN’s use of the query optimiser’s cost model to generate reward signals means that it is still dependent on a well-tuned cost model, which is a big part of the problem we wanted to solve in the first place. Ideally we’d like to extend the approach to handle full physical plan generation, and also remove the dependency on having an existing cost model.

Challenges in extending the approach

Once we go from just join ordering to the full search space including operator and access path selection etc., the approach from ReJOIN is unable to learn effective polities in reasonable time. An initial model failed to out-perform random choice even after 72 hours of training.

If we use actual query execution time to generate the reward, then initial policies which will often generate very inefficient plans will take a long time to obtain a reward signal. Thus we learn the slowest at exactly the point when we’d like to learn the fastest and the system takes a prohibitive amount of time to converge to good results. (An experiment with ReJOIN using real query latency instead of optimiser cost confirmed this).

Finally, query latency as a reward signal doesn’t meet the expectations of many DRL algorithms that the reward signal is dense and linear. A dense reward signal is one that provides incremental feedback with every action the agent takes (such as the score updating in an Atari game), not just at the end of the episode. The linear assumption means that an algorithm may attempt to maximise the sum of many small rewards within an episode.

We have identified how the large search space, delayed reward signal, and costly performance indicators provide substantial hurdles to naive applications of DRL to query optimization.

Should we just give up on the idea then? All is not lost yet! The last section of the paper details a number of alternative approaches that could overcome some of these hurdles.

Research directions

Three techniques that may help to make DRL-based query optimisation practical again are learning from demonstration, cost-model bootstrapping, and incremental learning. We’ll look at each of these briefly in turn next (there are no results from applying or evaluating these ideas as yet).

Learning from demonstration

In learning from demonstration, a model is first trained to mimic the behaviour of an existing expert, and then goes on to learn directly from its actions. In the context of query optimisation, we would first train a model to mimic the actions taken by an existing optimiser (indexes, join orderings, pruning of bad plans etc.), and then switch to optimising queries directly bypassing the ‘mentor’ optimiser. In this second phase the agent fine-tunes its own policy. The advantage of this strategy is that mimicking the existing optimiser in the early stages helps the optimiser agent to avoid the ‘obviously bad’ parts of the search space.

Since the behavior of the model in the second phase should not initially stray too far from the behavior of the expert system, we do not have to worry about executing any exceptionally poor query plans. Additionally, since the second training phase only needs to fine-tune an already-performant model, the delayed reward signal is of far less consequence.

Cost model bootstrapping

Cost model bootstrapping uses a very similar in spirit two-phase approach. In the first phase, instead of learning to mimic the actions of an existing expert optimiser, the judgements of the existing expert optimiser (i.e., it’s cost model) are used to bring the agent to an initial level of competence. The optimiser’s cost model is used as the reward signal during initial training, and once the agent has learned to produce good policies according to the cost model, it is then switched to learning from a reward based on actual query latency.

One complication is doing this is that the reward units (scale) need to be consistent across the costs produced by the query optimiser cost model, and the latency measurements of actual query executions. We could apply some kind of normalisation across the two, or alternatively transfer the weights from the first network to a new network (transfer learning).

Incremental learning

Incremental learning attempts to mitigate the issues stemming from poor query plans early in the learning cycle by beginning learning on simpler problems:

… incrementally learning query optimization by first training a model to handle simple cases and slowly introducing more complexity. This approach makes the extremely large search space more manageable by dividing it into smaller pieces.

In the context of query optimisation, we can make problems easier by reducing the number of relations, or by reducing the number of dimensions to be considered. That leads to a problem space that looks like this:

We could therefore try starting with a small number of pipeline phases and gradually introducing more, as shown in the following figure:

Or we could try starting with small examples and gradually focus on larger and larger queries.

Maybe a hybrid strategy will be best, starting with join order and small queries, and gradually increasing sophistication in both dimensions.

#Technology #Finance

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yahooresearch · 8 years ago

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Introducing Similarity Search at Flickr

By Clayton Mellina, Software Development Engineer

At Yahoo, our Computer Vision team works closely with Flickr, one of the world’s largest photo-sharing communities. The billions of photos hosted by Flickr allow us to tackle some of the most interesting real-world problems in image and video understanding. One of those major problems is that of discovery. We understand that the value in our photo corpus is only unlocked when the community can find photos and photographers that inspire them, so we strive to enable the discovery and appreciation of new photos.

