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Biogen Inc. discovers, develops, manufactures, and delivers therapies for treating neurological and neurodegenerative diseases worldwide. The company offers TECFIDERA, AVONEX, PLEGRIDY, TYSABRI, and FAMPYRA for multiple sclerosis (MS); SPINRAZA for the treatment of spinal muscular atrophy; and FUMADERM to treat plaque psoriasis. It also provides BENEPALI, an etanercept biosimilar referencing ENBREL; IMRALDI, an adalimumab biosimilar referencing HUMIRA; and FLIXABI, an infliximab biosimilar referencing REMICADE. In addition, the company offers RITUXAN for the treatment of non-Hodgkin's lymphoma, chronic lymphocytic leukemia (CLL), rheumatoid arthritis, two forms of ANCA-associated vasculitis, and pemphigus vulgaris; RITUXAN HYCELA for non-Hodgkin's lymphoma and CLL; GAZYVA to treat CLL and follicular lymphoma; and OCREVUS for the treatment of relapsing MS and primary progressive MS; and other anti-CD20 therapies. Further, it is involved in developing Opicinumab, BIIB061, and BIIB091 for MS and neuroimmunology; Aducanumab, BAN2401, BIIB092, BIIB076, and BIIB080 to treat Alzheimer's disease and dementia; BIIB067, BIIB078, BIIB110, and BIIB100 to treat neuromuscular disorders; BIIB054 and BIIB094 for treating movement disorders; BIIB111 and BIIB112 for ophthalmology; Dapirolizumab pegol and BIIB059 to treat immunology and others; BIIB104 for neurocognitive disorders; BIIB093, TMS-007, and Natalizumab to treat acute neurology; BIIB074 and BIIB095 for pain; and SB11 biosimilar, which are under various stages of development. The company offers products through its sales force and marketing groups. Biogen Inc. has collaboration agreements with Genentech, Inc.; Ionis Pharmaceuticals, Inc.; Eisai Co., Ltd.; Alkermes Pharma Ireland Limited; Bristol-Myers Squibb Company; Acorda Therapeutics, Inc.; AbbVie Inc.; Skyhawk Therapeutics, Inc.; Neurimmune SubOne AG; and Sangamo Therapeutics Inc. The company was founded in 1978 and is headquartered in Cambridge, Massachusetts.

Biogen

biogen

Biogen said on Thursday that the European Commission has granted marketing authorization under exceptional circumstances for its amyotrophic lateral sclerosis (ALS) drug Qalsody.

European Commission grants marketing approval to Biogen's ALS drug | Reuters

NEW YORK, May 30, 2024 /PRNewswire/ -- Attorney Advertising -- Bronstein, Gewirtz &amp; Grossman, LLC, a nationally recognized law firm, notifies investors that a class action lawsuit has been filed against Biogen Inc. (&quot;Biogen Inc.&quot; or &quot;the Company&quot;) (NASDAQ: BIIB) and certain of its officers.

BIIB INVESTOR ALERT: Bronstein, Gewirtz & Grossman LLC Announces that Biogen Inc. Investors with Substantial Losses Have Opportunity to Lead Class Action Lawsuit! | Morningstar

These Biotech Execs Made Perfectly Timed Trades in Health Care Stocks 

এই বায়োটেক এক্সিক্স স্বাস্থ্যসেবা স্টকগুলিতে নিখুঁতভাবে সময়োপযোগী ট্রেড করেছেন

Diese Biotech-Manager haben zum perfekten Zeitpunkt Geschäfte mit Gesundheitsaktien getätigt

Estos ejecutivos de biotecnología realizaron operaciones en el momento perfecto en acciones de atención médica

Ces dirigeants de la biotechnologie ont effectué des transactions parfaitement synchronisées sur les actions du secteur de la santé

इन बायोटेक अधिकारियों ने स्वास्थ्य देखभाल शेयरों में बिल्कुल सही समय पर व्यापार किया

これらのバイオテクノロジー幹部はヘルスケア株で完璧なタイミングで取引を行った

헬스케어 주식에서 완벽한 타이밍에 거래한 생명공학 경영진

Esses executivos de biotecnologia fizeram negociações perfeitamente cronometradas em ações de saúde

Эти руководители биотехнологий совершили идеально своевременные сделки по акциям здравоохранения

Bu Biyoteknoloji Yöneticileri Sağlık Hisse Senetlerinde Mükemmel Zamanlanmış Ticaret Yaptı

Những nhà điều hành công nghệ sinh học này đã thực hiện giao dịch theo thời gian hoàn hảo đối với cổ phiếu chăm sóc sức khỏe

这些生物技术高管在医疗保健股上进行了完美的时机交易

Github Motion to dismiss Court Filing, retrieved on January 26, 2023 is part of . You can jump to any part in this filing . This part is 20 of 26. HackerNoon’s Legal PDF Series here ARGUMENT IV. PLAINTIFFS’ TORT AND UCL CLAIMS FAIL. B. Plaintiffs Do Not Plausibly Allege A Contract Or Business Expectancy, Nor Defendants’ Knowledge Of One As Required For An Interference With Economic Advantage Claim. Plaintiffs’ tortious interference claim (Count III) against GitHub and Microsoft is deficient in multiple other ways. “Tortious interference with contractual relations requires (1) the existence of a valid contract between the plaintiff and a third party; (2) the defendant’s knowledge of that contract; (3) the defendant’s intentional acts designed to induce a breach or disruption of the contractual relationship; (4) actual breach or disruption of the contractual relationship; and (5) resulting damage.” Ixchel Pharma, LLC v. Biogen, Inc., 9 Cal. 5th 1130, 1141 (2020) (quotation marks omitted). The Complaint falls short on every element. Although Plaintiffs label their claim one for tortious interference with contract, they identify no contractual relationship between themselves and anyone else—much less a contract that GitHub and Microsoft knew about. See Compl. ¶¶ 188-93; see also Bed, Bath & Beyond of La Jolla, Inc. v. La Jolla Vill. Square Venture Partners, 52 Cal. App. 4th 867, 878 (1997) (plaintiff must allege an existing, enforceable contract). While Plaintiffs allege that they deposited materials in public GitHub repositories subject to open source licenses, they do not allege that any third party ever entered into a contract with them in regard to those materials. Nor do Plaintiffs plausibly allege that GitHub or Microsoft committed “intentional acts designed to induce a breach or disruption” or “actual breach or disruption” with respect to an open source license granted by Plaintiffs to any unidentified third party. Ixchel, 9 Cal. 5th at 1141. All they allege is that “[a]t GitHub’s upcoming yearly conference, GitHub Universe 2022, it will host a presentation called ‘How to compete with open source—and win.’” Compl. ¶ 190. Plaintiffs construct their claim on a tortured interpretation of this title, as though the presentation provides a blueprint on how to compete against open source, when the more natural reading is that it is about how to compete effectively using open source. But in all events, this is hardly an allegation of a specific act to knowingly interfere with specific contracts. And in any event, “compet[ing]” and “win[ning]” is not a tort. See Ixchel, 9 Cal. 5th at 1142. The Complaint therefore states no claim for intentional interference with contract against GitHub or Microsoft. The Complaint also fails to state a claim for the related tort of interference with prospective economic advantage. Although “[t]ortious interference with prospective economic advantage does not depend on the existence of a legally binding contract,” it still requires a plaintiff to “show that the defendant knowingly interfered with an economic relationship between the plaintiff and some third party, which carries the probability of future economic benefit to the plaintiff.” Ixchel, 9 Cal. 5th at 1141 (cleaned up). Specific known relationships must be identified. Westside Ctr. Assocs. v. Safeway Stores 23, Inc., 42 Cal. App. 4th 507, 523-28 (1996); Rosen v. Uber Techs., Inc., 164 F. Supp. 3d 1165, 1178-79 (N.D. Cal. 2016). Here, Plaintiffs fail to allege any specific relationship with a reasonably likely economic benefit that GitHub and Microsoft knew about and intended to interfere with. The Complaint merely asserts that “Defendants have wrongfully interfered with the business interests and expectations of Plaintiffs and the Class by improperly using Copilot to create Derivative Works that compete against OSC.” Compl. ¶ 189. This conclusory “the-defendant-unlawfully-harmedme accusation,” Iqbal, 556 U.S. at 678, does not identify the “economic relationship” supposedly interfered with or plausibly allege the “probability of future economic benefit” claimed to be disrupted. Nor does the Complaint allege “actual disruption” or specific “harm,” two additional elements of the claim. Korea Supply Co. v. Lockheed Martin Corp., 29 Cal. 4th 1134, 1153 (2003). As a result, the Complaint does not state a claim for any economic interference tort. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 4:22-cv-06823-JST retrieved on September 11, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. documentcloud.org

