This lesson will define neoplasia, distinguished between benign and malignant types, describe the classification and nomenclature of neoplasms, and illustrate some major histologic features and examples of neoplasia. There are six features which distinguish neoplasia from other forms of tissue growth. As with all rules, one should anticipate occasional exceptions. First, neoplasia results in the formation of a tissue mass, a neoplasm, or tumor. Although tumor literally refers to any kind of mass or swelling, the term is generally used to mean neoplasm. Tumors can grow to large, even enormous, size. Particularly when they arise in less obvious sites, variations in tumor size, location, and rate of growth can result in a large variety of clinical features. Even a small tumor can have profound effects upon the host. A neoplasm, one centimeter in diameter, arising in and obstructing the ampula of water, for example, can cause biliary obstruction, jaundice, and a variety of other symptoms. Second, neoplasia is generally considered to be an irreversible process. Third, neoplastic tissue tends to morphologically resemble its tissue of origin. This section is from a tumor of the thyroid gland. Histologically, there is a close resemblance to normal thyroid, with follicles lined by simple cuboidal epithelium and filled with colloid. The emphasis, however, should be placed on tens to, since some neoplasms show lesser degrees of morphologic resemblance to their tissue of origin. The histologic similarity of this thyroid tumor to normal thyroid tissue is not as obvious as in the previous one. Fourth, neoplastic tissue tends to functionally resemble its tissue of origin. Many neoplasms arising within the islet tissue of the pancreas not only resemble islet tissue with light microscopy. They also contain secretory granules ultra structurally, as demonstrable with electron microscopy. Like their non-neoplastic counterparts, some neoplastic islet cells secrete insulin. The amount of insulin produced by such tumors often exceeds the needs of the host. This results in hypoglycemia and, in turn, headaches, dizziness and other symptoms. Important clinical features of a tumor may result from its functional as well as its structural effects. Again, the emphasis is on tens to, since some neoplasms show little or no detectable evidence of tissue-specific functions. In addition, some neoplasms result in such non-specific systemic effects as catechia, fever, and increased susceptibility to infection. Fifth, neoplasms grow and function somewhat independently of the homeostatic mechanisms, which control normal tissue growth and function. This is referred to as autonomy. Once again, this feature must be qualified. Neoplasms are not, as they are sometimes portrayed, completely autonomous. Some breast cancers, for example, like normal breast, are sensitive to the effects of estrogen. Others are not. Growth of an estrogen-sensitive neoplasm might be arrested or reversed by anti-astrogen therapy, while an estrogen-resistant neoplasm would be expected to continue its growth. The estrogen-sensitive neoplasm is less autonomous than the estrogen-insensitive one. Such variation in autonomy has major prognostic and therapeutic implications. Sixth and last, neoplasms are rarely beneficial, are usually harmful, and sometimes are fatal to the host. Of particular significance to the host is whether the neoplasm is benign or malignant. Ultimately, this distinction depends upon the behavior of the tumor. In general, whether a neoplasm is benign or malignant can be determined by assessing its histologic features. It was pointed out previously that the thyroid tumor illustrated in this slide closely resembles normal thyroid tissue. In general, benign tumors are histologically identical to or closely resemble their tissue of origin. This is true functionally as well as structurally. This benign neoplasm arose in a parathyroid gland. It shows a close histologic resemblance to normal parathyroid gland, and like normal parathyroid, it secreted parathormone. Although benign neoplasms tend to be less autonomous than malignant ones, their growth is progressive, and they can function independently of the host's needs. Benign parathyroid tumors, for example, can, by secreting excessive amounts of parathormone, cause hyperparathyroidism. At lower magnification, the same parathyroid tumor is seen to be compressing the adjacent non-neoplastic glandular parenchyma and stroma. It has formed a discreet and well-demarcated mass. A benign neoplasm consists of a discreet tissue mass which continues to grow. Despite the fact that it may attain enormous size, it remains discreet and distinct from adjacent non-neoplastic tissue. The benign tumor, as it enlarges, compresses adjacent tissue, sometimes causing atrophy. The junction between a benign neoplasm and surrounding tissue may be converted into a fibrous connective tissue capsule. The terms malignant neoplasm, malignant tumor, and cancer are synonymous. The histologic and cytologic features of cancer are summarized by the term anaplasia. These features include derangement of the normal tissue architecture, the crowding of its cells, their lack of orientation termed dyspilarity, and their heterogeneity in size and shape termed pleomorphism. The cytologic features of anaplasia include an increased nuclear cytoplasmic ratio, nuclear pleomorphism, clumping and increased staining of nuclear chromatin, and enlarged and increased numbers of nucleoli. This section shows juxtaposed normal and cancerous colonic mucosa. The non-neoplastic epithelium is evenly distributed and oriented along the basement membrane. Its cells and nuclei are uniform in size and shape, and it shows the features of a highly differentiated tissue. The cancerous tissue is dyspilaric, pleomorphic, poorly differentiated, and shows the cytologic features of anaplasia. A section from another cancer also shows the features of anaplasia. While here we can compare the histologic and cytologic features of benign and malignant tumors arising in the adrenal cortex. Malignant neoplasms often contain numerous mitotic cells. These are typically abnormal, such as the tripolar mitosis in the slide. Such mitotic aberrations account for some of the karyotypic abnormalities found in most cancers. Bizarre, multi-nucleated cells are also seen in some cancers, especially those which are highly anaplastic. In the pathogenesis of cancer, normal tissues do not suddenly become anaplastic. The term dysplasia refers to a pre-malignant state in which a tissue demonstrates histologic and cytologic features which are intermediate between normal and anaplastic. Dysplasia is considered to be at least potentially reversible. The sequence of histologic changes from normal to dysplastic to cancer has been most clearly demonstrated in serial biopsies of the uterine cervix. This section shows the normal stratified squamous epithelium of the exo cervix. The cells show progressive maturation from the deep layer of normal, less differentiated basal cells to the superficial layer of flattened and highly differentiated squamous cells. In this section of dysplastic exo cervix, there has been some loss of these progressive maturational changes. Many cells, especially in the deeper portions, have become somewhat dyspolaric and less stratified. There is some pleomorphism, and the nuclear cytoplasmic ratio is increased. In other words, some of the features of anaplasia are present, at least in the deeper layers. On the other hand, the changes are mild. A gradient of maturation is still evident, especially in the more superficial portions. In this section, the changes have become more severe and extensive, involving virtually the entire thickness of the cervical epithelium. They are now of sufficient magnitude to warrant a diagnosis of cancer. Note, however, that the basement membrane is still intact. This means that the cancer is still in situ. That is, it has not invaded the underlying stroma. A comparison of normal cervix, dysplastic cervix, and cervix with in situ cancer highlights their differences. In addition to the previously noted features, note that mitosis are relatively infrequent and limited to the basal areas in the normal cervix, while they are frequent and found throughout the entire thickness of the cancer. The dysplastic cervix shows an intermediate number and distribution of mitosis. It should be noted that although the designation of cancer implies an irreversible process, in situ cancers of the cervix are an exception, in that they have occasionally been observed to undergo spontaneous regression. On the other hand, if untreated, an in situ cancer is likely to break