![]() He served as the Alumni Representative on our Huron University College Corporation for 12 years. As a student, he fostered positive student experiences not only as Huron’s Orientation Co-chair and as a Don, but also as the President of both Huron and Western University Student’s Councils.Įven after graduation, Alfred didn’t relinquish his incredible commitment to Huron. In 2009, he was acclaimed as President of the Liberal Party of Canada and served until 2012.Īlfred’s contributions to the Huron community over the years have been exceptional. He has also chaired many large political campaign and fundraising efforts. Over the years, he has been called on as a speechwriter and advisor to several Prime Ministers, Premiers and other elected officials. Involved in politics from the age of 15, Alfred first came to prominence in 1979 when, at age 22, he was elected Executive Vice-President of the Ontario Liberal Party. In 2015, he joined Miller Thomson LLP where he heads up the firm’s national structured finance and securitization practice. In 2012, he moved his practice to Wildeboer Dellelce. In 2001, he returned to practising law at Fasken Martineau. which he went on to merge with three (3) of its U.S. In 1998, Alfred led a business combination between Lehndorff and Dundee Realty Corporation, and, after a short period as President & COO of the successor corporation, was appointed CEO of Newstar Technologies Inc. At Lehndorff, in addition to overseeing one of Canada’s most complex debt restructurings, he also oversaw the creation of one of Canada’s first REITs. In 1993, at the age of 36, he was named CEO of an international commercial real estate firm, The Lehndorff Group, with over $5 Billion in commercial real estate assets throughout North America. He joined Fasken Martineau as an Associate in 1989 and was named Partner in 1991. He also serves and has served on the Board of Directors of a number of public and private companies. ![]() Over his career, Alfred has led companies and raised capital in Canada, the United States and Europe. He acted as debtor counsel in the $32 Billion restructuring of the Canadian Asset-backed Commercial Paper or ABCP markets, the largest such restructuring in Canadian history. He has been ranked by Lexpert and UK-based Practical Law Company as one of Canada’s leading lawyers in these fields. Alfred has been recognized as leading counsel in the area of restructuring, mergers and acquisitions, private equity investment and infrastructure finance. But, above all, we lived in harmony.” He shared similar messages of compassion, courage and the conviction to create positive change when he addressed the first-year class at Huron at the commencement ceremony in 2008.Īfter Huron, Alfred graduated in law from the University of Toronto and was called to the Ontario bar in 1987. As he wrote in his HUCSC President’s message to the graduating class of 1979, “At Huron, we held hopes for the future. He is also the proud father of five amazing daughters and, just this year, became a grandfather for the first time to another baby girl.Īlfred graduated from Huron University College with BA (Hons) in Philosophy and Economics in 1979. ![]() He is a Senior Partner at Miller Thomson LLP. Recipient of the Alumni Award of Distinction for outstanding achievement in a professional field and outstanding volunteer service to Huron University CollegeĪlfred Apps is a well-respected lawyer, businessman and prominent political activist.
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![]() This character is about as simple as you can get - just a static image of a cartoon bomb, apparently drawn in MS Paint. The first shot was fired by a character designer who goes by “Ironcommando” with the release of “A-Bomb” in 2008. You’re not meant to be able to play against them at all, let alone win. But a cheapie takes things to a whole different level. A Ronald McDonald edit named “Dark Donald” is one of the most notorious, as is the dreaded “Omega Tom Hanks,” who can fill the screen with damaging DVDs of the actor’s famous films. And there are plenty of MUGEN characters that fit that description. Give them powerful, fast, far-reaching attacks and let them cook. It’s pretty easy to make a strong character in a fighting game. The community has a name for these creations: “cheapies.” You know when you’re arguing “who would win” there’s always someone who tries to bend the rules to get the outcome they want? MUGEN gives these folks their time to shine by letting them make increasingly overpowered characters that can lay waste to your everyday Kung Fu Man without breaking a sweat. Prepare yourself for Polygon's Who Would Win Week. One eternal question spans all of pop culture: "Who would win?" This week we have answers. Caillou? There are like eight different versions of Caillou. Tens of thousands of MUGEN characters exist, spanning every franchise you can think of. With a few free downloads, you can have Peter Griffin face off against Jake from Avatar, or Jake from Adventure Time, or Jake from State Farm. That’s because MUGEN isn’t exactly a game - it’s a construction kit for fighting games, assembling tools to create characters from the ground up. But when you start it up, you discover that the game only has one character: a nondescript martial artist named Kung Fu Man. Super Marvel vs.While most “who would win” battles take place in the unfettered realm of the imagination, in 1999 a group of anonymous software developers released a little program called MUGEN that lets you make them a little more real.