GitHub, Microsoft Had No Contractual Obligation to Programmers Whose Code They Used to train Copilot

Bacteria: Chapter 1 - THE BIOLOGY OF BACTERIA

R.U.R. (Rossum's Universal Robots): Act Three - The same. Afternoon

Popular Apple analyst, Ming-Chi Kuo suggested in a note last September that we should expect a new Apple Watch design. The new Apple Watch Series 7 with significant changes could come as early as 2021. We set out to look at recent Apple research on Apple Watch and found six features that have solid potential for the Apple Watch to make big waves in the wearable industry. Some of these ideas are based on recent Apple patent publications, while others are based on supply chain rumors from far East. A Transformation of The Apple Watch Band That is Long Overdue For the last 15 years since the introduction of the original Apple Watch in 2005, the Apple Watch band has functionally remained the same. New materials have been added and new latch designs introduced but the functionality of the band has essentially stayed the same. This could change with a significant re-design of the Apple Watch, given the amount of unused space available on the band. More Battery Power via the Band (Probability - High) Although Apple has improved the battery performance of the Apple Watch over the past few years, users are always looking for more battery power between charges. To solve this problem, Apple could easily add backup battery provision to the band. A future watch band could include an inner frame with multiple slots, each slot configured to house a battery. The band will have the capability to transfer power to the Apple Watch when the low power signal is detected. This is not a far-fetched idea since Apple already has experience in developing backup power via accessories for iPhone and could increase the appeal of the watch to new users. Research on this idea started in 2017 and we have seen new patents along this line of thinking. Apple is not alone in thinking about utilizing the watch band for energy storage. along the same lines according to a recent patent published this week. Google has been researching Watch Band with Optical Sensor (Probability - Low) An older research, originally filed in 2016 and has yet make it to the Apple Watch. The idea here is to attach an optical sensor (camera) to the Apple Watch band. The Apple Watch display would serve as the viewfinder when a user uses the flexible camera band to capture a photo. Given that there is already a called Cameracast that provides this feature at an incredibly high price point of $250, Apple would probably not introduce this feature unless it can engineer this at a more affordable price point. third-party product Apple Watch band could also gain additional sensors that help with health monitoring. A Thinner Apple Watch (Probability - High) This is more likely given that Apple is thinking about significant design changes to the upcoming release. One of the main reasons why it is difficult to make a thinner version of a smartwatch is because a smartwatch has optical sensors on the back of the unit and they protrude outwards in order to help with getting a reading on the PPG sensor. More importantly, the battery inside the watch consumes a lot of space making it harder for designers to offer a watch that has all the new functionality and offer a long battery life between charges. Apple is approaching this problem differently. Recent Apple research shows that the Cupertino giant may be looking at by building in the PPG sensors directly into the core electronics. Instead of having the sensors stick out, the LED and photo-diode sensors will be built-in into the main circuit housing. fabricating the watch internals A New Energy Efficient Watch Display (Probability - High ) A transition to Micro-LED / Mini-LED display for Apple products is very likely given the news coverage around the company’s supply chain. According to Taiwan supply chain news, Apple is teaming up on the new factory with Epistar, Taiwan's top LED producer, and Taiwanese LCD panel maker, AU Optronics. The plant will be located in the Longtan branch of the Hsinchu Science Park and Apple's total investment is estimated at NT$10 billion (US$334 million). Apple has long been expected to use of Mini-LED and Micro LED across a range of products, including at times the 2020 Apple Watch, and now a future refresh of the 16-inch MacBook Pro. Like OLED, micro-LED is self-luminous. However, compared with OLED, micro-LED can support a higher brightness, higher dynamic range, and wider color gamut, all the while achieving a faster update rate, wider viewing angle, and lower power consumption, all qualities favored by Apple. A Micro-LED display on the Apple Watch 7 would probably be the biggest change to the Apple Watch in 2021. Source: https://www.macrumors.com/2020/06/01/apple-invests-330-million-taiwan-micro-led/ New Health and Wellness offerings (Probability - High) We have noticed a long list of partnerships and clinical trials in 2020 where Apple Watch is being used for early COVID detection, post-surgery rehab via partnership with ZimmerBiomet, prediction of Heart failure study in collaboration with UNH and more. The software-as-a-medical-device (SaMD) area is ripe for Apple’s new innovation. It is very likely that we will see blood pressure monitoring on the Series 7. The functionality already exists on the Samsung Galaxy Watch. There has also been rumors that the next Apple Watch and the Samsung Galaxy Watch will feature non-invasive blood glucose monitoring. This is very much possible since Apple has been exploring the use of Thz (Thera-hertz) Raman Spectroscopy for detection of biomarkers. A Japanese company, Quantum Operatin Inc. already showcased a at the 2021 CES, hence, it is not farfetched to imagine that Apple could incorporate a version of it in the upcoming Apple Watch 7. non-invasive blood glucose monitoring Given the stringent FDA standards for disease diagnosis, non-invasive blood glucose monitoring could be a hard feat to achieve. It is more likely that we will see some type of glucose monitoring feature that will get refined in a year or two. Apple Watch Series 7 could also be the first watch to help people who have dementia. The company has been working with related to early dementia onset symptoms and recently published research by Apple researchers showcases the potential around working with Parkinson’s disease use cases. Biogen to understand the biomarkers The work Apple is doing at UCI (University of California, Irvine) to detect early onset of will also play an important role in the offering of a new health monitoring service on the next Apple Watch. Asthma symptoms using the Apple Watch Fitness+ offerings to get additional bells and whistles Apple’s new Fitness+ exercise platform has become a hit. Apple has successfully integrated the Apple Watch into its streaming offerings for fitness enthusiasts. We expect 2021 to bring additional fitness service options integrated via the Apple Watch. The current Fitness streaming options are very one-directional in that you don’t get too much feedback other than a few simple metrics. This is expected to change as Apple launches a personalized and . We have seen the published work from Jay Blahnik and his Fitness+ team which highlights how the Watch will become integral in motivating user behavior. automated Fitness and coaching service Based on your Fitness goals, your Apple Watch will be able to guide you through different workouts and adjust the frequency and duration based on the history of your cardio fitness level metric and other related metrics. 2021 sounds like a promising year for Apple Watch fans. As more and more users are adopting Apple’s wearables and the product segment experiences massive growth, Apple is seriously evaluating new features and form factor that is expected to appeal to the 100 million Apple Watch users worldwide.