To further that effort, today we are introducing similarity search on Flickr. If you hover over a photo on a search result page, you will reveal a “...” button that exposes a menu that gives you the option to search for photos similar to the photo you are currently viewing.

In many ways, photo search is very different from traditional web or text search. First, the goal of web search is usually to satisfy a particular information need, while with photo search the goal is often one of discovery; as such, it should be delightful as well as functional. We have taken this to heart throughout Flickr. For instance, our color search feature, which allows filtering by color scheme, and our style filters, which allow filtering by styles such as “minimalist” or “patterns,” encourage exploration. Second, in traditional web search, the goal is usually to match documents to a set of keywords in the query. That is, the query is in the same modality—text—as the documents being searched. Photo search usually matches across modalities: text to image. Text querying is a necessary feature of a photo search engine, but, as the saying goes, a picture is worth a thousand words. And beyond saving people the effort of so much typing, many visual concepts genuinely defy accurate description. Now, we’re giving our community a way to easily explore those visual concepts with the “...” button, a feature we call the similarity pivot.

The similarity pivot is a significant addition to the Flickr experience because it offers our community an entirely new way to explore and discover the billions of incredible photos and millions of incredible photographers on Flickr. It allows people to look for images of a particular style, it gives people a view into universal behaviors, and even when it “messes up,” it can force people to look at the unexpected commonalities and oddities of our visual world with a fresh perspective.

What is “similarity?”

To understand how an experience like this is powered, we first need to understand what we mean by “similarity.” There are many ways photos can be similar to one another. Consider some examples.

It is apparent that all of these groups of photos illustrate some notion of “similarity,” but each is different. Roughly, they are: similarity of color, similarity of texture, and similarity of semantic category. And there are many others that you might imagine as well.

What notion of similarity is best suited for a site like Flickr? Ideally, we’d like to be able to capture multiple types of similarity, but we decided early on that semantic similarity—similarity based on the semantic content of the photos—was vital to wholly facilitate discovery on Flickr. This requires a deep understanding of image content for which we employ deep neural networks.

We have been using deep neural networks at Flickr for a while for various tasks such as object recognition, NSFW prediction, and even prediction of aesthetic quality. For these tasks, we train a neural network to map the raw pixels of a photo into a set of relevant tags, as illustrated below.

Internally, the neural network accomplishes this mapping incrementally by applying a series of transformations to the image, which can be thought of as a vector of numbers corresponding to the pixel intensities. Each transformation in the series produces another vector, which is in turn the input to the next transformation, until finally we have a vector that we specifically constrain to be a list of probabilities for each class we are trying to recognize in the image. To be able to go from raw pixels to a semantic label like “hot air balloon,” the network discards lots of information about the image, including information about appearance, such as the color of the balloon, its relative position in the sky, etc. Instead, we can extract an internal vector in the network before the final output.

For common neural network architectures, this vector—which we call a “feature vector”—has many hundreds or thousands of dimensions. We can’t necessarily say with certainty that any one of these dimensions means something in particular as we could at the final network output, whose dimensions correspond to tag probabilities. But these vectors have an important property: when you compute the Euclidean distance between these vectors, images containing similar content will tend to have feature vectors closer together than images containing dissimilar content. You can think of this as a way that the network has learned to organize information present in the image so that it can output the required class prediction. This is exactly what we are looking for: Euclidian distance in this high-dimensional feature space is a measure of semantic similarity. The graphic below illustrates this idea: points in the neighborhood around the query image are semantically similar to the query image, whereas points in neighborhoods further away are not.

This measure of similarity is not perfect and cannot capture all possible notions of similarity—it will be constrained by the particular task the network was trained to perform, i.e., scene recognition. However, it is effective for our purposes, and, importantly, it contains information beyond merely the semantic content of the image, such as appearance, composition, and texture. Most importantly, it gives us a simple algorithm for finding visually similar photos: compute the distance in the feature space of a query image to each index image and return the images with lowest distance. Of course, there is much more work to do to make this idea work for billions of images.