Īt first glance, MUGEN looks like one of the zillions of lousy Street Fighter 2 ripoffs that clogged arcades in the 1990s. Asura - An original character that fuses Akuma with Rugal Bernstein.Oni - Akuma after he becomes one with the Satsui no Hadou in Super Street Fighter IV: Arcade Edition.Rare Akuma - A joke version of Akuma with massive damage output, easy infinites, and various references and clichés.Sticking to techniques used in the A-ISM mode and beyond the game itself, he is a very offensive character with useful tools, faithful to his respective appearance.įelineki's Akuma captures the gameplay of Super Street Fighter 2 Turbo, while incorporating several modes. This Akuma, titled the Japanese name Gouki, is based on his appearance in Street Fighter Alpha 3. In M.U.G.E.N, Akuma has been made by various creators. He also avoids using his full potential to avoid ending fights prematurely. ![]() He eventually learned that Ryu has a latent Satsui no Hado ability, so he tries to make Ryu follow it's path, though Ryu, knowing it is an evil power, struggles to get rid of it.Īkuma has a twisted moral code, as he himself does not challenge anyone and refuses to fight with anyone he perceives as being weak, since all of his fights are intended to be fights to the death. After killing his master Goutetsu in a fair fight, Akuma seeks to completely master the Hado, and for such he searches for strong opponent to which he can have a fight to the death. Akuma, however, searching to become the strongest fighter in the world, pursued the path of the Satsui no Hadou, becoming an almost demonic-looking figure. Gouken left Goutetsu's dojo after he became aware that Satsui no Hado was an evil energy. He also appears as a playable character in many of Capcom's crossover games, as well as a playable guest character in Tekken 7: Fated Retribution.Īkuma is Gouken's brother and a student of Goutetsu, who taught them both the art of the Satsui No Hado. CyberAkumaTv's Akuma 1.0 (SSF3 + Resurrection and Shin Akuma)Īkuma (known as Gouki in Japan) is a recurring character from the Street Fighter series, first appearing as a secret final boss in Super Street Fighter II Turbo and later as a recurring playable character.There are versions of this character available that don't have their own branch articles! Mother of the Bride Dresses with Jackets.The Best Wedding Guest Dresses Under $50.Perfect for a special party or romantic Christmas in the City, this sparkling dress is bound to become a seasonal garment go-to for the princess. The princess debuted this emerald green dress for a private party for King Charles’ 75th birthday - the ultra-glamorous gown has scalloped sequin detailing and is adorned with rich floral lace artwork. Though the Christmas season had concluded, Kate’s ‘ Aurora’ sequin gown was still a sensation with its intricate vintage lacework and ruffle trim at the neckline and shoulders. Kate brought the Christmas cheer in this festive red sequin gown from Needle and Thread for a palace party in January 2020. The ‘Goldfinger’ gown with its fully loaded sequin embellished cape and long train made for an unforgettable red carpet moment at the James Bond movie premiere in London. Golden goddess meets royal renaissance woman - Kate’s shimmery gold caped dress brought to mind a style slaying superhero and nearly broke the Internet. The princess’ dress felt familiar as the metallic rose ‘Georgia’ dress has the same silhouette and crystal detailing as her Tenille sequin green gown (worn in Pakistan and at a fall Royal Variety show). Proving she could pull off the Barbiecore trend with aplomb, Kate was ravishing in this pretty pink Packham dress. The princess looked enchanting in this gleaming gown at the Jordanian royal wedding reception. ![]() Kate’s sequin blue gown likely had a hidden meaning - paying homage to the baby boy she was expecting in April the following year - Prince Louis. The icy blue sequin dress has layers of illusion tulle covered in light-catching crystals and tonal powder blue sequin flowers. Getty ImagesĪt the Palladium Theatre in London in late November 2017, Princess Kate wore this dress which was likened to Disney’s Elsa in the animated movie Frozen. Kate’s matching shawl and clutch appeared in Mumbai for a fundraiser in 2016 but later, she wore the gown sans wrap for a Buckingham Palace birthday party for charity Place2Be. Designed by Jenny Packham, the floor-length frock features sleeves and a bodice embellished with blue beads and sequins that create an elaborate floral motif. Kate has worn this hand-beaded gown on more than one celebratory occasion inspired by the fashions of Bollywood, elements of the royal blue dress were embroidered in India. ![]() Radiating that new mom glow at the Tusk Conservation Awards in September 2013 - Kate set off a flurry of