6 Predicted Apple Watch Features with Potential for the New Apple Watch Series 7

What Happens When You Get Sick Right Now? 

Quantum physics to me was always this mystifying property which give unreal capabilities to subatomic particles. It was never anything more than just a great conversation starter I used to blow people’s minds away. I mean come on, teleportation? Being in two place at once? Seems science fiction to me. Now if you aren’t aware of quantum mechanics, basically electrons or any other subatomic particle have some wacky but neat super powers such as superposition, (the power to be a wave and a particle, at the same time) quantum tunnelling, (the power to bypass any barriers i.e move through walls) entanglement (the power to be psychic, like having a twin where if one is affected then simultaneously so is the other). Neat, huh? Unfortunately us humans don’t have these super powers so these forces never really affect us or matter in the classical physics world. Or so I thought. Introducing: Quantum Computing With quantum computing we can harness the super powers superposition and entanglement to solve complex problems that our classical computers cannot do. Thus a quantum computer uses the quantum phenomena of subatomic particles to compute complex mathematical problems. A quantum computer uses qubits to supply information and communicate through the system. Its encoded with quantum information in both states of instead of classical bits which can only be . This means a qubit can be in multiple places at once due to superposition. 0 and 1 0 or 1 Imagine the following example, I write an X on a random page in a random book in a library with 1 million books and tell a quantum and classical computer to find the X. For a classical computer, it would have to sort through every page of every book one by one to find the X which would consume a lot of time. For a quantum computer, a qubit in superposition can be in a multiple places at once so it can analyze every page at the same time and find the X instantly. Superposition and entanglement in a quantum computer: qubits unlike classical computers can be in a superposition of both 0 and 1 a complex system of qubits can be in many superpositions at once, example 5 qubits can be in a superposition of 32 states (2^n) 2 entangled qubits are correlated with one another, information on one qubit will reveal information about the other unknown qubit At about 50 qubits, many say a quantum computer could achieve “quantum supremacy” ~ John Preskill Together both properties of superposition and entanglement will enable qubits to compute huge amounts of data simultaneously and solve complex problems such as optimization which classical computers would take millions of years to calculate. Why is the optimization problem important? An optimization problem is essentially finding the best solution to a problem from endless number of possibilities. would have to configure and sort through every possible solution one at a time, on a large scale problem this could take millions of years. Classical computers can find all possible variants at the same time using superposition and entanglement and sift through large amounts of data in a significantly small amount of time. Quantum computers One problem we have now is simulating possible chemical compositions of different compounds due to complex structures that involve a lot of combinations of electron repulsion and attraction. It is another type of optimization problem with countless possibilities for bonds and shapes of molecules. With quantum computing this problem is easily scalable with enough qubits to configure all possibilities for the structure of a molecule. It can be revolutionary for drug discovery in the pharmaceutical industry for classification of millions of drugs and optimizing for the best possible ones for a certain disease. This can be a game changer for personalized medicine, genomics, and being able to fully scale our DNA. One company using this cutting edge technology is with labs and to completely change the way of traditional drug discovery. Biogen Accenture 1QBit The process of computing on a quantum computer is very different from a classical computer. To solve optimization problems there are set algorithms used and qubits function a lot differently to reach the optimal value. The process is explained below in a very simple manner but in reality it is a lot more complex. The Process of Utilizing Quantum Algorithms: Activate qubits to reach a superpositions of all possible states Encode the optimization problem by applying a phase on each superposition state Use methods of interference to cancel or add phases to optimize for the correct answer and shrink the wrong answers (like noise canceling in headphones) However its not very easy getting qubits to cooperate with a system. As more number of qubits are added to a system the higher the error rates. To have a working computational quantum computer your system must satisfy these properties: intialize all of your qubits to a known state rotate individual qubits. measure individual qubits. perform an operation that entangles pairs of qubits. stay free of outside interference (decoherence) for as long as it takes to finish a computation. For a quantum computer to be functional it must have qubits that can harness quantum properties and have all possible measures must be taken to reduce error rates. Qubits are prone to instability and error which is why the system needs to be cooled down as close to 0 kelvin degrees and all accounts of error must be calculated to have a decoherent environment. These are the types of qubits being used today: The Canadian based quantum computer company has a 2000 qubit quantum computer on the market for $15 million. Pretty decent price I would say for a machine that can do math on just a couple atoms. It uses superconducting loops to create functional qubits. The other possible methods are being explored by large tech companies like Google, IBM, Intel, Microsoft, etc. Everyone is in the race to reach 2018 is another year for many future breakthroughs in quantum computers, there is no way to tell how soon we will reach quantum supremacy. D-Wave Systems Inc. “quantum supremacy.” This is the point where quantum computers will outperform classical supercomputers. Why should we be excited about quantum computers? Quantum computers are in their early stage of development much like the classical computers back in the 50’s. No doubt with the classical computers came revolutionary technology such as the internet so imagine the applications of quantum computers for the future. Who back in the 50’s could predict such a thing as social media and the concept of being connected to millions of people through transmitting signals? Future Applications of quantum computers: Better online security with development in quantum encryption Significantly improve AI technology Drug research and discovery More accurate weather predictions Optimizing traffic control Key Takeaways: Quantum mechanics are the laws of subatomic particles with phenomenas such as quantum tunnelling, superposition and entanglement. Quantum computers will use qubits to encode quantum information and calculate complex mathematical problems using superposition and entanglement. Quantum computers can solve optimization problems which can revolutionize drug discovery and many more industries. Qubits are unstable and very susceptible to environmental changes, only work with 0 outside interference due to fragile system Many types of qubits being used today to create the world’s best functional quantum computer Lots of research still needed to create a quantum computer that can defeat our classical supercomputers as I explain some of the more complex theories and types of qubits in quantum computing. Stay tuned for more articles Comment below for more resources and information on quantum computing.