through the basement membrane and invade subjacent stroma. Invasion and metastasis, the spread of the neoplasm to distant sites, are the definitive features of malignancy. When the pathologist describes a neoplasm as anoplastic, the implication is that it is capable of invading and metastasizing. Most cancers appear to invade early in their growth. Unlike benign tumors which simply push adjacent tissues out of the way, malignant tumors grow into the adjacent tissue. The border of a growing malignant neoplasm is reminiscent of the astrologic crab, hence the term cancer. Malignant cells invade singly or in small groups. These breast cancer cells have infiltrated adjacent adipose and fibrous connective tissue stroma in the breast. A cancer originating in the colonic mucosa may form a mass which protrudes into the lumen, but which also invades deeply into the submucosal, muscular and serosal layers of the intestinal wall. Eventually the growing and invading cancer may penetrate the organ's external surface and extend into adjacent structures and organs, incorporating them into a fixed mass of cancerous tissue. Cancer cells typically invade such thin walled vessels as small veins, venules, capillaries and lymphatics. In this longitudinal section of a systemic vein, the invading cancer has grown into the lumen, forming anitis from which cancer cells and clumps can break off and become emboli. The passage of cancer cells via lymphatics to lymph nodes and via blood vessels to other organs and structures and their subsequent implantation and growth in those sites is referred to as metastasis. In addition to lymph nodes, liver, lung, bone and bone marrow and brain are common sites for metastasis. Virtually any organ or anatomic structure can be involved, however. Seeding is still another means by which some cancers spread. In some forms of ovarian cancer, for example, malignant cells are shed into the peritoneal fluid. These cells may be carried by the fluid to distant sites on the peritoneal surface, where they can implant and form new foci of cancer growth. The classification and the naming of most neoplasms reflect their tissue of origin, whether a neoplasm is benign or malignant, and sometimes additional features. The great majority of neoplasms may be divided into four types, epithelial and non-epithelial, and blastomas and teratomas. Benign epithelial neoplasms are generally classified as adenomas, polyps, or papillomas. Malignant epithelial neoplasms are termed carcinomas. Adenomas are benign epithelial neoplasms which arise in solid organs, such as liver, kidney and thyroid. Typically, they are composed of glandular structures. Since they are benign, adenomas remain discrete, pushing and compressing the surrounding tissue rather than invading it. Similarly, since they are benign, adenomas tend to show a close histological resemblance to their tissue of origin. This adenoma, which arose in the renal cortex, is very similar to normal renal tubular epithelium. Other adenomas may show more variant histologic patterns. They can, for example, be papillary or cystic. Variant patterns continue to show the histologic and cytologic features of benignity. Polyps are benign epithelial neoplasms which arise from the linings of such organs as the intestine. They are typically mushroom-shaped, with their head and stalk covered by epithelium. Their core consists of non-neoplastic connective tissue stroma. The glands and cells of an adenomatous polyp are generally more crowded than those of the normal colon. Nevertheless, there is a close resemblance to normal colonic mucosa. Papillomas are similar to polyps, except that they tend to be finger-like rather than mushroom-shaped. Sometimes the papilloma consists of multiple finger-like projections. Malignant epithelial neoplasms are termed carcinomas. Like benign epithelial neoplasms, carcinomas can arise in solid organs or from epithelial surfaces. The carcinoma in this open segment of colon has invaded the deeper portions of the intestinal wall. Carcinomas, while retaining some of the histologic features of their tissue of origin, are clearly anoplastic. Adenocarcinomas are carcinomas in which gland-like structures are present. This adenocarcinoma of the colon shows some attempt at gland formation. Other carcinomas, like adenomas, may be papillary or cystic. The degree to which adenocarcinomas, as well as other carcinomas and malignant neoplasms, resemble their tissue of origin is indicated by modifiers, such as well-differentiated, moderately-differentiated, and poorly-differentiated. This adenocarcinoma, for example, might be described as moderately or even poorly-differentiated. In general, more highly-differentiated or low-grade cancers tend to have a better prognosis than poorly-differentiated or high-grade cancers. This is a section from a squamous cell carcinoma of the skin. The darker pink areas are dense aggregates of a major squamous cell product, keratin. The features of both partial squamous differentiation and overt anaplasia are present. The cancer is moderately well-differentiated. This transitional cell carcinoma of the bladder closely resembles non-neoplastic bladder epithelium. Although it is a relatively well-differentiated carcinoma, experience with histologically similar bladder tumors allows the pathologist to predict malignant behavior. The second major category of neoplasms is the non-epithelial tumors. These are generally of mesenchymal or mesodermal origin. Non-epithelial neoplasms are generally designated by a prefix which indicates their histologic type and a suffix which indicates whether they are benign or malignant. The suffix oma generally means benign, while sarcoma means malignant. This section is from a well-demarcated tumor in the wall of the uterine fundus. Histologically, it consists of smooth muscle, which is virtually identical to that of normal myometrium. The prefix liomyo indicates smooth muscle and the suffix oma benignity. This neoplasm is a liomyoma. An invasive tumor was present in a similar location in another patient. Histologically, although a resemblance to smooth muscle is evident, so are the features of anaplasia. The prefix is still liomyo, but the suffix is now sarcoma, indicating malignancy. This is a liomyosarcoma. A discrete mass was removed from the subcutaneous tissue of the forearm. It consists of histologically unremarkable adipose tissue. This neoplasm is a lipoma. Another mass removed from the subcutaneous tissue of the buttock consists of anaplastic adipose tissue. It is a liposarcoma. The histologic features of benign and malignant neoplasms of cartilage, a chondroma and a chondrosarcoma can be compared in this slide. There are a variety of other non-epithelial neoplasms for which the same principles of nomenclature apply. The epithelial and non-epithelial neoplasms discussed thus far are generally composed of a single type of tissue. Blastomas and teratomas contain more than one type of tissue. Blastomas are a group of generally malignant neoplasms which consist of tissues resembling the embryonic counterpart of the organ in which the tumor originates. In addition, a blastoma can also contain other tissues into which the organ's embryonic onlogin are capable of differentiating. This somewhat convoluted definition is clarified by a specific example, the blastoma which arises in the kidney. This is the nephroblastoma or Wilms tumor. Nephroblastomas typically occur in infants and young children. They may reach enormous size before they are detected. This one was removed from a two-year-old boy. A remnant of kidney is seen at the inferior pole of the mass. Histologically, a nephroblastoma may consist of any of the tissues into which the embryonic mesodermal onlog of the kidney can differentiate. This section shows nests of primitive renal precursor cells or blastoma and tubular-like derivatives reminiscent of embryonic kidney. Another area contains a structure which resembles a primitive glomerulus. Still another area consists of skeletal muscle. This is not surprising considering the mesodermal nature of the renal onlaga. A trichrome stain shows red striated skeletal muscle and blue collagenous stroma. There are a variety of other blastomas which arise in other organs. They too resemble the embryonic onlaga of their respective organs. Blastomas are therefore considered to be multi-potent in terms of their ability to form various tissues. Teratomas are the fourth major type of neoplasm. This group includes a variety of benign and malignant neoplasms which are derived from germ cells. Most arise in the gonads, but they may occur in other locations as well, usually in the midline of the body. Since