flashbulbs from onlookers and photographers alike. Kate wore a heavily embellished floor length metallic sequin gown from Jenny Packham for her first glam appearance following the birth of Prince George. The princess wore Jenny Packham’s emerald ‘Tenille’ gown with a belted waist, crystal embellishment at the neck and allover sequins for the theater performance. With sideswept mermaid waves, gold chandelier hoops and a matching clutch covered in coordinating green sequin, Kate looked every inch the sequin starlet. ![]() Kate brought a little ‘Middleton Magic’ to the Royal Variety performance at Royal Albert Hall in November 2021. Here are 8 of Kate Middleton’s Best Sequin Gown Moments: ![]() When she wants to razzle dazzle royal watchers and thrill fashion critics with shimmering sequins, she goes big. While the Princess of Wales may be inclined to step out for regal red carpet appearances in chiffon dresses with a hint of Grecian glamour, she’s on occasion gone full glitz and donned beaded ballgowns, crystal cape dresses and sequin statement gowns. Three important elements are needed before fine-tuning our model: hardware, photos, and the pre-trained stable diffusion model. Let’s do it! Fine-tuning stable diffusion with your photos Now that we’ve covered all the relevant pieces of the theory, all that’s left is to fine-tune the image synthesis model. ![]() The super resolution component of the model (which upsamples the output images from 64 x 64 up to 1024 x 1024) is also fine-tuned, using the subject’s images exclusively. The subject’s images are fitted alongside images from the subject’s class, which are first generated using the same Stable Diffusion model. This means that the model will learn to reproduce the subject with high fidelity, but mostly in the poses and contexts present in the training images. The first one is overfitting: these extremely large generative models will inevitably overfit such a small set of images, no matter how varied it may be. However, there are still two issues we must address before we can fine-tune the model: The authors of DreamBooth found that including a relevant class noun in the training prompts decreased training speed and increased the visual fidelity of the subject’s reproduced features. In other words, it will be much easier for the model to learn what we look like if we tell it that we are a person and not a refrigerator. The motivation behind linking our unique identifier with a class noun during training is to leverage the model’s strong visual prior of the subject’s class. Yes, this approach works with animals and other objects too! Other examples of class nouns include woman, child, teenager, dog, or sunglasses. For instance, for Fernando Bernuy (co-writer and one of the victims of our experiment), a possible prompt would be A fbernuy man. Following the instructions in DreamBooth’s paper, we’ll use the prompt A where is an identifier that will reference us, and is an already existing class in the model’s vocabulary which describes us at a high level. Once you’ve collected these images, the next step is to label them with a text prompt. Here are examples from the three victims we chose for this blog post: Fernando, Giuls, and Luna (from left to right). Still, these images must exhibit some variation in terms of different perspectives of your face (e.g., front, profile, angles in between), facial expressions (e.g., neutral, smiling, frowning), and backgrounds. The technique we’re about to show you achieves results like you have seen above with no more than a dozen images of your face. Does this mean that we need thousands of pictures of ourselves for the model to reproduce us faithfully?įortunately, the answer is no. How many of these images will we need? Deep Learning models usually require large amounts of data to produce meaningful results (even more so these large image synthesis models). To achieve this, we’ll fine-tune this model with a set of images, binding them to a unique identifier that references us.īut wait a minute. Instead, the model must learn what we look like down to the last detail so that it can later reproduce us in the most fictional scenarios. It’s still very hard for these models to reconstruct the key visual features that characterize a specific person. Can’t we just feed the model an extremely detailed description of the person and be done with it? The short answer is no. After all, these new generation image synthesis models have unprecedented expressive power. You may be wondering why we need to follow such a special procedure. To do so, we’ll follow a special procedure to implant ourselves into the output space of an already trained image synthesis model. The first step towards creating images of ourselves using DreamBooth is to teach the model how we look. □įeel free to skip this section if you’re not particularly interested in the theory behind the approach and prefer to dive straight into the implementation. Don’t say we didn’t warn you!