A Brief Introduction to Quantum Computing

Projects spawned in the mouth of hell Who is there, my friend, can climb to the sky?Only the gods dwell forever in sunlight.As for man, his days are numbered,whatever he may do, it is but wind.-Gilgamesh, trans. Andrew George The sprint retrospective was going about as well as could be expected Hell is empty and all the devils are here Don’t trust those smiley affirmative the-universe-will-provide types who go . -I have no regrets Only sociopaths have no regrets. Presumably, sometime after man’s first disobedience brought woe unto the world, mortal taste and all that, even god, 1000 yarding it through the bottom of a whiskey glass late at night, be like – oh, maaaan! I got plenty regrets. And in computers, those regrets generally come somewhere in the middle of the how many-eth night you’re trudging on a death march against a towering deadline you don’t have a hope in hell of making, stacked empty pizza boxes a greased monument to your failure to build anything more meaningful than trash that’ll go into the recycling next time you get round to cleaning. I don’t even really like pizza. But it’s passed into lore that techies perform to pizza like dolphins to sardines and so managerial types keep on running the math that cardboard pizza adequately compensates for all the overtime. The customer loathes you, you despise the customer, the thinks the technicians are lazy bums, the technicians wonder out loud if the managers, given an accelerometer and an orienting frame of reference, would be able to find their own backsides — an inability all the more surprising when that’s where most of the deeply unmotivating motivational speeches seem to be coming from. But everyone involved, everyone hates the salesman. project manager really The salesman doesn’t hate anyone, because he’s off somewhere sunny enjoying his bonus. Because he already got his bonus on signing the deal, not on actual delivery, nor even for making promises that’re even vaguely realistic in a continuum where parallel lines never meet. You regret the corners you have to cut to make it over the line, you regret your bleeding fingers, you regret your irradiated raw eyeballs, you regret how good it could have been if you only had a bit more time, you regret the life you’re missing out on, the birthday parties you didn’t attend, the concerts you didn’t go to, the restaurant bookings you had to cancel, you regret the love interests to whom you can’t even really explain the why without getting into heavy tech specifics that they have no interest in anyway, you regret the whole sorry state of an engineering industry pretending to be a customer servicing , you regret the sense of pride and duty that won’t let you do a half-ass job even under these goddamn circumstances. And no one will be positing rhetorically that your glory can’t fade either, because whatever you have just busted a hump getting done will be superannuated in less than 5 years. And no one will care. You won’t even care. And you will regret that too. business You wonder idly, if you left for the airport , you could be somewhere sunny before tomorrow, track down that bastard salesman, throttle him in his sleep, make it back before anyone even knows you were gone. Even if they caught you, no court in the land would convict you, right? You got . now cause Redefining the measure of success It’s a quintessential human trait: we stumble ball-deep through sewers of our own making, mired in foetid excreta, and we still take the time to stop and to ask -just how did we get this far into the shit? Organizations take the time to call meetings on this right there in the stygian gloom, wallowing in their effluvia as they choke in the miasma. Never again, they say, lessons have been learnt, celebratory flagellants. At this point you don’t need a paddle, you need a steamer. Try slipping that by procurement. The worst start redefining what success means until they can hail blatant failures as heroic successes. Governments are pretty good at this sort of thing. And here you are, in the dead of night, you ask -Where did it all go so horribly wrong? And it’s one of those worldly ironies that it’s not normally all that difficult to answer that question either, all other things being equal. Because you know. . . like the man said in impeccable dactylic dimeter: someone had blundered. The difficulty is more normally to get the right people to what caused the problem, given that the right people are usually the ones who catalyzed the cascading failure in the first instance. The right people being the right people in that they’re the ones who could have done something about it, but in all other respects they are the wrong people. Dunning Kruger writ large. admit Hell, I’m not even naive and idealistic enough anymore to hope that the jabronis who cause the problems will have a moment of clarity, if not basic decency, and actually admit that, yes, in hindsight, yes there is a problem here and yes in hindsight that problem is one that we created and having said all that, here’s a vaguely realistic plan for how we’ll fix it. I’ll settle for them just not doing it again. A teachable moment, of sorts. Fat chance, sonny jim. Whisper it, I’m also not always all that convinced that maybe I didn’t have a bigger hand in the failure than I thought I did. Should have fought harder, been smarter, quicker off the mark. There’s an Xtina song in there somewhere. As an employee, there’s not a helluva lot you can do about this dynamic. Yours is not, usually, to reason why. As a freelancer, or a business owner, assuming you’re sensible, you get warier about barging in with timbs where angels more normally fear to tread. Even so, keeping with Pope, a little learning is a dangerous thing. Hellmouth projects take on a life of their own it’s alive! it’s alive! Hellmouth projects achieve biogenesis by evolving a malevolent homeostasis of sorts. The hellmouth maintains its perverse momentum by endergonic metabolic reactions that suck any chance of achieving positive results out of its hosts as a precursor to full-blown metamorphosis into a facultative ectoparasite. It lives, in short. Like Swamp Thing. It’s hungry too. The hellmouth consumes energy for its own parasitic political purposes, energy that should have been spent delivering the end-product instead. Sort of like the Straumli Blight, once woken unstoppable by nothing short of a destructive spasm in space-time that destroys both contagion and host. And then while you’re there, wondering how it went so wrong, you marshal the sorry remainders of your synapses to extricate some sort of redemption from the doom: satisfy the stakeholders, satisfy the customer, survive this thing somehow. You wonder with former District Attorney Dent whether you can be a decent man in an indecent time? Baobabs start out by being little The time where you get to fix a project where everything goes wrong is before it starts. Anything later than that is too late. Measure twice, cut once. There’s no Buffy to save you once the hellmouth opens. I’m not even going to bother with the numbers. If you’ve worked anywhere within the ambit of IT you’ve lived this, nevermind seen it. If you thought it was just you, take heart: see the various stats from the likes of Gartner and Standish’s CHAOS over the years — something in the area of 70% of IT projects fail. The bigger the project, the bigger the fail. You probably nitpick over the data gathering methodologies of these studies, so let’s just say that the number is way too high, whatever it is. could Given those mistakes that you laughably name your experience, you get better at spotting the signs of trouble in advance. Assuming you don’t spend all your time justifying previous failures instead of learning from them, you develop a combat mindset, tingling spidey-like in the presence of implausible project objectives, duplicitous salesmen and enervated project managers. Like a quaker, you don’t swear oaths. Oaths, not oats. All promise outruns performance Don’t make promises you can’t keep. If you don’t understand empirically what promises you can keep, you shouldn’t be making them. Especially if those promises are what a civil court would refer to as a “contract”. That’s not quite the same as promising to eat more vegetables and do more exercise this year. Also, if you’re unclear what means, stick to the golf. empirical Sometimes this means saying No to customers. This is hard. Customers come to you in need, and in their desperation they can push hard to get to hear answers they want to hear, rather than the answers that they need to hear. They’ll put you on the spot to commit to a guaranteed fixed price and a deadline when you don’t fully understand the full scope of the delivery yet. Unknown Scope of Delivery, Fixed Price and Immovable Deadline, three horsemen of the project delivery apocalypse. The Fourth Horseman is the idiot who is willing to engage on those terms. At best the idiot is jejune, committing the crime of credulity and unwarranted optimism, at worst it is deliberately and disingenuously profiteering in the short-term even though the long-term cost & reputational damage far outweigh any overall benefit. The Fourth Horseman: “. . .and Hell followed with him.” postscript: Wow, that took a dark turn pretty quickly, huh? I wrote the above while I was thinking about how a lot of companies generally behave when projects go wrong, and the flashbacks that overpowered me somewhere around para. 4 weren’t your pleasant Woodstock type. None of the above refers to any project past or present of 345. I was thinking of the time I spent in the trenches way back in the beginning of my career — sadly probably an all too common malaise in this industry — when you’re still junior enough to have to put up and shut up. In fact, not following the script for these nightmare scenarios is sort of the point of 345. I was fortunate enough to meet great business partners some years ago and we all share a vision of doing things differently: a large part of the reason that we started 345 was that we want to deliver great software that makes all the difference for happy customers, rather than being yet another IT consultancy that specializes in politicizing excuses for non-delivery. If you too are sick and tired of IT projects that fail before they even begin, and want to talk about how we can work together to buck the trend and have great IT project delivery instead, get in touch here . Stay safe, -T x Originally published at www.345.systems on March 31, 2017.