they arise from totipotent germ cells, teratomas are at least potentially capable of differentiating into tissues derived from all three embryonic germ layers, as well as extra embryonic trophoblastic tissues. Thus, while the great majority of epithelial and non-epithelial neoplasms may be regarded as histogenetically unipotent and blastomas as multipotent, teratomas are theoretically at least totipotent. This section is from a benign teratoma of the ovary which contained a dozen different types of tissue. Seen here are skin, cartilage, pancreas and respiratory epithelium. The high degree of differentiation in this benign teratoma is evident at higher magnification in its cutaneous and cartilaginous components. Another area shows cerebellar-like tissue. The neurons are well differentiated and resemble those of the normal Purkinje layer. Still another area in this teratoma demonstrates an attempt to form an eye. Retinal and pigmented components are evident. Sometimes teratomas differentiate along a single embryonic germ line. The ovarian dermoid cyst is a common example. This opened dermoid cyst contains hair, sebum and a tooth. Microscopically, the wall of the cyst is composed of well-differentiated skin complete with numerous sebaceous glands and a superficial layer of keratin. Malignant teratomas are often termed teratocarsanomas. Teratocarsanomas contain, in addition to various differentiated tissues, a population of totipotent malignant germ cells termed embryonal cells. The embryonal cells are the progenitors of the differentiated elements of the teratocarsanoma. This section from a teratocarsanoma shows a spectrum ranging from embryonal cells on the left to partially differentiated tissues in the center to well-differentiated cartilage on the right. The highly anaplastic, poorly differentiated character of malignant embryonal cells is seen in this slide. Malignant teratomas may also contain tissue which resembles derivatives of extra embryonic trophoblasts. These include choreocarsanoma, which resembles placental choreonic villi, and yolk sac carcinoma, which resembles endodermal sinus tissue of the embryonic yolk sac. Embryonal cell, teratomatous, yolk sac, and choreocarsanomatous tissue may all be found in a single malignant teratoma or teratocarsanoma. In discussing the nomenclature of neoplasms, it must be pointed out that there are important exceptions. There are, for example, several malignant neoplasms whose traditional names end in oma. Melanoma is a malignant neoplasm of melanocytic origin. Sometimes these malignant melanocytes contain abundant pigment, as in the cell indicated by the arrow. This tumor, which was on the skin, was nearly black. Since it is a malignant neoplasm of epithelial origin, the preferred term is melanocarsanoma. Melanoma is a malignant neoplasm of hepatocellular origin. The hepatocellular features, along with those of anaplasia, are seen in this slide. The preferred term is hepatocellular carcinoma. Lymphomas are malignant neoplasms arising from cells of the lymphoid series. The preferred term is lymphocarcoma. The terms melanoma, hepatoma, and lymphoma are deceptive, but they remain firmly ensconced in the medical literature. In leukemia, there is a malignant proliferation of white blood cells. Large numbers of white blood cells are most evident in the blood, as in this smear. Leukemic cells may be myeloid or lymphoid in origin. Leukemic cells may also be present in solid tissues. They tend to diffusely infiltrate these tissues, rather than form the solid masses typical of other cancers. Another exception to the rules of tumor nomenclature is provided by those neoplasms with eponymic designations. Wilm's tumor was previously mentioned as synonymous with nephroblastoma. Kaposi's sarcoma is an otherwise rare form of cancer, which is relatively common in AIDS patients. Leukemic Kaposi's sarcoma contains prominent vascular and stromal components. Hodgkin's disease is a form of lymphoma. Finally, the hammertoma. Actually, this lesion is generally considered to be non-neoplastic. Hammertomas of the lung are typically discovered in asymptomatic adults who have a chest renconogram. It appears as a solitary, well-demarcated mass. A hammertoma consists of architecturally disorganized, but generally well-differentiated tissues indigenous to the organ in which it occurs. The typical pulmonary hammertoma consists of well-differentiated bronchial components, respiratory epithelium, smooth muscle, and hyaline cartilage, but without recognizable bronchus formation. This lesson has considered the definitive features of neoplasia. It has compared and contrasted benign and malignant neoplasms and outlined and illustrated the classification and nomenclature of neoplasms. Although each program in the series is designed to stand alone, it is recommended that you study Neoplasia I, nomenclature, benign, and malignant states before using this program. Of all the criteria used to describe neoplastic cells, the malignant state is probably best defined by the ability of cells to invade and or metastasize. Although we tend to treat this as a simple concept in which neoplastic cells come to reside in inappropriate sites, it is actually represented by a highly complex sequence of events. During this process, cells from a primary neoplasm become autonomous and undergo a physical separation from the primary tumor. This is referred to as invasion. Next, they enter a compartment or fluid space and are thereby transported to a distant site. Finally, they arrest at a new site and establish themselves in a different environment to produce a metastasis. Although some of the mechanisms related to these steps have been elucidated, the regulatory factors which orchestrate the sequence remain mostly unknown. It is assumed that many neoplasms originate in a manner similar to that scene in this photomicrograph of in situ squamous cell carcinoma of the uterine cervix. Here, the cytologic changes in the epithelial cells are clearly those associated with neoplasia, such as nuclear atypia and loss of maturation. It should be noted, however, that neoplastic cells reside only within the epithelium and that the basement membrane which separates them from their underlying stroma is intact. Once the integrity of the basement membrane has been violated, we use the designation microinvasive. Here, the neoplastic cells can be seen invading across the basement membrane into spaces which are believed to represent both capillaries and lymphatic channels. Further invasion of tumors into their surrounding connective tissue and supporting stromal elements requires that the cells have the ability to modify their microenvironment. Specifically, they must break down a resistive barrier to allow their transit. This process destroys portions of the extracellular milieu and produces the homogenized appearance of the stroma as seen around these prostatic carcinoma cells. This represents loss of macromolecules and entry of edema fluid. The extracellular matrix is in great part composed of collagen fibers suspended in a matrix of glycosaminoglycans. It is not surprising, therefore, that the enzymes believed to be responsible for matrix dissolution include collagenases and glycosidases. In addition, chain reactions can occur that amplify the tumor cell's inherent capabilities. One such mechanism is the activation of latent interstitial collagenase by tumor cell proteases. Although such a process may at first seem foreign or grossly abnormal, it is important to recognize that similar processes are required during embryonic organogenesis. For example, organs, such as the salivary glands, breast and prostate, are formed by a progressive invasion of epithelial buds into beds of mesenchymal cells. Here we see prostatic epithelium growing into a bed of splanknic mesoderm. Branches occur due to differences in rates of cell division, cell death, and matrix breakdown. This process produces complex glands composed of multiple asenar secretory units, linked to each other by a ductal network. As mentioned earlier, in the cascade of metastasis, invasion into tissue is the precursor to establishment of metastatic foci. Certain tumors show a remarkable ability for invading via the vascular system. Here, at the arrow, we see an example of a carcinoid tumor destroying the elastica of a blood vessel as it emigrates out into the surrounding tissue. However, such gross invasion of tumors via large bore blood vessels is rarely witnessed. Instead, we see examples of micrometastasis. Here we see an example of an adenocarcinoma of the prostate metastasizing via the microvascular. Following emigration from the blood vessel wall, metastatic co...