Īlso, if you know part of our team, you may recognize some faces in the following images. ![]() ![]() Be warned, generating images of yourself (or your friends) is highly addictive. To do so, we’ll implant ourselves into a pre-trained Stable Diffusion model’s vocabulary. Now, in this blog post, we will guide you through implementing DreamBooth so that you can generate images like the ones you see below. In our previous post, we discussed text-to-image generation models and the massive impact that models like DALL♾ and Stable Diffusion are having throughout the Machine Learning community. Have you ever wished you were able to try out a new hairstyle before finally committing to it? How about fulfilling your childhood dream of being a superhero? Maybe having your own digital Funko Pop to use as your profile picture? All of these are possible with DreamBooth, a new tool developed by researchers at Google that takes recent progress in text-conditional image synthesis to the next level. Note that an attacker with administrative access to a device, be it a Cisco device or one from any other vendor, can perform activities that may be dangerous or disruptive. It is therefore important that administrators protect credentials for privileged accounts (for example, privilege 15) with appropriate controls and by implementing credentials management policies. However, these technologies will not protect Cisco IOS Software from unauthorized access due to compromised credentials. Administrators should make sure their hardware and software supports these features to ensure protection of the integrity of the device. To install malware in Cisco IOS Software, attackers may try to use one of the methods described in this section.Ĭisco IOS Software implements several techniques, including the use of safe coding libraries, Address Space Layout Randomization (ASLR), digitally signed software, and Cisco Secure Boot to help protect against memory and code manipulation and provide assurances of authenticity. By a combination of some or all of the preceding methods.By modifying hardware components of a Cisco IOS device.By modifying the ROM monitor on systems with flash-based ROM monitor storage.By tampering with Cisco IOS memory during run time.This type of malware would be persistent and remain after a reboot. By altering the software image stored on the onboard device file system.Malicious software in Cisco IOS Software may be introduced in the following ways: ![]() ![]() On Cisco devices running Cisco IOS Software, a limited number of infection methods are available to malware. In general, malware can be installed by using various methods: by exploiting vulnerabilities on the system, or by manipulating an authorized user via social engineering attacks. Methods to identify possibly compromised infrastructure devices by using telemetry data are discussed in the Telemetry-Based Infrastructure Device Integrity Monitoring white paper. Potentially, sophisticated Cisco IOS malware could attempt to hide its presence by modifying Cisco IOS command output that would normally reveal information about the malware's presence.Īn additional property of a malware is the capability to be remotely programmable from Command and Control (C&C) server. Malware is usually designed to monitor and exfiltrate information from the operating system on which it is running without being detected. One of the characteristics of effective malware is that it can run on a device stealthily in privileged mode. Malware is software created to modify a device's behavior for the benefit of a malicious third party (attacker). In fact, by owning an infrastructure device such as a router, the attacker may gain a privileged position and be able to access data flows or crypto materials or perform additional attacks against the rest of the infrastructure. While these types of attacks still represent the majority of attacks on network devices, attackers are now looking for ways to subvert the normal behavior of infrastructure devices due to the devices' privileged position within the IT infrastructure. In the past, attackers were primarily targeting infrastructure devices to create a denial of service (DoS) situation. ![]() Note: This document applies only to Cisco IOS Software and to no other Cisco operating systems. Additionally, the document presents common best practices that can help protect against attempts to modify hardware or inject malicious software (also referred to as malware) in a Cisco IOS device. This document analyzes methods that may be used to compromise Cisco devices, including the injection of malicious software in Cisco IOS Software, and describes ways to verify that the software on a Cisco router, both in device storage and in running memory, has not been modified. Use Centralized and Comprehensive Logging Use TACACS+ Authorization to Restrict Commands Use Authentication, Authorization, and Accounting Leverage the Latest Cisco IOS Security Protection Features Verify MD5 Validation Feature for the Text RegionĬisco IOS Address Space Layout Randomization ConsiderationsĬhecking That Cisco IOS Software Call Stacks Are Within the Text Section BoundariesĬhecking Command History in the Cisco IOS Core Dump Verifying Authenticity for Digitally Signed ImagesĬisco IOS Run-Time Memory Integrity Verification Using the Message Digest 5 File Validation Feature |
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