When project delivery goes bad

Abstract is a one of retrovirus. It can introduce target gene into some difficult-transfected cells, such as primary cells, and integrate target genes randomly into the host genome, thereby greatly increasing the transfection efficiency, with stable expression in cell lines for several generations. It can also be used for screening stable cell strain. Because there is a random integration with uncertain factors, some companies provide targeted integration technology to integrate target gene into the genome of the specific position, so as to ensure the high expression and not to generate damage to cell produced by random integration. Lentivirus Content The processes that contain packaging, transfection and stable genetic information integration. Lentiviral packaging plasmids can provide all auxiliary proteins needed in transcription, packaging and recombination. In order to produce virus particle with high titer, it needs to use the expression vector and packaging plasmids to co-transfect cells, and do virus packaging in cells. The purified virus particles are secreted into the extracellular medium, and the supernatant can be directly used for host cell infection after centrifugation. lentiviral vectors Lentivirus genome is a positive stranded RNA. When the genome goes into cells, it is transformed into DNA by reverse transcriptase owned by itself and forms pre-integration complex to enter the nucleus. DNA is integrated into the cell genome. The integrated DNA transfers mRNA, and returns to the cytoplasm, expressing target protein or producing RNAi interference. The system consists of a packaging plasmid mixture (Mix) and a lentiviral vector plasmid. lentivirus packaging The vector contains the basic components of HIV, such as 5 ‘LTR, 3’ LTR and other auxiliary components. Packaging plasmid mixtures are not the same in different systems. The three-plasmid system in the laboratory is taken as an example. The plasmid mixtures contain pMDL, VSVG, pRSV-Rev, and the ratio in 5:3:2. Among them, pMDL contains the gag gene encoding the major structural protein of HIV virus and the pol gene encoding the virus specific enzyme; PRSV-Rev has a regulatory factor rev gene that regulates the expression of gag and pol genes; VSVG contains the VSVG gene that comes from the herpes simplex virus needed for viral packaging. Following is the introduction of 293T cells in 6-well plate (35mm) for viral packaging. the other plate should be regulated corresponding to volume. Preparation Reagent chapter Plasmid 293T cells with exponential growth Virus packaging plasmid Mix:1 µg/µl (Mix=pMDL: VSV-G : REV=5:3:2): Packaging virus plasmid is different by different vector systems. This system can be used for packaging PBOBi, PLKO and Plv plasmid Transfection reagents: turbofect or lipo2000 Cell sorting Cells were collected by trypsin digestion, and cells were cultured on the 35mm culture dish with suitable complete medium (According to the experiment, the culture dish was selected, and the area occupied reached more than 80% of the total area of the culture dish). The cells were incubated in 37℃ box containing 5% CO2. When the cells were completely adherent, they could be transfected. 2 h before transfection, 1.5ml complete-medium was replaced with the old medium. The mixing of core plasmid and packaging plasmid Take a 1.5ml EP sterile tube, and add 400 µl serum-free DMEM, 1.5 µg core plasmid and 1.5 µg virus packaging plasmid, 6 µl turbofect (turbofect: plasmid = 2:1), mixing them and and keep them static for 15–20min. Incubation The mixture of this 400 μl was added into the cell culture medium of above monolayer, gently shaking the plate after mixing and keep it for incubation in 5% CO2 box. Collecting virus supernatant After 6–10 h later, suck away the medium and add the 2.5 ml 37℃ complete-medium. Cells continue being incubated. About 40 h later, lentivirus supernatant can be collected to infect or to be packed in －80℃ for use.(by 1500rpm centrifugation for five minutes, we can collect 2ml supernatant containing virus.) Virus infections 1. The cells are spread in six-well-plate or twelve-well-plate. The best density is 30% 70%. 2. For six- well-plate (twelve-plate is half): Infectmouse cells：800–1600µl virus + 1600–800 µl fresh medium =2.4 ml totalInfecthuman cells：200–800µl virus + 1200 µl-800 µl fresh medium =2 ml total 3. Add 2–5 g/ml polybrene. 4. After 12–24 h, change the fluid and do experiment.

Packaging Steps and Experience Summary of Lentivirus

Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing: Conclusion, and References

The Curse of Pygmalion - What Would An Ideal AR/VR Device Look Like?

পিগম্যালিয়নের অভিশাপ - একটি আদর্শ এআর/ভিআর ডিভাইস দেখতে কেমন হবে?

Der Fluch von Pygmalion – Wie würde ein ideales AR/VR-Gerät aussehen?

La maldición de Pigmalión: ¿cómo sería un dispositivo AR/VR ideal?

La malédiction de Pygmalion – À quoi ressemblerait un appareil AR/VR idéal ?

पिग्मेलियन का अभिशाप - एक आदर्श AR/VR डिवाइस कैसा दिखेगा?

ピグマリオンの呪い - 理想的な AR/VR デバイスとはどのようなものでしょうか?

피그말리온의 저주 - 이상적인 AR/VR 장치는 어떤 모습일까요?

A Maldição de Pigmalião – Como seria um dispositivo AR/VR ideal?

Проклятие Пигмалиона: как должно выглядеть идеальное AR/VR-устройство?

Pygmalion'un Laneti - İdeal Bir AR/VR Cihazı Nasıl Görünür?

Lời nguyền của Pygmalion - Thiết bị AR/VR lý tưởng sẽ trông như thế nào?