Large-scale approximate nearest neighbor search

With an index as large as Flickr’s, computing distances exhaustively for each query is intractable. Additionally, storing a high-dimensional floating point feature vector for each of billions of images takes a large amount of disk space and poses even more difficulty if these features need to be in memory for fast ranking. To solve these two issues, we adopt a state-of-the-art approximate nearest neighbor algorithm called Locally Optimized Product Quantization (LOPQ).

To understand LOPQ, it is useful to first look at a simple strategy. Rather than ranking all vectors in the index, we can first filter a set of good candidates and only do expensive distance computations on them. For example, we can use an algorithm like k-means to cluster our index vectors, find the cluster to which each vector is assigned, and index the corresponding cluster id for each vector. At query time, we find the cluster that the query vector is assigned to and fetch the items that belong to the same cluster from the index. We can even expand this set if we like by fetching items from the next nearest cluster.

This idea will take us far, but not far enough for a billions-scale index. For example, with 1 billion photos, we need 1 million clusters so that each cluster contains an average of 1000 photos. At query time, we will have to compute the distance from the query to each of these 1 million cluster centroids in order to find the nearest clusters. This is quite a lot. We can do better, however, if we instead split our vectors in half by dimension and cluster each half separately. In this scheme, each vector will be assigned to a pair of cluster ids, one for each half of the vector. If we choose k = 1000 to cluster both halves, we have k2 = 1000 * 1000 = 1e6 possible pairs. In other words, by clustering each half separately and assigning each item a pair of cluster ids, we can get the same granularity of partitioning (1 million clusters total) with only 2*1000 distance computations with half the number of dimensions for a total computational savings of 1000x. Conversely, for the same computational cost, we gain a factor of k more partitions of the data space, providing a much finer-grained index.

This idea of splitting vectors into subvectors and clustering each split separately is called product quantization. When we use this idea to index a dataset it is called the inverted multi-index, and it forms the basis for fast candidate retrieval in our similarity index. Typically the distribution of points over the clusters in a multi-index will be unbalanced as compared to a standard k-means index, but this unbalance is a fair trade for the much higher resolution partitioning that it buys us. In fact, a multi-index will only be balanced across clusters if the two halves of the vectors are perfectly statistically independent. This is not the case in most real world data, but some heuristic preprocessing—like PCA-ing and permuting the dimensions so that the cumulative per-dimension variance is approximately balanced between the halves—helps in many cases. And just like the simple k-means index, there is a fast algorithm for finding a ranked list of clusters to a query if we need to expand the candidate set.

After we have a set of candidates, we must rank them. We could store the full vector in the index and use it to compute the distance for each candidate item, but this would incur a large memory overhead (for example, 256 dimensional vectors of 4 byte floats would require 1Tb for 1 billion photos) as well as a computational overhead. LOPQ solves these issues by performing another product quantization, this time on the residuals of the data. The residual of a point is the difference vector between the point and its closest cluster centroid. Given a residual vector and the cluster indexes along with the corresponding centroids, we have enough information to reproduce the original vector exactly. Instead of storing the residuals, LOPQ product quantizes the residuals, usually with a higher number of splits, and stores only the cluster indexes in the index. For example, if we split the vector into 8 splits and each split is clustered with 256 centroids, we can store the compressed vector with only 8 bytes regardless of the number of dimensions to start (though certainly a higher number of dimensions will result in higher approximation error). With this lossy representation we can produce a reconstruction of a vector from the 8 byte codes: we simply take each quantization code, look up the corresponding centroid, and concatenate these 8 centroids together to produce a reconstruction. Likewise, we can approximate the distance from the query to an index vector by computing the distance between the query and the reconstruction. We can do this computation quickly for many candidate points by computing the squared difference of each split of the query to all of the centroids for that split. After computing this table, we can compute the squared difference for an index point by looking up the precomputed squared difference for each of the 8 indexes and summing them together to get the total squared difference. This caching trick allows us to quickly rank many candidates without resorting to distance computations in the original vector space.