Good evening y'all first off. Let me apologize because I know it's late Some people might be getting ready to go to bed and whatnot. So sorry for keeping you awake Or some people might say hey engraving your videos put me to sleep. So there you go. It'll help but anyway You ever had somebody that you know that When there's a problem instead of trying to fix the problem they try to create an even bigger problem Well, that is what this Article seems to be to me. I've been seeing this picture float around all day today and Initially when I saw it, I'm like what there's no that that can't be real. It can't be there is no way that that's real I ain't believing it. But then I saw the link to the article and Initially when I first saw the proposal. I'm like, hold up. This this gotta be satire It gotta be satire. It gotta be one of those fake Websites that's that's trolling but they try to paint it as a serious thing But I I don't I don't think it is I think right this person that wrote this That they were serious but let's just read the article together and Yeah, anyway it is from a Wisconsin sport heroics It is by McQuaid Arnold and it says a proposed trade between Ravens and the Packers Sends an all-pro to Green Bay And you like from jump the title of the article it kind of make you scratch your head like I Trade between the Ravens and the Packers was sending all-pro to Green Bay But what about what would you send it to the Ravens? But Let's read it Who yikes says the Packers and the Ravens both have glaring needs on the offensive side of the ball both teams Expect to compete for a top playoff seed and a deep playoff run This trade would fill a gap for both teams without leaving a hole at a different position Okay Let's just keep going going into the 2022 season the Green Bay Packers seem to be without a true number one tight end So y'all already know it. This is headed. You already know what this is headed But let's keep going they are hoping that Robert Tanya returns to his 2020 record-breaking self However, there are no guarantees coming off of that torn ACL With the Ravens trading Marquis Brown to the Cardinals their top two receivers over Shaw Bateman and Devin Duvenay Together those two wide receivers accounted for just over 700 yards in three touchdowns in 2021 so you see how he's setting this thing up, right? That's why again, I'm hoping that this is satire, but I don't think it is Anyway, keep going. He said the proposed trade The Ravens would receive tight end Robert Tanya Why receive a my Rogers in a 2023 third round pick and They would give up so the Packers would receive Titan Mark Andrews in a 2023 fifth round pick this trade is absolutely Disgusting and that's of course no offense to Robert Tanya and my Rogers, but this trade is absolutely Disgusting this is nasty It's really really nasty. That's why I said that this this can't be real But when I read the article I'm like hold up. Maybe the homeboy might be serious But let's keep reading this type of trade would be colossal in the NFL. Oh, it sure would be It sure would be and I would need Colossus from X-Men to knock out whoever approved this trade if it happened, but let's keep going Green Bay would acquire an all-pro tight end. Yeah, they would Something that they haven't had in quite some time and Rogers has shown what he can do with the competent tight end over the years The Ravens would get a more than serviceable tight end in return with Robert Tanya But they also get a marquees brown replacement while the Mari Rogers didn't have his successful rookie season His ceiling remains sky high in this league if the Ravens utilize him like they did Brown. He could flourish So, hey From the Green Bay Packers. Hey Baltimore Ravens. What's up? Oh, yeah. Yeah, everything cool everything cool I just wanted to see if you guys would be willing to give up the guy who was the best tight end in the league last year Because you guys got some unproven receivers.  How about you do that? That's what this trade is saying It makes a problem an even bigger problem Because we know yeah right now Ravens got a lot of a lot of unproven guys at the receiver position but why Why would you create a hole? to just add another unproven guy To your group of unproven guys It's like Mark. I don't even know I'm talking so much about this anyway he said Mark Andrews tied for the third most receptions for a tight end in a season in 2021 he also had the third most receiving yards and see with that with Mark Andrew I mean, this ain't got to be explained to none of y'all because he already know but Mark Andrews show like hey I can get it done with anybody Obviously got it done with Lamar Jackson's like, okay, that's expected Tyler Huntley came in there nothing changed and then Josh Johnson was in there Mark Andrews was still putting up numbers So those are those players that can do that Those in my eyes are the elite of the elite when they have every single reason every single reason to Be out of the games to not be a factor anymore But they still keep on trucking and they get it done and a lot of it depends on circumstance too But anyway, the Andre Hawkins is another one of those guys remember when he was with the Texans and they went through like 50 quarterbacks But he's still bold anyway He also had the third most receiving yards He's just the seventh tight end to finish a season with 100 exceptions and 1000 receiving yards Mark Andrews is just 26 years old and his prime years could still be ahead of him. I'm like I'm looking and I'm trying to find something that says Hey, it's a joke Hey, maybe this is an article from April 1st Hey, I'm just playing around Hey, I'm just messing around now Let me look at the the related posts because maybe they're the related posts They can tell the story of what type of articles he normally write So I look at the related posts. It says PFF drops crazy grade for quarterback Aaron Rogers Okay, ain't nothing crazy. Packers coach has strong words for absence Sammy Watkins. Okay Packers named his likely destination for unhappy Pro Bowl receiver Okay proposed trade between Panthers and Packers since veteran wide receiver to Green Bay See that must be talking about Robbie Anderson. So, you know, I do like that. I do like that that he Be he be throwing some different trace scenarios out there. We do the same thing on here too, but this like This oh This yeah, no, man So I actually do think that Yeah, I think he's serious about it. He was serious about that proposal serious about that like just that the cut the construction of that trade and I feel like whether somebody was a Ravens fan or Packers fan, I Think when you think about a scenario like that, and I know it's the off-season. So our minds they get to wondering that much more So ain't got no problem with that. That's cool. It is what it is but to think of something like that and And Yeah, he went for it. So, you know what? I respect them for going for it I do think it is a little crazy not him itself But the actual trade like the Ravens giving up one of the best players not even on offense But one of their best players on the team To get back Runyon or 10 10ian tonion. I don't even remember his name right now. And uh, and am I right is really man, I I I mean sometimes we'll throw some stuff out there, but I Don't know man. It's just that's just wild to me. So My apologies if you was getting ready to go to bed and in this little notification woke you up Go back to bed and enjoy your rest or if you just waking up whenever whatever time you're watching this I Just I just had to share with y'all how I felt about that man because that one was That this one got a big yikes from me a real big yikes from me because like I said, I was seriously scared that like this was Something like real like serious like hey. Oh, yeah package. We gonna get Mark Andrews Well, we will take Mark Andrews and and what a fifth round pick and The Packers are send over to Titan and Amara in a third Like for you to try to Ravens like that For you to try them like that at least if you're gonna do something like that You're gonna propose something like that then you got to at least throw in two first round picks straight up and You know and it's Mark Andrews now man. It ain't just no random type. It's Mark Andrews You got to throw in at least two first That like all right.  So anyway, I love y'all team keep it clean. I appreciate y'all It's getting ready to be June 1st. Maybe by the time you watching this, it'll be already be June 1st, but um just Just keep on a lookout for these Just talking some of these articles and there'd be some videos too now but just Cuz I Don't because we get some questions from subscribers. We get some questions from subscribers that I put out some stuff Oh, okay Okay, I respect the greed and again, I do respect the greed But I mean, I don't think we've ever gotten anything like that before like anything that crazy But it is what it is team keeping clean appreciate y'all I love y'all and like Mark Andrews won't be anytime soon when it comes to being with the Ravens I'm out


