Jump to content

Leaderboard

Popular Content

Showing content with the highest reputation on 02/22/18 in all areas

  1. Am și eu o întrebare am foat condamnat la 5 luni de închisoare cu executare pentru o frauda însă nu mai contează vreau sa stiu cum merg treburile in pușcărie mai pe limba noastră nu mie teama de ia numai ca e pentru prima data si pe altcineva din familie prieteni rude n-am ce se ii întreb na făcut nimeni aștept răspunsurile celor care stiu
    1 point
  2. No, las că în iunie esti afară. De curiozitate în ce oraș te închide? (posibil să fi omis eu). Daca e Timișoara prin martie încep niște lucrări arheologice p-aproape. În caz că sunt valabile ambele chestii specificate de mine și reușești să intri îți aduc eu țigări, pixuri și șireturi să le vinzi și să-ți faci viața bună acolo...numa să dai un semn
    1 point
  3. am o cunostinta din copilarie care a stat 2 ani...tot pentru frauda bancara. cunostinta mea a invatat java apoi a dat toate restantele la facultate si a terminat facultatea acum locuieste in bucuresti si are salariu de 3500 ron. probabil va pleca si el in anglia! sfatul meu este: gandeste-te ce vrei sa faci in viitor, si investeste timpul in educatia ta ! e foarte bine ca nu fumezi, cere tigari de la rude/etc sunt bune si alea! pentru alti nu pentru tine ! e foarte important sa nu te apuci de fumat! invata o limba. invata sah spor si mult succes!
    1 point
  4. Researchers have discovered new variants of Spectre and Meltdown. The software mitigations for Spectre and Meltdown seem to block these variants, although the eventual CPU fixes will have to be expanded to account for these new attacks. via Bruce Schneier gasesc interesant si articolul The Future of Computing Depends on Making It Reversible, It’s time to embrace reversible computing, which could offer dramatic improvements in energy efficiency de Michael P. Frank , cit si un dialog postat de cititori: "I don't understand why they don't just make a separate processor for security sensitive concerns — one that's slower and auditable but still powerful enough to do nice things — and give that it's own physical bank of RAM, and allow it to simply communicate with the "crazy fast but side-channel-exfiltrateable" CPU(s). You know they did all of that right? Intel ships a Pentium-class CPU, with no speculative execution, inside every CPU. AMD has something too, I've heard rumors it's ARM. Too bad they did it exactly in the wrong way. They made an unauditable, unusable, trusted component (ME/PSP) that can compromise the main CPU. We can't remove their code, we can't put our own code there... but if we could, it would be exactly what you asked for. They're even advertising it as "for security"."
    1 point
  5. Eu am glumit.. asa este, citeste mult si ai grija de tine... parerea mea.. dar cum a zis si tex... ramai limpede, timpul trece.....
    1 point
  6. Ia-ti carti cu tine sau spune acasa sa-ti aduca multe carti. Citeste si nu asculta ce vorbeste lumea (hotii, cum sa faci una/alta). Si daca esti in lanturi, mintea poate fi in paradis. Aia nu iti poate lua nimeni.
    1 point
  7. sa nu te simti atacat, te rog, in perceptia mea, un idiot teribilist. a comis ceva amendabil legal, probabil a produs si un prejudiciu, prejudiciu reparat, motiv pentru care s-a captusit cu citeva luni de bulau... e inspaimintat, vrea sa para tare... va fi infrint nu de colegii de celula, cadrele o vor face. procedural, au sanse maxime. sistemul o va face. va iesi "afara", o parte din fiinta intrata in sistemul de "reeducare", partea rea, partea permanent in garda, partea cu sindrom post traumatic, irecuperabila social. va fi cosmarul celor ce n-au fost "inauntru". daca e dobitoc, va recidiva, va sfirsi alcoolic, marginal... daca are un EQ balansat cu IQ-ul , cind va iesi, va fi cosmarul tau, al meu, al multora de aici, daca are si o componenta histrionica, inclin sa cred ca o are de vreme ce scriem pe acest topic. studiu de caz, SOV.
    1 point
  8. pe cutitari si dinaia cu chestii grave, nu ii baga in aceeasi oala cu altu care o facut ceva nu atata de grav... dar oricum depinde si tu pe ce motiv intri si in functie de asta te baga la dinaia care te intreaba ai scapat sapunul? sau din contra la cei care iti afirma AI SCAPAT SAPUNU!
    1 point
  9. Laptop GL503VD - 4000 Lei. Il urmaresc de ceva timp si chiar merita. Cel mai probabil il iau si eu zilele astea. https://www.pricezone.ro/product/laptop-gaming-asus-rog-strix-gl503vd-fy004-intel-core-i7-7700hq-pana-la-38ghz-156quot-full-hd-8gb-1tb-8gb-cache-nvidia-geforce-gtx-1050-4gb-free-dos-p2124938?_ts=alerts https://www.youtube.com/watch?v=mTS7juj9N1E
    1 point