LOPQ adds one final detail: for each cluster in the multi-index, LOPQ fits a local rotation to the residuals of the points that fall in that cluster. This rotation is simply a PCA that aligns the major directions of variation in the data to the axes followed by a permutation to heuristically balance the variance across the splits of the product quantization. Note that this is the exact preprocessing step that is usually performed at the top-level multi-index. It tends to make the approximate distance computations more accurate by mitigating errors introduced by assuming that each split of the vector in the production quantization is statistically independent from other splits. Additionally, since a rotation is fit for each cluster, they serve to fit the local data distribution better.

Below is a diagram from the LOPQ paper that illustrates the core ideas of LOPQ. K-means (a) is very effective at allocating cluster centroids, illustrated as red points, that target the distribution of the data, but it has other drawbacks at scale as discussed earlier. In the 2d example shown, we can imagine product quantizing the space with 2 splits, each with 1 dimension. Product Quantization (b) clusters each dimension independently and cluster centroids are specified by pairs of cluster indexes, one for each split. This is effectively a grid over the space. Since the splits are treated as if they were statistically independent, we will, unfortunately, get many clusters that are “wasted” by not targeting the data distribution. We can improve on this situation by rotating the data such that the main dimensions of variation are axis-aligned. This version, called Optimized Product Quantization (c), does a better job of making sure each centroid is useful. LOPQ (d) extends this idea by first coarsely clustering the data and then doing a separate instance of OPQ for each cluster, allowing highly targeted centroids while still reaping the benefits of product quantization in terms of scalability.

LOPQ is state-of-the-art for quantization methods, and you can find more information about the algorithm, as well as benchmarks, here. Additionally, we provide an open-source implementation in Python and Spark which you can apply to your own datasets. The algorithm produces a set of cluster indexes that can be queried efficiently in an inverted index, as described. We have also explored use cases that use these indexes as a hash for fast deduplication of images and large-scale clustering. These extended use cases are studied here.

Conclusion

We have described our system for large-scale visual similarity search at Flickr. Techniques for producing high-quality vector representations for images with deep learning are constantly improving, enabling new ways to search and explore large multimedia collections. These techniques are being applied in other domains as well to, for example, produce vector representations for text, video, and even molecules. Large-scale approximate nearest neighbor search has importance and potential application in these domains as well as many others. Though these techniques are in their infancy, we hope similarity search provides a useful new way to appreciate the amazing collection of images at Flickr and surface photos of interest that may have previously gone undiscovered. We are excited about the future of this technology at Flickr and beyond.

Acknowledgements

Yannis Kalantidis, Huy Nguyen, Stacey Svetlichnaya, Arel Cordero. Special thanks to the rest of the Computer Vision and Machine Learning team and the Vespa search team who manages Yahoo’s internal search engine.

#Yahoo #Yahoo Research #Flickr #Computer Vision #Similarity #Search #Deep Learning #Similarity Search

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quickclickhosting · 6 years ago

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When Mysql_Connect Php 7 Support

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robertbryantblog · 6 years ago

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When Mysql_Connect Php 7 Support

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supersushimi · 7 years ago

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LOG

Wednesday, i compiled most of these note prior to the test.

W1 UI and XI stand for user interface and user experience they are huge growth industries. User centered design is Created for the for end use. User testing ie (falsifying)mental model of how things work.Folk theory and"wisdom of the crowd” both sort of mean how the river flows is how it will flow.

CRAP stands for Contrast, Repetition, alignment, proximity, all these elements i will try to put into practice into my picture gallery. and this to element, line, color, shape, text, form Negative

End game, narrow purposed design to be in control of the interface design Whats my end game, to “will” the user into my portfolio of design and ultimately choose me as there designer.

W2 Heuristics Principles. usability of system status Real world logical. Freedom undo and redo consistency error prevention# Recognition Flexibility Minimal Error handling. Documentation. = Happy users

Heuristic tests, expert reviews. me to carry out

Getting all this right will impact whether or not they choose me as a client.

W3 servers contain website data. Http stands for hypertext transfer protocol, computers use this to communicate with. Cascading style sheets are how elements are styled.

W4 This week we learnt about Root Folders and how websites are structured, this is important because it keeps all the files in the right places and makes it easy for other to interface with.

W5 Static, page specific with, stubborn layout, fixed with. Fluid, crunched all the way-to the with of the browser- not practical . Adaptive Static and adaptive with break points. Responsive design- fluid and adaptive, flexible, fluid, font changes spacing changes and breakpoints. Design for size not devices.