MOOC is basically massive open online course. So massive meaning a lot of students take part in it, open meaning that it is open to anybody, there is no admission process as such, there is no entry criteria, online meaning it is delivered using the internet, put up on a server, anybody can access it, courses of course, course. So distance education has been around for a long time, everybody knows what distance education has been.  So in the MOOC model, it is almost like it replaces the distance education model of the past. But instead of giving the course material, shipping the course material physically to people, the course material is made available on a web portal, people register for the course, it is open that is the new thing about MOOCs, it is open. So anybody can register for it, anybody who has access to the internet can go online to that web portal register and they get a login and password and they are registered. And the material becomes available to them immediately. But the interesting thing about what is being given as MOOC right now is that it is organized exactly like a university course. So it is not that all the material is given to the person immediately, some material is released in the first week and the student has to study it and at the end of it there are some assignments that they have to do and after that some more material is released in the next week. So it almost runs like a university course. So you go for a few lectures every week and you do assignments and then the next week you go for a few more lectures and some assignments etcetera and it mirrors university course. So well there has been different formats for MOOC lectures, usually they are short. So they do not have a long 50 minute lecture for it but it is not really a format of the MOOC as such but it has got to do with what people can, what kind of time people will have with them. So for instance when students are assumed to be busy in college, so they may not have all the time in the world. So they would have maybe short periods of time in which they can quickly watch one module. So that is one way of thinking about it but usually the modules are short and there is no real reason for why there is a format but you know that is the accepted way in which MOOCs are delivered, short modules so that can be assimilated very easily. This MOOC specifically is on computer science, basic computer science and specifically we focus on programming. So most people would have studied programming in some form or the other in school and there are lots of institutes out there which offer programming courses. So what is different about this specific course? The main thing is that we are not just doing programming, we are also doing data structures and algorithms. So these three together programming along with an ample dose of data structures and algorithms really is what is needed to make somebody job ready in the IT industry particularly not just in the IT industry, in any industry today people are expected to program and not just write program any program that works but write good programs, efficient programs and for that you not only should know programming which some people might say just the syntax of the language, it is not enough to know the syntax of the language, you should know something called data structure, how to store the data efficiently and you should know something called algorithms, how to process the data efficiently and write efficient programs. So since all of these things are coming together I would more or less say this is an introduction to computing kind of course, how to write programs, how to write efficient programs, how to write good programs. So it is a nice bag of topics which you would probably find in multiple courses in engineering colleges but we have collected these topics together with the aim of making people job ready for specifically for high tech industry, any industry that needs program. It is not aimed at a very high level, so the MOOC lectures, the background that is needed for students is more or less 12 standard level, we are not expecting anything beyond that, some basic algebra and calculus and basic mathematics is probably needed but we are not expecting anything beyond that. So we do not expect researchers in this area to be taking part in it as students but definitely people who want to be job ready this will help a lot and people who are already in companies maybe they are fresh recruits going through their training process for them this will be of invaluable use. In this specific MOOC we are doing programming like I said and programming assignments are nice in the sense that we can evaluate them automatically by computer online. So there is a portal in which there will be an editor that will pop out and you have to key in your program and then you press submit and the program gets submitted and some server compiles it runs it checks the output against some preset actual output that it should obtain and if the outputs match it gives you marks. So that is how the assignments are graded. So that is the nice thing about programming everything can be done online. So all our assignments that is the other thing I should point out all the assignments in this MOOCs are going to be programming assignments more or less there is no simple multiple choice I mean there will be also some multiple choice questions but really multiple choice questions are not going to measure how good a programmer you are. So you really need to actually program. So more or less all the assignments in this MOOC are program based. So NPTEL is the organization which is organizing the content behind the MOOC and NPTEL is a project under the Ministry of Human Resource Development and it is a project that has been running for about 10 years now we have a collection of lot of course material. In this specific MOOC we have had faculty from the computer science departments of several IITs who have come together and have created a syllabus. And this syllabus was created after several meetings and in those meetings we had participation from IIT companies such as TCS, data consultancy services and CTS, cognizant technology solutions and NASCOM also. And through NASCOM a lot of other companies have also contributed to the content creation process in the sense I mean in the syllabus creation process. Now after the syllabus was formed lectures were recorded and for those lectures faculty from IIT have contributed. So different faculty have contributed faculty from IIT Madras, IIT Kanpur, IIT Kharagpur, IIT Goahti all of them have recorded lectures eventually these will be released in the MOOC. So that will happen later but the content creation industry has played a role in the syllabus creation and IITs have created role played a role in the content creation. Now we will also have some special lectures from industry. So that is to provide an industry view of how these topics are useful in say in applications that people have seen in industry. So we also have industry lectures but those are different from the main course content lectures. The main course content lectures are from the faculty at IIT and industry lectures are to supplement this effort also. So the recording of the lectures happened in the NPTEL studios in IIT Madras in Kanpur, Goahti and Kharagpur. So through the NPTEL project the IITs are equipped with really high quality studios where one can record lectures very easily HD format and all the latest technology is out there and the other part of it is the portal itself. Now the web portal also plays a very important role in delivering the MOOCs. The students log into the web portal, they see the lectures on the portal, they submit the assignments on the portal so everything happens on the web portal. And for the web portal we have a very strong association with Google. So Google through its course builder platform and in fact they are modifying the course builder platform to host this MOOC and Google is also offering services for hosting the portal so that cost is also being provided by Google. So we have a very active partnership with the engineers at Google and the team in Bangalore as well as there is a small team in Palo Alto which is working on this. So they are creating the portal. So the portal is one part of it and the studios are the other part of it. Of course the content input comes from the faculty you know. What student will need definitely is a computer and an internet connection. And the internet connection it is good if it is broadband but even I would say even let's say a 2G dongle the USB dongle that people have a 2G one might also be good enough but if you have a 3G connection that's great but an internet connection is definitely a must and a computer is a must. Now it might so in case as a student if you don't have access to the internet if you don't have a computer at home we are hoping the colleges will get involved and help out in getting the students access to internet and computers. Most colleges today have internet access and if they are involved then the internet connection and computers can be provided by the college and then the students can access it through the college. As a student this is an exciting opportunity to learn basic programming and data structures and algorithms which will first of all give you the essential requirements for becoming job ready in the IT industry and the high tech industry. But not only that I think it will take you towards an analytical approach to problem solving. So that is that is quite important as you as you become better and better engineers to to be a good engineer in general you have to be able to solve problems. Not only just by practice but you should also have an analytical reason for why you are solving the problem in that specific way and this course will really teach you about that. And you might be surprised at how some simple things which you might have done in very inefficient ways can be suddenly made very efficient if you analyze it analyze it carefully apply your mind and solve the problem very nicely. So I hope you enjoy this course and all the best.