  10. Optimization by natural selection Evolutionary algorithms are a heuristic-based approach to solving problems that cannot be easily solved in polynomial time, such as classically NP-Hard problems, and anything else that would take far too long to exhaustively process. When used on their own, they are typically applied to combinatorial problems; however, genetic algorithms are often used in tandem with other methods, acting as a quick way to find a somewhat optimal starting place for another algorithm to work off of. The premise of an evolutionary algorithm (to be further known as an EA) is quite simple given that you are familiar with the process of natural selection. An EA contains four overall steps: initialization, selection, genetic operators, and termination. These steps each correspond, roughly, to a particular facet of natural selection, and provide easy ways to modularize implementations of this algorithm category. Simply put, in an EA, fitter members will survive and proliferate, while unfit members will die off and not contribute to the gene pool of further generations, much like in natural selection. Context In the scope of this article, we will generally define the problem as such: we wish to find the best combination of elements that maximizes some fitness function, and we will accept a final solution once we have either ran the algorithm for some maximum number of iterations, or we have reached some fitness threshold. This scenario is clearly not the only way to use an EA, but it does encompass many common applications in the discrete case. Initialization In order to begin our algorithm, we must first create an initial population of solutions. The population will contain an arbitrary number of possible solutions to the problem, oftentimes called members. It will often be created randomly (within the constraints of the problem) or, if some prior knowledge of the task is known, roughly centered around what is believed to be ideal. It is important that the population encompasses a wide range of solutions, because it essentially represents a gene pool; ergo, if we wish to explore many different possibilities over the course of the algorithm, we should aim to have many different genes present. Selection Once a population is created, members of the population must now be evaluated according to a fitness function. A fitness function is a function that takes in the characteristics of a member, and outputs a numerical representation of how viable of a solution it is. Creating the fitness function can often be very difficult, and it is important to find a good function that accurately represents the data; it is very problem-specific. Now, we calculate the fitness of all members, and select a portion of the top-scoring members. Multiple objective functions EAs can also be extended to use multiple fitness functions. This complicates the process somewhat, because instead of being able to identify a single optimal point, we instead end up with a set of optimal points when using multiple fitness functions. The set of optimal solutions is called the Pareto frontier, and contains elements that are equally optimal in the sense that no solution dominates any other solution in the frontier. A decider is then used to narrow the set down a single solution, based on the context of the problem or some other metric. Genetic Operators This step really includes two sub-steps: crossover and mutation. After selecting the top members (typically top 2, but this number can vary), these members are now used to create the next generation in the algorithm. Using the characteristics of the selected parents, new children are created that are a mixture of the parents’ qualities. Doing this can often be difficult depending on the type of data, but typically in combinatorial problems, it is possible to mix combinations and output valid combinations from these inputs. Now, we must introduce new genetic material into the generation. If we do not do this crucial step, we will become stuck in local extrema very quickly, and will not obtain optimal results. This step is mutation, and we do this, quite simply, by changing a small portion of the children such that they no longer perfectly mirror subsets of the parents’ genes. Mutation typically occurs probabilistically, in that the chance of a child receiving a mutation as well as the severity of the mutation are governed by a probability distribution. Termination Eventually, the algorithm must end. There are two cases in which this usually occurs: either the algorithm has reached some maximum runtime, or the algorithm has reached some threshold of performance. At this point a final solution is selected and returned. Example Now, just to illustrate the result of this process I will show an example of an EA in action. The following gif shows several generations of dinosaurs learning to walk by optimizing their body structure and applied muscular forces. From left to right the generation increases, so the further right, the more optimized the walking process is. Despite the fact that the early generation dinosaurs were unable to walk, the EA was able to evolve the dinosaurs over time through mutation and crossover into a form that was able to walk. Sursa: https://towardsdatascience.com/introduction-to-evolutionary-algorithms-a8594b484ac
    1 point
  11. 1 point
  12. Am văzut la Auchan săpun lichid 1litru 0.90bani
    -1 points
  13. Poate cineva sa-mi dea un root de flood si sa-mi explice ce trebuie sa fac gratis?
    -2 points
×
×
  • Create New...