Why is all this important, new devices are being brought to market every day and how many older devices are there?

W6 Skewamorphic (old school) design.vs Flat design.

Redundant design incorporated into new. We learnt how to make buttons, dials, toggle slate. on off, checkbox list and Radio button list, Drop downs with multiple selections, single slider, Dual Slider.

Choose what is functional ! I like both, but for my website i might go for both in the menu page and the design work page.

W8 We learnt how to make GIFs in a seamless loop. here’s how, File. mov import. Video layer to PS. Take video Preview clip limit to every frame Make frame Animation Output. File export to web legacy. Boom.

W9 Low fidelity prototype For App testing. (paper and ink) For VI testing finding faults.

Benefits. 1 less time. 2 Design changes. 3 less pressure on user. 4 Desires feel less wadded to LFP 5 Stakeholders Recognize work isn't functional yet.

I particularly like LFP this really sparked my imagination.

Benefits in High fidelity Prototyping 1 Faster system response 2 interactivity and visuals, testing workflow. 3 live software to users more likely to behave realistically 4 focusing on test instead of what comes next. 5 Less likely to make human error

W10 Wire frames sketches of page layouts and solutions Clarifying a complex system with a graphic dynamic approach

Wire frame fidelity who what tasks tasks desired how are the tasks learnt where are the tasks performed relationships other tools for users how often tasks time constraints ⑥ what happens when something goes wrong.

I like the structured check list here, it puts the question out there What is the purpose of my web site.

W11 “gate way” CARP Rss,feed old tech packaged in unified form- subscription content, podcasts, reader views, social media,, notifications, Rss super limited

whats trending today. send to twitter passing the content.

(Api) another standard. APIS (Application Programming interface.) how interface M to M machine.

Api Page (Development page) security $ other developments. le Amazon. Widgets (plugins) POWR website export into DW adds more features.

All useful tools to add functionality to your website.

W12 Ed Roberts made Curb Cut accessible (digital accessibility)

human dimensions. S, M, L → uniform etc.

SEO, do not forget this one, don’t want to get left behind the pack. FWI (Search Engine Optimization.

In conclusion my Design web site, i want to be first and foremost functional. My demographic will be individuals companies or prospective employees (designers) and interested individuals on social media platforms, all looking for the qualities that they require, hopefully my work will demonstrate technical expertise and a sound knowledge of design principles, typography, 2d and 3d, UI XI, photography color theory etc.

My pages will be comprised mainly of two forms the menu page and my design work pages.

Will my demographic be younger millennial, best bet probably not. In an article from "https://datausa.io/profile/soc/271020/#employment" stated that the average designers age is 40.5, so ill defiantly cater to these tastes, The male female split is relatively even so for males and females ill try my best to pull a few heart strings with my rubies cube and my mac emulated design.

Also in an article from dezeen.com they stated The "2016 Design Census revealed that 73 per cent of those surveyed identified as white, nine per cent were Hispanic, eight per cent were Asian, and three per cent were black.” Catering to white tastes might not be a stupid idea.

My competitors in today's digital age could be on any sort of platform twitter, Facebook, linked, Instagram, etc. What will put me ahead of the pack( clarity of what I’m about, and a clear message of innovation. I did say not millennial right ?, well these platforms like You Tube is the world’s second largest search engine and third most visited site after Google and Facebook, what do i think about that, it cant be ignored. Whats the answer make all these platforms work for all age demographics.

Testing my site i found that i need to set up my tables with a little more though to stop double handling my design work. I found google to be a good “how to in dream weaver” ie. making links into new pages etc and Bridge PS, ie like batch image processing these are both issues explode and resolved. Moving the image to the background to insure that the table of text was visible was also explored and resolved. Making buttons on an image was also explored and resolved also creating a new page and vs a link that stays on the same page also was researched and resolved.

How to make the page responsive i will attempt during my work, i hope to use this so my page will look and feel professional.

Ive got a lot to learn and a lot of ideas yet to come into reality. What i liked the most was the Low fidelity prototyping stages and nutting out problems and finding out what the user really wants. Learning all the ins and outs of DW was also enjoyable.