Our fifth presenter is Jeremiah Milbauer, whose presentation is titled Knowledge Augmentation with Artificial Intelligence. From writing to libraries to telecommunications and the web, humanity has always invented new tools to organize and share knowledge. In my thesis, I argue that artificial intelligence could be the next great knowledge tool if we use it to augment our intelligence rather than replacing it. So my work focuses on AI systems that help us explore the connections between ideas and across contexts. So imagine you're reading a document and every time you encounter a new idea, it's highlighted and annotated. Imagine automatic fact verification embedded in every news article, explanations in every lease agreement, or background info summaries when you read a scientific paper from a new field. No more helpless googling, no more endless Wikipedia tabs. Artificial intelligence has the potential to embed an expert-level knowledge discovery process directly into our lives. So I began my research by looking at high-level connections across communities. I used natural language processing to train conceptual maps of ideologically distinct communities and then mathematically aligned those maps to find where the communities agreed and disagreed. This technique was able to automatically discover instances of coded in-group language as well as conceptual analogies across community divides. Essentially instances where people use different terminology to talk about the same shared underlying ideas. Now these techniques could help scientists connect new ideas to their existing expertise or help all of us explore new information when we don't really know how to search for it. My work has also looked at sort of specific connections between ideas in knowledge artifacts like documents. So normally comparing documents at this very, very fine detailed level would be too computationally expensive for artificial intelligence. So I developed an algorithm which accelerates this by storing and reusing the artificial intelligence's own internal representation of each sentence. And this year as a night fellow at CMU's Center for Informed Democracy I applied those ideas to build a tool which turns news articles into documents like you see here with annotated links that connect disputed and supported information across a whole collection of related news articles. Going forward I plan to collaborate with law librarians to apply these same techniques to trace the lineage of ideas through the history of Supreme Court cases. So from law to the news to science and beyond if we use artificial intelligence to augment our intelligence we can accelerate discovery and democratize access to information for everyone. Thank you for listening.


Trojan horse is not a Malware type. Weird statement, since it is listed as a Malware type literally everywhere, but hear me out. Everyone is probably familiar with the definition.  While the Trojan horse used by the Greeks during the Trojan War carries soldiers in its belly, the Trojan Malware has a malicious payload hidden in the useful program. Now there are two problems with this definition. First, while everyone explains it this way, the term Trojan is actually not used in this sense. And secondly, it actually describes an infection vector instead of a Malware type. Now, let me explain in detail. In the past, that is like 30 years ago, viruses and worms made up the majority of Malware. And other Malware types were rather rare. Unlike today, worms were also categorized as a subtype of virus. More specifically, they were defined as network viruses by some researchers. So that is why antivirus products are still called antivirus. More appropriate name would be anti-Malware. And some of the newer vendors have used anti-Malware for their products to stand out, but there's no real difference between those products. So whether they call themselves antivirus or anti-Malware, they all protect from Malware. Viruses as well as worms spread on their own. So once a virus is in the wild, a Malware operator just has to wait for more machines to become infected. They don't have to do anything. In contrast, non-Viral Malware spreads or requires constant effort to infect new systems. For instance, sending infection carriers. Such a carrier could be the Trojan horse and its useful functionality provides a compelling reason for a user to run the Malware. In the past, the benign or useful portion of the Trojan horse was more a fixed trade that belonged to the Malware family. For instance, the AIDS ransomware, I think the first ransomware that existed, came alongside an AIDS prevention program which was distributed with a diskette via mail, not email mail. The AIDS prevention program and AIDS ransomware were tied together and didn't exist separately. At the time it made sense to have a Malware type called Trojan horse because it was fixed, you know. However, the threat landscape changed tremendously and nowadays the majority of Malware is non-Viral. Worms are commonly not seen as a subtype of virus anymore but rather a distinct separate type and non-Viral Malware quite often has some Worm component or functionality but it is rarely the main focus or the main way they are spread. So the main infection vectors are nowadays email attachments, malicious software downloads, infected websites and similar Malware downloaders or carriers. These malicious carriers may or may not involve a benign program as a lure but this has become detached from the Malware family itself. How so? Well, people use tools to bind or join Malware programs with benign programs and they may or may not use these tools at any given moment. So further more development and spreading of Malware has become a separate task. So these tasks are often done by different groups of people. So spreading Malware is a business, it's a service you can buy and because of that some Malware ends up being spread by a multitude of ways using different services by different threat actors. So unlike viruses and worms, the other infection vectors are not an integral part of the Malware code anymore and the non-Viral infection vectors are ever changing and therefore not a characteristic or fixed trait of a Malware family. Malware types like keylogger, virus, red, stealer attempt to classify Malware families for their fixed characteristics that means they describe behavior and features which are part of the main code and rarely change but that's not the case for Trojan. The Trojan horse is just one possible way a certain Malware spread and of course you can attempt to distinguish Malware samples based on infection vectors, you could do that but in that case Trojan as a type for Malware makes as much sense as email attachment for Malware type. So but we do not list email attachment or email links alongside keylogger, red and stealer, right? These are different categories. It's like comparing apples with Cushion sofas. I mean, you can put an apple on a Cushion sofa but comparing them for their nutritional values, tastes and sitting comfort is probably not useful. Why is the term Trojan still used so much though? I can only guess but I think one major reason is how prevalent the term in detection names is. Detection names are the names intervirus vendors use for Malware detection. So that's what you see on Virus Toto if you upload a Malware sample. Many of them have Trojan as default type if the Malware is unknown. That's likely a legacy of the threat landscape 30 years ago. Having detection names for unknown Malware is very common because many detection signatures nowadays are created by automation without knowledge of the family or the type. So you have a lot of unknown Malware that's called Trojan in detection names. And many people use detection names to determine the family and the type of the Malware to have a name for the Malware. So Trojan ends up to be commonly used in media and online descriptions of Malware. And that term when it is used from detection names is meant in the sense of non-viral Malware instead of the original definition which includes a benign program as a law for the malicious payload. So the Trojan as a matter of type has become part of learning material for certifications for IT security degrees. And it is so popular you see it everywhere and it's taught everywhere. But you will become confused at some point if you really dive into Malware analysis and you will also maybe note that most professional Malware analysts avoid the term because it's pretty fuzzy. And if they use it, they put an explanation to it. I hope this clears up some confusion you might have or prevents confusion you might have in the future. If you have any questions, please let me know in the comment section below and leave a like and see you next time.