皮格马利翁的诅咒——理想的 AR/VR 设备是什么样的？

Life in the Next 100 Years According to Jail-Broken Claude Sonnet 3.5

Life in 2050 According to Jail-Broken GPT-4o 

Investigating Ad Targeting on YouTube: Implications and Legal Analysis

Authors:
(1) Toshit Jain, Indian Institute of Science Bangalore, India;
(2) Varun Singh, Indian Institute of Science Bangalore, India;
(3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India;
(4) Upkar Singh, Indian Institute of Science Bangalore, India;
(5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden;
(6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India;
(7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Table of Links Abstract and Intro
Ocean data
pyParaOcean: Architecture
pyParaOcean: Functionalities
Case study: Bay of Bengal
Conclusion
Acknowledgments and References Abstract Visual analysis is well adopted within the field of oceanography for the analysis of model simulations, detection of different phenomena and events, and tracking of dynamic processes. With increasing data sizes and the availability of multivariate dynamic data, there is a growing need for scalable and extensible tools for visualization and interactive exploration. We describe pyParaOcean, a visualization system that supports several tasks routinely used in the visual analysis of ocean data. The system is available as a plugin to Paraview and is hence able to leverage its distributed computing capabilities and its rich set of generic analysis and visualization functionalities. pyParaOcean provides modules to support different visual analysis tasks specific to ocean data, such as eddy identification and salinity movement tracking. These modules are available as Paraview filters and this seamless integration results in a system that is easy to install and use. A case study on the Bay of Bengal illustrates the utility of the system for the study of ocean phenomena and processes. 1. Introduction Understanding ocean data is paramount to predicting extreme weather events such as hurricanes and tsunamis, better understanding of planetary scale processes like global warming, and sustainably managing and preserving ocean resources and its marine life. Visualizing ocean data is challenging due to the presence of multiple fields and parameters that change with time. Ocean currents are undeniably the biggest factor that maintain the heat balance of the ocean-atmosphere system and affect the movement of minerals and salt. Mesoscale eddies, that span from tens to hundreds of kilometers in diameter and have a lifespan that can range from days to months [RR10], are ubiquitous in the oceans. They play a big role in transporting heat and mass within the oceans [McW08]. They also have a big impact on the ecology of the ocean and on the biogeochemical processes [MJD∗ 99,BNBD∗ 07]. With the strides in collection and generation of ocean data [FD06, Ros89], there is a need for tools that support effective visualization of the data, and are scalable to keep pace with the ever expanding resolutions and sizes of ocean datasets. 1.1. Related work Visualization in oceanography is a challenging area of research due to the rapidly increasing size of data, the heterogeneity and multivariate nature of the data, and the inherent complexity of the ocean phenomena. The use of general purpose analysis and visualization software such as Matlab, Tecplot, AVS, and Paraview is prevalent in the community. However, oceanographers often use tools developed specifically for ocean data, such as Ferret [Fer23], pyFerret [pyF23], and Copernicus MyOcean [myO23]. These specialized tools provide multiple functionalities and produce 2D views of the data. A few software frameworks developed within the visualization community provide 2D and 3D data visualization capabilities. COVE [GSK∗ 08] is a collaborative ocean visualization environment that supports interactive analysis of ocean models over the web. RedSeaAtlas [AGT∗ 19] supports the selection of regions in a 2D map and provides exploratory views of winds, waves, tides, chlorophyll, etc. over the Red Sea. OceanPaths [NL15] is a multivariate data visualization tool that computes pathways tracing ocean currents and supports the plotting of different high-dimensional data along the pathways. This enables the study of correlations between different oceanographic features. An oceanographer’s analysis workflow includes a few common tasks [GSK∗ 08] such as inspection of temperature and salinity distributions and vertical cross sections, compare recently measured salinity data against model data, inspect and analyze current vorticities and circulation based on flow data, and analyze extreme events. Isosurfaces and volume rendering are natural choices for visualization of 3D temperature and salinity distributions [DAN12, PBI04]. However, visualization of the dynamically changing distributions is a challenge. VAPOR [LJP∗ 19] is one of the few tools that provides efficient 3D visualization for oceanography and atmospheric science applications. The VAPOR data collection (VDC) data model supports interactive visual analysis of large data sizes on modern GPUs and commodity hardware. Xie et al. [XLWD19] and Afzal et al. [AHG∗ 19] present surveys of visual analysis methods and tools developed for ocean data. Xie et al. classify the visual analysis tasks into four categories, namely study of different environmental variables, ocean phenomena identification and tracking, discovery of patterns and correlations, and visualization of ensembles and uncertainty. Further, they identify different opportunities and unexplored areas for future work including efficient and scalable methods for data processing and management, identification of features at multiple scales, and immersive platforms. While we recognize the availability of several methods for oceanography visualization, we note that they are often standalone solutions. We aim to leverage the extensive advancement in visualization technology as implemented in Paraview while providing functionalities and views that are specific to ocean data. 1.2. Contributions We present pyParaOcean, a system for interactive exploration and visual analysis of ocean data. The system leverages the power of Paraview [AGL05] to enable scalable visualizations of data available from ocean models while supporting a multitude of tasks and functionalities that are specialized for oceanography. The visualization capabilities of pyParaOcean are available via a seamless integration into Paraview using plugins. Key features of the system include • 3D field visualization to study salinity and temperature distribution with support to display and explore dynamically evolving isovolumes. • Ocean current visualization with a support for different seeding strategies for streamlines and pathlines. • Vertical section views and parallel coordinates plot that support interactive selection and slicing of the data. • Identification and tracking of salinity movement via extraction of surface fronts. • Visualization and tracking of eddy features. • An extensible design that supports incorporation of new functionalities as filters in Paraview. We present the results of an exploration of the Bay of Bengal, performed in collaboration with an oceanographer, as a case study to demonstrate the utility of the system. This paper is available on arxiv under CC 4.0 license. Authors: (1) Toshit Jain, Indian Institute of Science Bangalore, India; (2) Varun Singh, Indian Institute of Science Bangalore, India; (3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India; (4) Upkar Singh, Indian Institute of Science Bangalore, India; (5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden; (6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India; (7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Authors: Authors: (1) Toshit Jain, Indian Institute of Science Bangalore, India; (2) Varun Singh, Indian Institute of Science Bangalore, India; (3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India; (4) Upkar Singh, Indian Institute of Science Bangalore, India; (5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden; (6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India; (7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Table of Links Abstract and Intro Ocean data pyParaOcean: Architecture pyParaOcean: Functionalities Case study: Bay of Bengal Conclusion Acknowledgments and References Abstract and Intro Abstract and Intro Ocean data Ocean data pyParaOcean: Architecture pyParaOcean: Architecture pyParaOcean: Functionalities pyParaOcean: Functionalities Case study: Bay of Bengal Case study: Bay of Bengal Conclusion Conclusion Acknowledgments and References Acknowledgments and References Abstract Visual analysis is well adopted within the field of oceanography for the analysis of model simulations, detection of different phenomena and events, and tracking of dynamic processes. With increasing data sizes and the availability of multivariate dynamic data, there is a growing need for scalable and extensible tools for visualization and interactive exploration. We describe pyParaOcean, a visualization system that supports several tasks routinely used in the visual analysis of ocean data. The system is available as a plugin to Paraview and is hence able to leverage its distributed computing capabilities and its rich set of generic analysis and visualization functionalities. pyParaOcean provides modules to support different visual analysis tasks specific to ocean data, such as eddy identification and salinity movement tracking. These modules are available as Paraview filters and this seamless integration results in a system that is easy to install and use. A case study on the Bay of Bengal illustrates the utility of the system for the study of ocean phenomena and processes. Visual analysis is well adopted within the field of oceanography for the analysis of model simulations, detection of different phenomena and events, and tracking of dynamic processes. With increasing data sizes and the availability of multivariate dynamic data, there is a growing need for scalable and extensible tools for visualization and interactive exploration. We describe pyParaOcean, a visualization system that supports several tasks routinely used in the visual analysis of ocean data. The system is available as a plugin to Paraview and is hence able to leverage its distributed computing capabilities and its rich set of generic analysis and visualization functionalities. pyParaOcean provides modules to support different visual analysis tasks specific to ocean data, such as eddy identification and salinity movement tracking. These modules are available as Paraview filters and this seamless integration results in a system that is easy to install and use. A case study on the Bay of Bengal illustrates the utility of the system for the study of ocean phenomena and processes. 1. Introduction Understanding ocean data is paramount to predicting extreme weather events such as hurricanes and tsunamis, better understanding of planetary scale processes like global warming, and sustainably managing and preserving ocean resources and its marine life. Visualizing ocean data is challenging due to the presence of multiple fields and parameters that change with time. Ocean currents are undeniably the biggest factor that maintain the heat balance of the ocean-atmosphere system and affect the movement of minerals and salt. Mesoscale eddies, that span from tens to hundreds of kilometers in diameter and have a lifespan that can range from days to months [RR10], are ubiquitous in the oceans. They play a big role in transporting heat and mass within the oceans [McW08]. They also have a big impact on the ecology of the ocean and on the biogeochemical processes [MJD∗ 99,BNBD∗ 07]. With the strides in collection and generation of ocean data [FD06, Ros89], there is a need for tools that support effective visualization of the data, and are scalable to keep pace with the ever expanding resolutions and sizes of ocean datasets. 1.1. Related work Visualization in oceanography is a challenging area of research due to the rapidly increasing size of data, the heterogeneity and multivariate nature of the data, and the inherent complexity of the ocean phenomena. The use of general purpose analysis and visualization software such as Matlab, Tecplot, AVS, and Paraview is prevalent in the community. However, oceanographers often use tools developed specifically for ocean data, such as Ferret [Fer23], pyFerret [pyF23], and Copernicus MyOcean [myO23]. These specialized tools provide multiple functionalities and produce 2D views of the data. A few software frameworks developed within the visualization community provide 2D and 3D data visualization capabilities. COVE [GSK∗ 08] is a collaborative ocean visualization environment that supports interactive analysis of ocean models over the web. RedSeaAtlas [AGT∗ 19] supports the selection of regions in a 2D map and provides exploratory views of winds, waves, tides, chlorophyll, etc. over the Red Sea. OceanPaths [NL15] is a multivariate data visualization tool that computes pathways tracing ocean currents and supports the plotting of different high-dimensional data along the pathways. This enables the study of correlations between different oceanographic features. An oceanographer’s analysis workflow includes a few common tasks [GSK∗ 08] such as inspection of temperature and salinity distributions and vertical cross sections, compare recently measured salinity data against model data, inspect and analyze current vorticities and circulation based on flow data, and analyze extreme events. Isosurfaces and volume rendering are natural choices for visualization of 3D temperature and salinity distributions [DAN12, PBI04]. However, visualization of the dynamically changing distributions is a challenge. VAPOR [LJP∗ 19] is one of the few tools that provides efficient 3D visualization for oceanography and atmospheric science applications. The VAPOR data collection (VDC) data model supports interactive visual analysis of large data sizes on modern GPUs and commodity hardware. Xie et al. [XLWD19] and Afzal et al. [AHG∗ 19] present surveys of visual analysis methods and tools developed for ocean data. Xie et al. classify the visual analysis tasks into four categories, namely study of different environmental variables, ocean phenomena identification and tracking, discovery of patterns and correlations, and visualization of ensembles and uncertainty. Further, they identify different opportunities and unexplored areas for future work including efficient and scalable methods for data processing and management, identification of features at multiple scales, and immersive platforms. While we recognize the availability of several methods for oceanography visualization, we note that they are often standalone solutions. We aim to leverage the extensive advancement in visualization technology as implemented in Paraview while providing functionalities and views that are specific to ocean data. 1.2. Contributions We present pyParaOcean, a system for interactive exploration and visual analysis of ocean data. The system leverages the power of Paraview [AGL05] to enable scalable visualizations of data available from ocean models while supporting a multitude of tasks and functionalities that are specialized for oceanography. The visualization capabilities of pyParaOcean are available via a seamless integration into Paraview using plugins. Key features of the system include • 3D field visualization to study salinity and temperature distribution with support to display and explore dynamically evolving isovolumes. • Ocean current visualization with a support for different seeding strategies for streamlines and pathlines. • Vertical section views and parallel coordinates plot that support interactive selection and slicing of the data. • Identification and tracking of salinity movement via extraction of surface fronts. • Visualization and tracking of eddy features. • An extensible design that supports incorporation of new functionalities as filters in Paraview. We present the results of an exploration of the Bay of Bengal, performed in collaboration with an oceanographer, as a case study to demonstrate the utility of the system. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