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glenmenlow · 8 years ago

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How Emotion Drives Brand Choices And Decisions

We now know more about how the human brain processes information and triggers our behaviors than ever before. Even with all the technological advancements and resulting brand transformation, it may be the discoveries in neuroscience that have the largest impact about how we think about brands today. We now know that up to 90 percent of the decisions we make are based on emotion. Take a minute and read that again; almost every decision we make is based on emotion, not rational thought and measured consideration. Our decisions are the result of less deliberate, linear, and controlled processes than we would like to believe. This is true whether it is a personal decision, a professional one, or even a group decision.

Yet for over 1,000 years in the West, philosophers, scientists, and even psychologists did not focus their attention on human emotion. The general opinion was that emotions were a base part of humanity, a vestige of our “animal” past, and that rationality was what separated Homo sapiens from other, lesser animals.

This attitude began to change somewhat in the twentieth century, with considerable focus being brought on the psychological and psychiatric treatment of neuroses and psychoses. But the subject of what emotions are and what their evolutionary or survival value is to human beings was not addressed to any great extent until the last 20 years.

A big change in the field was sparked by the discoveries of neurophysiologist Antonio Damasio, reported in his 1999 book, The Feeling of What Happens. In it, Damasio reports: “Work from my laboratory has shown that emotion is integral to the process of reasoning and decision making, for worse and for better. This may sound a bit counterintuitive, at first, but there is evidence to support it. The finding comes from the study of several individuals who were entirely rational in the way they ran their lives up to the time when, as a result of neurological damage in specific sites of their brains, they lost a certain class of emotions and, in a momentous parallel development, lost their ability to make rational decisions . . . These findings suggest that selective reduction of emotion is at least as prejudicial for rationality as excessive emotion. It certainly does not seem true that reason stands to gain from operating without the leverage of emotion. On the contrary, emotion probably assists reasoning, especially when it comes to personal and social matters involving risk and conflict. I suggested that certain levels of emotion processing probably point us to the sector of the decision-making space where our reason can operate most efficiently. I did not suggest, however, that emotions are a substitute for reason or that emotions decide for us. It is obvious that emotional upheavals can lead to irrational decisions. The neurological evidence simply suggests that selective absence of emotion is a problem. Well–targeted and well–deployed emotion seems to be a support system without which the edifice of reason cannot operate properly.”

If Damasio’s work was the catalyst, then the Nobel Prize-winning work by psychologist Daniel Kahneman was the revolution that changed our way of thinking about thinking. Through dozens of experiments over decades, Kahneman created a new model to explain how people think and make decisions. Kahneman created a construct, called System 1 and System 2, to replace left and right brain, respectively. System 1 handles basic tasks and calculations like walking, breathing, and determining the value of 1 + 1; System 2 takes on more complicated, abstract decision making and calculations, like 435 x 23. System 1 is more driven by intuition, snap judgments, and emotion. System 2 is driven more by reason. Kahneman explains the differences as follows: “‘System 1 does X’ is a shortcut for ‘X occurs automatically.’ And ‘System 2 is mobilized to do Y’ is a shortcut for ‘arousal increases, pupils dilate, attention is focused, and activity Y is performed.’”

What may be most important about Kahneman’s work is his finding that most human decisions are made emotionally, and that the function of human reason is to justify those decisions after the fact.

“The implication is clear: as the psychologist Jonathan Haidt said in another context ‘The emotional tail wags the rational dog’ . . . Until now I have mostly described it [System 2] as a more or less acquiescent monitor, which allows considerable leeway to System 1. I have also presented System 2 as active in deliberate memory search, complex computations, comparisons, planning, and choice. Self-criticism is one of the functions of System 2. In the context of attitudes, however, System 2 is more of an apologist for the emotions of System 1 than a critic of those emotions—an endorser rather than an enforcer. Its search for information and arguments is mostly constrained to information that is consistent with existing beliefs, not with an intention to examine them. An active, coherence-seeking System 1 suggests solutions to an undemanding System 2.”

The idea of humans as rational actors who make decisions to buy or use products and services in purely rational thinking is clearly flawed. We’ve seen firsthand that brands that do well today are the ones that touch peoples’ emotions in deep, meaningful, and authentic ways. Features, specifications can be noise—additional and largely unneeded fodder for System 2 to rationalize emotion-based decisions after they are already made.