OK, so we're getting ready now for the second talk of this session. The title is efficient and optimally secure key length extension for block ciphers via randomized cascading. And the authors are Peter Gazi, Stefan Osaro. Peter will be giving the talk. Thank you very much. So the topic of our paper is the question of key length extension for block ciphers. And therefore, I will start by recalling the notion of a block cipher and introducing the problem of key length extension before getting to presenting our results. So block ciphers are well-known and widely used cryptographic primitives. And we can see a block cipher as a mapping that takes n bits of plain text and k-pa bits of a secret key and outputs n bits of cipher text in such a way that for every fixed key, the mapping obtained is a permutation on n-bit strings to be decodable, to be decryptable, of course. And but for most of the applications that are using block ciphers, the security guarantee that we actually require from block ciphers is to be absolute random permutation, which means that the block cipher, when used with a secret randomly chosen key, is indistinguishable from a random permutation. So more precisely, we consider a distinguisher which can participate in two different settings. Either it is interacting with the block cipher that is used with a random secret key that is hidden from the distinguisher, or it is interacting with a randomly chosen permutation over the domain of the plain text of the block cipher. And the distinguisher is allowed to ask both forward and backward queries to each of these permutations. And after some time and such an interaction, it is expected to output a single bit representing its guess, whether it's in the left or in the right world. Then we can, of course, define the PRP advantage of this distinguisher against the block cipher, which is the difference in probabilities that the distinguisher outputs one in both of these worlds. And we can talk about the PRP security of the block cipher in question to be the amount of resources that an distinguisher needs to achieve a constant distinguishing advantage between these two settings. Constant meaning, for example, one half. We still need to make more precise what we mean, by resources, and what are the resources that we care about. And I will do this. But before that, let me make a simple observation about the security of block ciphers. If a block cipher is to be a good pseudo random permutation, it is essential that it's killing this sufficient. This is because the distinguisher can always mount the following attack. If he's given an oracle, which is either the permutation or the block cipher, he can issue a single query to this oracle, let's say, all zeros, and then try to encrypt the same value of all zeros using the block cipher E with all possible keys on his own. And of course, comparing the results to the result that was obtained in the first step. And if a match occurs, this suggests that the key that was used for the encryption is actually the key that is being used for the block cipher, and that the distinguisher is actually talking to the block cipher, so he's in the right scenario, not in the left one. And this can be easily verified by the distinguisher by using a different input. And this whole attack just requires two to the kpa, evaluations of E. So this is an upper bound, since it allows the distinguisher to succeed in his distinguishing task, this is an upper bound to the PRP security of the block cipher E, which can be a problem for block ciphers such as this that have an unacceptably short key length, only 56 bits. But apart from that, they do not show serious structural weaknesses. It's also worth mentioning that this attack is generic, meaning that it can be mounted on any block cipher, even one that is flawlessly designed. So it upper bounds the PRP security of any block cipher. And motivated by this trivial brute force attack, the question that we address is that of a key length extension. So if we are given a block cipher E, our goal is to come up with a construction that uses this block cipher and results, again, in a block cipher with the same block length, but with a key length that is greater than the previous one, and does it in such a way that also the security increases with respect to generic attacks, meaning that the best generic attack on this construction E prime requires more than two to the kpa construct queries or evaluations. So it requires more effort than the brute force attack on the underlying block cipher E that I described. In order to capture these generic attacks, the model that we use is the ideal block cipher model. And we model the underlying block cipher E as being ideal, and therefore providing an independent uniformly randomly chosen permutation for each of the keys. So the actual formulation of the key length extension problem in the ideal cipher model looks as follows. We have a distinguisher that is, again, in one of the two worlds. And in the first world, he's allowed to query a random permutation. And in the second world, he can query the key extension construction. But apart from that, he's also allowed in both worlds to query the ideal block cipher E, which in the right world is actually the same block cipher that is underlying our construction. And he can query this block cipher in both directions, meaning he's allowed to issue encryption and decryption queries under an arbitrary key of his choice. Then we evaluate the complexity of such a distinguisher to be the total amount of all queries that it has issued during its interaction in the experiment. And now we can make the PRP security definition that I mentioned before more precise by asking how many such queries does the distinguisher actually need to achieve this constant distinguishing advantage in distinguishing the two settings described above. So this is a key length extension problem. And before I move to our results in this area, I would like to mention two existing approaches to key length extension. The first one is cascading. This is based on the easy idea that if you have a block cipher, what you can do is apply it multiple times with independently chosen keys. And what you get is an encryption by a new block cipher that has a greater key length. The trivial application of this approach leads to double encryption, but it is well known that double encryption is susceptible to the mid and the middle attack, which I will now briefly review. So what the distinguisher can do when he's given an oracle, which is either a double encryption or a random permutation, he can, again, query this oracle for a value, say, all zeros. Then he can use his block cipher oracle to encrypt this value all zeros under all possible keys, obtaining all possible intermediate values denoted by you here in the figure. Then he can do the same from the other side. So he can take this value y that he obtained in the first step and try to decrypt it with all possible keys, obtaining another set of possible intermediate values. And then he can just try to find the match between these two sets of values. And if such a match is found, it suggests that the corresponding keys were the keys used in the double encryption, and that the distinguisher is actually talking to the double encryption and not to a random permutation. And this can, again, be verified by using other values here. And we can see that this attack only requires two to the k-pa evaluations of the block cipher and its inverse. So this gives us that the double encryption doesn't provide any significant security increase, which leaves us with triple encryption as being the shortest possible cascade where we can expect some reasonable security increase. And indeed, it was shown by Belar and Drogaway that triple encryption is secure up to two to the k-pa plus minimum of n-half and k-pa-half queries, also in the ideal cipher model. And there is also an upper bound on the security for the case of triple desks used with independent keys, which was given by Lukes. And it was also observed by Uli Maurer and myself that for the case of longer cascades, the security improves further with the length of the cascade if the block cipher has smaller key length than message length. So this was the first approach cascading, and the second approach that I want to mention, because it will be useful later, is the approach of key whitening, which is, for example, used in the desks construction proposed by Rivest. And it's based on the idea of using additional keys to hide the inputs and outputs of the block cipher by a simple XOR operation. Indeed, this construction was studied also by Even and Mansour, as discussed in the previous talk. And it was shown to be secure up to 2 to the KPA plus N half queries in the ideal cipher model by Kilian and Rogeway. And this proof also covers the case if the two keys used for the whitening steps are the same, so if there is just a single key for the whitening. So from what we have seen so far, we know that none of the efficient constructions that we mentioned so far was secure beyond 2 to the KPA plus, the minimum of N half and KPA half queries. And if we want to achieve a security beyond the basic level of 2 to the maximum of KPA plus N, KPA or N, maximum of KPA or N, we have to pay a price for that in terms of efficiency, because we have to use the triple encryption, and that requires three block cipher queries per single invocation. So the question that we actually ask is what can be achieved with more efficient constructions? And we take a look at constructions that issue at most two queries to the underlying block cipher per invocation. So let me now address this question by first showing what we cannot achieve and describing some generic attacks that we give in the paper. First, we focus our attention on one query