pyParaOcean, A System for Visual Analysis of Ocean Data: Abstract and Intro

pyParaOcean, একটি সিস্টেম ফর ভিজ্যুয়াল অ্যানালাইসিস অফ ওশান ডেটা: অ্যাবস্ট্রাক্ট এবং ইন্ট্রো

pyParaOcean, ein System zur visuellen Analyse von Ozeandaten: Zusammenfassung und Einführung

pyParaOcean, un sistema para el análisis visual de datos oceánicos: resumen e introducción

pyParaOcean, un système d'analyse visuelle des données océaniques : résumé et introduction

pyParaOcean, महासागर डेटा के दृश्य विश्लेषण के लिए एक प्रणाली: सार और परिचय

pyParaOcean、海洋データの視覚的分析システム: 概要と紹介

pyParaOcean, 해양 데이터의 시각적 분석 시스템: 개요 및 소개

pyParaOcean, um sistema para análise visual de dados oceânicos: resumo e introdução

pyParaOcean, Система визуального анализа океанических данных: Краткое содержание и введение

pyParaOcean, Okyanus Verilerinin Görsel Analizi İçin Bir Sistem: Özet ve Giriş

pyParaOcean, Hệ thống phân tích trực quan dữ liệu đại dương: Tóm tắt và giới thiệu