Jonathan Haidt, in his book The Righteous Mind: Why Good People Are Divided by Politics and Religion, notes, “The mind is divided, like a rider on an elephant, and the rider’s job is to serve the elephant. The rider is our conscious reasoning—the stream of words and images of which we are fully aware. The elephant is the other 99 percent of mental processes—the ones that occur outside of awareness but that actually govern most of our behavior.”

What this suggests is that, in order to impact and affect decision making, you have to appeal and connect to people’s emotions. Although perhaps counterintuitive to prevailing sentiment, playing to rational considerations is not a compelling motivator; in fact, it limits the potential of building bonds. The bottom line is that scientific and academic data has proven that human beings react intuitively to everything they perceive and base their responses on those reactions rather quickly. Within the first second of seeing something, hearing something, or meeting another person, impressions are made and actions are born. Intuitions come first.

This suggests that traditional models, constructs, and methodologies that we have used for decades to drive our marketing and communications efforts outweigh the importance of rational thinking, rendering them outdated and faulty.

One particular aspect of the work done by Kahneman and his peers has been widely adopted by Behavioral Science and Behavioral Economics. Behavioral Economics aims to change the way economists think about the way humans perceive value and express preferences. This line of thinking, which uses psychological experimentation to develop theories about decision making, has identified a range of biases related to people’s perceptions of value and expressed preferences. In short, people don’t make considered choices; they are not always benefits-maximizing, cost-minimizing, and self-interested. They go with what feels right, with what feels like the right option. They are influenced by readily available information, and that includes our memories and salient information in the environment. We tend to live in the moment, resist change, are poor predictors of future behavior, are subject to distorted memory and are impacted by psychological and emotional states.

Our thinking is subject to insufficient knowledge, feedback, and processing capacity, as well as cognitive biases and emotions. This makes the context of our decision making extremely influential. Additionally, behavioral scientists have recognized that humans do not make choices in isolation. We are social beings with social preferences, trust and reciprocity play a key role.

Behavioral Science also suggests that we use “stories” as a way to help us organize the information we receive, help us remember it, and help us make sense of the world. Since our brains have a limited amount of processing power and memory, shortcuts like stories and heuristics help us to make sense of our environment. This starts first thing in the morning as we consume information and interact with the world around us continuing through the day and into our dreams. This can become an important opportunity for marketers—using stories to impact how we notice, connect, and consider brands. Combining the significant role emotion can play with the power of a strong narrative establishes a new fundamental for effective brand building.

Contributed to Branding Strategy Insider by: Mario Natarelli and Rina Plapler. Excerpted from their new book Brand Intimacy – A New Paradigm in Marketing.

The Blake Project Can Help: Accelerate Brand Growth Through Powerful Emotional Connections

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markjsousa · 8 years ago

Text

How Emotion Drives Brand Choices And Decisions

What may be most important about Kahneman’s work is his finding that most human decisions are made emotionally, and that the function of human reason is to justify those decisions after the fact.

Contributed to Branding Strategy Insider by: Mario Natarelli and Rina Plapler. Excerpted from their new book Brand Intimacy – A New Paradigm in Marketing.

The Blake Project Can Help: Accelerate Brand Growth Through Powerful Emotional Connections

Branding Strategy Insider is a service of The Blake Project: A strategic brand consultancy specializing in Brand Research, Brand Strategy, Brand Licensing and Brand Education

FREE Publications And Resources For Marketers

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joejstrickl · 8 years ago

Text

How Emotion Drives Brand Choices And Decisions

What may be most important about Kahneman’s work is his finding that most human decisions are made emotionally, and that the function of human reason is to justify those decisions after the fact.

Contributed to Branding Strategy Insider by: Mario Natarelli and Rina Plapler. Excerpted from their new book Brand Intimacy – A New Paradigm in Marketing.

The Blake Project Can Help: Accelerate Brand Growth Through Powerful Emotional Connections

Branding Strategy Insider is a service of The Blake Project: A strategic brand consultancy specializing in Brand Research, Brand Strategy, Brand Licensing and Brand Education

FREE Publications And Resources For Marketers

0 notes