pyParaOcean，海洋数据可视化分析系统：摘要和简介

Authors:
(1) Toshit Jain, Indian Institute of Science Bangalore, India;
(2) Varun Singh, Indian Institute of Science Bangalore, India;
(3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India;
(4) Upkar Singh, Indian Institute of Science Bangalore, India;
(5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden;
(6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India;
(7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Table of Links Abstract and Intro
Ocean data
pyParaOcean: Architecture
pyParaOcean: Functionalities
Case study: Bay of Bengal
Conclusion
Acknowledgments and References Acknowledgments This research was funded by a grant from SERB, Govt. of India (CRG/2021/005278), partial support from National Supercomputing Mission, DST, the J. C. Bose Fellowship awarded by the SERB, DST, Govt. of India, the Dr. Ram Kumar IISc Distinguished Visiting Chair Professorship in EECS, and a scholarship from MoE, Govt. of India. Part of this work was carried out towards partial fulfilment of a thesis requirement at BITS Pilani. References [AGL05] AHRENS J., GEVECI B., LAW C.: Paraview: An end-user tool for large data visualization. The visualization handbook 717 (2005). 2 [AGT∗19] AFZAL S., GHANI S., TISSINGTON G., LANGODAN S., [AGT∗19] AFZAL S., GHANI S., TISSINGTON G., LANGODAN S.,DASARI H. P., RAITSOS D. E., GITTINGS J. A., JAMIL T., SRINIVASAN M., HOTEIT I.: RedSeaAtlas: A visual analytics tool for spatiotemporal multivariate data of the red sea. In EnvirVis: Workshop on Visualization in Environmental Sciences (EnvirVis2019) (2019), pp. 25–32. 1 [AHG∗19] AFZAL S., HITTAWE M. M., GHANI S., JAMIL T., KNIO O., HADWIGER M., HOTEIT I.: The state of the art in visual analysis approaches for ocean and atmospheric datasets. Computer Graphics Forum 38, 3 (2019), 881–907. 1, 4 [AMM17] AMORES A., MELNICHENKO O., MAXIMENKO N.: Coherent mesoscale eddies in the North Atlantic subtropical gyre: 3-D structure and transport with application to the salinity maximum. Journal of Geophysical Research: Oceans 122, 1 (2017), 23–41. 5 [BNBD∗07] BENITEZ-NELSON C. R., BIDIGARE R. R., DICKEY T. D., LANDRY M. R., LEONARD C. L., BROWN S. L., NENCIOLI F., RII Y. M., MAITI K., BECKER J. W., ET AL.: Mesoscale eddies drive increased silica export in the subtropical pacific ocean. Science 316, 5827 (2007), 1017–1021. 1 [DAN12] DINESHA V., ADABALA N., NATARAJAN V.: Uncertainty visualization using HDR volume rendering. The Visual Computer 28 (2012), 265–278. 1 [FD06] FRASER S., DICKSON B.: Data mining geoscientific data sets using self organizing maps. Mastering the Data Explosion in the Earth and Environmental Sciences, Extended Abstracts (2006), 5–7. 1 [Fer23] Ferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]. 1 [FFH21] FRIEDERICI A., FALK M., HOTZ I.: A winding angle framework for tracking and exploring eddy transport in oceanic ensemble simulations. In EnvirVis: Workshop [GEP04] GUO D., EVANGELINOS C., PATRIKALAKIS N.: Flow feature extraction in oceanographic visualization. In Proceedings of Computer Graphics International Conference (07 2004), pp. 162–173. doi:10. 1109/CGI.2004.1309207. 5 [GSK∗08] GROCHOW K., STOERMER M., KELLEY D., DELANEY J., LAZOWSKA E.: COVE: A visual environment for ocean observatory design. Journal of Physics: Conference Series 125, 1 (2008), 012092. 1 [KNR∗07] KUMAR S. P., NUNCIO M., RAMAIAH N., SARDESAI S., NARVEKAR J., FERNANDES V., PAUL J. T.: Eddy-mediated biological productivity in the Bay of Bengal during fall and spring intermonsoons. Deep Sea Research Part I: Oceanographic Research Papers 54, 9 (2007), 1619–1640. 4 [LJP∗19] LI S., JAROSZYNSKI S., PEARSE S., ORF L., CLYNE J.: Vapor: A visualization package tailored to analyze simulation data in earth system science. Atmosphere 10, 9 (2019), 488. 1 [Mad08] MADEC G.: NEMO ocean engine. Note du Pôle de modélisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008. 2 [MAIS16] MATSUOKA D., ARAKI F., INOUE Y., SASAKI H.: A new approach to ocean eddy detection, tracking, and event visualization– application to the northwest pacific ocean. Procedia Computer Science 80 (2016), 1601–1611. 5 [McW90] MCWILLIAMS J. C.: The vortices of two-dimensional turbulence. Journal of Fluid mechanics 219 (1990), 361–385. 4 [McW08] MCWILLIAMS J. C.: The nature and consequences of oceanic eddies. Ocean modeling in an eddying regime 177 (2008), 5–15. 1 [MJD∗99] MCNEIL J., JANNASCH H., DICKEY T., MCGILLICUDDY D., BRZEZINSKI M., SAKAMOTO C.: New chemical, bio-optical and physical observations of upper ocean response to the passage of a mesoscale eddy off bermuda. Journal of Geophysical Research: Oceans 104, C7 (1999), 15537–15548. 1 [myO23] Copernicus myOcean. https://marine.copernicus. eu/access-data/ocean-visualisation-tools, 2023. [Online; accessed 28-April-2023]. 1 [NL15] NOBRE C., LEX A.: OceanPaths: Visualizing multivariate oceanography data. In Proceedings of the Eurographics Conference on Visualization (EuroVis 2015) - Short Papers (2015), The Eurographics Association. doi:10.2312/eurovisshort.20151124. 1 [Oku70] OKUBO A.: Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences. Deep sea research and oceanographic abstracts 17, 3 (1970), 445–454. 4 [PBI04] PARK S., BAJAJ C., IHM I.: Visualization of very large oceanography time-varying volume datasets. In Computational Science-ICCS 2004: 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part II 4 (2004), Springer, pp. 419–426. 1 [pyF23] pyferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]. 1 [Ros89] ROSENBLUM L. J.: Visualizing oceanographic data. IEEE computer graphics and applications 9, 3 (1989), 14–19. 1 [RR10] ROBINSON I. S., ROBINSON I. S.: Mesoscale ocean features: Eddies. Discovering the Ocean from Space: The unique applications of satellite oceanography (2010), 69–114. 1 [Sar13] SARMIENTO J. L.: Ocean biogeochemical dynamics. In Ocean Biogeochemical Dynamics. Princeton university press, 2013. 4 [SDVN22] SINGH U., DHIPU T. M., VINAYACHANDRAN P. N., NATARAJAN V.: Front and skeleton features based methods for tracking salinity propagation in the ocean. Computers & Geosciences 159 (2022), 104993. doi:https://doi.org/10.1016/j.cageo. 2021.104993. 5 [TFL∗17] TIERNY J., FAVELIER G., LEVINE J. A., GUEUNET C., MICHAUX M.: The Topology Toolkit. IEEE Transactions on Visualization and Computer Graphics 24, 1 (2017), 832–842. 5 [TZG∗17] TOYE H., ZHAN P., GOPALAKRISHNAN G., KARTADIKARIA A. R., HUANG H., KNIO O., HOTEIT I.: Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing. Ocean Dynamics 67, 7 (Jul 2017), 915–933. doi:10.1007/ s10236-017-1064-1. 2 [VCMN04] VINAYACHANDRAN P. N., CHAUHAN P., MOHAN M., NAYAK S.: Biological response of the sea around Sri Lanka to summer monsoon. Geophysical Research Letters 31, 1 (2004). 6 [VY98] VINAYACHANDRAN P. N., YAMAGATA T.: Monsoon response of the sea around Sri Lanka: generation of thermal domes and anticyclonic vortices. Journal of Physical Oceanography 28, 10 (1998), 1946– 1960. 5, 6 [WHP∗11] WILLIAMS S., HECHT M., PETERSEN M., STRELITZ R., MALTRUD M., AHRENS J., HLAWITSCHKA M., HAMANN B.: Visualization and analysis of eddies in a global ocean simulation. Computer graphics forum 30, 3 (2011), 991–1000. 5 [XLWD19] XIE C., LI M., WANG H., DONG J.: A survey on visual analysis of ocean data. Visual Informatics 3, 3 (2019), 113–128. 1, 2 This paper is available on arxiv under CC 4.0 license. Authors: (1) Toshit Jain, Indian Institute of Science Bangalore, India; (2) Varun Singh, Indian Institute of Science Bangalore, India; (3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India; (4) Upkar Singh, Indian Institute of Science Bangalore, India; (5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden; (6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India; (7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Authors: Authors: (1) Toshit Jain, Indian Institute of Science Bangalore, India; (2) Varun Singh, Indian Institute of Science Bangalore, India; (3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India; (4) Upkar Singh, Indian Institute of Science Bangalore, India; (5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden; (6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India; (7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Table of Links Abstract and Intro Ocean data pyParaOcean: Architecture pyParaOcean: Functionalities Case study: Bay of Bengal Conclusion Acknowledgments and References Abstract and Intro Abstract and Intro Ocean data Ocean data pyParaOcean: Architecture pyParaOcean: Architecture pyParaOcean: Functionalities pyParaOcean: Functionalities Case study: Bay of Bengal Case study: Bay of Bengal Conclusion Conclusion Acknowledgments and References Acknowledgments and References Acknowledgments This research was funded by a grant from SERB, Govt. of India (CRG/2021/005278), partial support from National Supercomputing Mission, DST, the J. C. Bose Fellowship awarded by the SERB, DST, Govt. of India, the Dr. Ram Kumar IISc Distinguished Visiting Chair Professorship in EECS, and a scholarship from MoE, Govt. of India. Part of this work was carried out towards partial fulfilment of a thesis requirement at BITS Pilani. 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N., CHAUHAN P., MOHAN M., NAYAK S.: Biological response of the sea around Sri Lanka to summer monsoon. Geophysical Research Letters 31, 1 (2004). 6 [VY98] VINAYACHANDRAN P. N., YAMAGATA T.: Monsoon response of the sea around Sri Lanka: generation of thermal domes and anticyclonic vortices. Journal of Physical Oceanography 28, 10 (1998), 1946– 1960. 5, 6 [WHP∗11] WILLIAMS S., HECHT M., PETERSEN M., STRELITZ R., MALTRUD M., AHRENS J., HLAWITSCHKA M., HAMANN B.: Visualization and analysis of eddies in a global ocean simulation. Computer graphics forum 30, 3 (2011), 991–1000. 5 [XLWD19] XIE C., LI M., WANG H., DONG J.: A survey on visual analysis of ocean data. Visual Informatics 3, 3 (2019), 113–128. 1, 2 This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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