constructions that only issue a single query to the underlying block cipher. And we show that none of these queries can be expected to provide a reasonable security increase because they cannot be secure beyond the bound of 2 to the maximum of KPA and N. I will briefly sketch the proof of the attack, but only for a special case of constructions that are injective in the way that for a fixed key, distinct inputs to the construction imply distinct queries to the underlying block cipher. This is a special case, but in the paper we prove the statement for all one query constructions. So what the distinguisher can do in such a setting, he can first issue 2 to the N plus K k-pah of random queries, distinct queries to the underlying block cipher. In the second step, he issues the same number of queries to the Oracle O, which is either the construction or random permutation. And in the third step, he doesn't issue any queries to the Oracles at all. He just performs some computation. More precisely, he tries to evaluate the construction by himself on all the values that were queried in the second step on all these y values. But of course, he can evaluate the construction only if he knows the answer of the query that the construction would ask to the underlying block cipher. And the only way he could have learned this value was in the first step by querying the block cipher itself. But based on the numbers of queries that were asked in the first and the second step and on the injectivity property that we assume, we can expect that at least for one y value, this will be possible. And distinguisher will be able to evaluate this construction. He will try to do it for all possible keys k prime. And of course, if he manages to evaluate the construction, he will compare the result with the value that he obtained in the second step. And if he's actually talking to the construction, this check will succeed if the right key is used. But in the case he's talking to a random permutation, the check will most probably fail. So this allows him to distinguish. It takes some more technical work to show that constructions that don't issue injective queries do no better in this aspect. And then combining these two claims, we can arrive at the above statement for all one query constructions. So this, of course, leads to questions about two query constructions. What can we say about them? Here we also give an attack, give an upper bound on security. We show that for a wide and very natural class of two query constructions, they can achieve at most two to the k-pa plus and half query security. And this is, again, the class of constructions with injective queries, meaning that for a fixed key, distinct inputs to the construction imply distinct first queries. And distinct outputs from the first query imply distinct second queries by the construction. But if we look at the bound, we see that this already allows for some significant security improvement. And therefore, we can look for two query constructions that would achieve this. And this is what we actually provide as the main result of our paper. So let me finally get to presenting our construction. As you can see, it's very simple. It uses the paradigm that was discussed also in the last talk. And it is just a double encryption with two additional whitening steps, one taking place before the encryption and one taking place between the encryptions. We use the same key set for both of the whitening steps. And for the encryption steps, we derive the second key from the first key by a simple publicly known operation, like just a single bit flip. So we need an n-bit long key for the whitening steps and the k-bit long key, k-pa-bit long key for the encryption steps, meaning that the total key length of our construction is k-pa plus n. And the main result of our paper is that actually this double X or k-skade, as we call the construction, is secure up to 2 to the k-pa plus n-half queries. So it meets the upper bounds that was given by the generic attack that I presented earlier on two query constructions. And in this sense, optimal in this class. I will not give the whole proof, of course, of the security of the construction, but I will just mention some important points. So let me recall that the initial setting that we are considering is the setting with a distinguisher that this has access to an ideal block cipher and either a random permutation or a block cipher and our construction using this block cipher. And the goal of our proof is to show that the distinguishing advantage of this distinguisher must be small if he issues significantly less than 2 to the k-pa plus n-half queries. How we do this is that we actually reduce it to a simpler combinatorial problem and show this to be hard, which is, again, related to the previous talk. So the new problem that we are considering here is the problem of distinguishing only permutations. So it doesn't involve block ciphers at all. And the setting looks as follows. We have a distinguisher who either is given access to two permutations that are independent and uniformly randomly chosen, or he is given access to two permutations that are randomly chosen but correlated in such a way that they satisfy this equation for a random secret value z, meaning that if they were used in our construction instead of the encryption steps, and z would be the whitening key, then the whole construction would result to an identity. And we show that distinguishing these two settings is hard for less than 2 to the n-half queries. And we also show that this actually implies the desired bound on the security of our construction that I claimed. So before I get to some final remarks and conclusions, let me just mention one last thing on the design of our construction. One might be tempted to consider even simpler constructions using just a single whitening step, either before the encryption steps in between or after, after two encryption steps, or one might want to consider a construction where the two whitening steps are used in a more symmetric way, meaning before and after both encryptions. But it turns out that all of these constructions can be attacked in two to the maximum of k-pi and n-queries, and therefore do not provide the desired security increase that we achieve by our construction. So let me summarize the contribution of our paper. We presented new key length extending construction for block ciphers, which is both more efficient than triple encryption because it requires only two block cipher calls for invocation. And it is also more secure than triple encryption for certain parameters of k-pi and n. Here you can compare the two bounds. And we also give generic attacks that support the optimality of our construction within the class. These attacks show that any one query construction can be attacked or is insecure for more than two to the maximum of k-pi and n-queries. And no two-query construction from the class of injective constructions can be secure beyond the bound of two to the k-pi plus n-half, which is the bound achieved by our construction. So this is our contribution. Thank you very much. We do have a little bit of time for a question or two. One of the types of queries and said either one of them is costing you a unit of query. It might be a bunch of clearly distinguished between the two because one of them is really related to the amount of data available and the other is related to the amount of time you apply your attack. So how would your results change if you tried to distinguish between the two types of queries that you could do in your own? Well, it is true that these are two different types of queries that represent something else or two different things. Actually, some of the results that I mentioned do make distinction between these queries. For example, the result for desks. But we took the more common approach to consider only the sum of the queries as the complexity measure for the distinguisher, which is the usual way. I think you typically, if the bound is two to the k-plus and a half unit, for example, in the attacks, you need to ask two to the n queries of the more complicated type that you discussed. So that you named as a more complicated type. So this would be the more fine-grained formulation of our results, but we give the sum of the queries. Well, since we are working in the ideal cipher model, we don't need any additional assumptions about the block cipher. This is sufficient because in the ideal cipher model, the permutations performed for each of the keys are independently random. But in practice, if you would use a block cipher in this construction, you would need some degree of some security against key related attacks, but in a quite mild form. But of course, you could use this construction also with independent keys for the two encryption steps. The whole proof would go through exactly the same way. Only you would have a larger key length and the same security. So you could use independent keys if you wanted. But in the ideal cipher model, you don't need to. Okay, so let's thank Peter.


Ya know, I don't talk during the day but this one is a family kind of matter so I'm going to talk about it during the day. Ya know, I posted, I was making my, a number of people have made comments about the wedding of President Hlima's daughter. A number of people have made comments. Inendan the kofi, you have stood what nivaka panga, awale wutuka umurilo, umun panga. Kuchutren, kuchutren, kuchutren, bla bla bla. Don't talk about family. Nonsense. Nonsense I want to tell you. Nonsense, you talk about my family day in, day out. You talk about my family, even HH himself speaks about my family. Even HH speaks about my family. Nomba inendan the kofika, no, no kofi. Iwedini. I mean, and what I've said really is just the truth. You may not like it but it's just the truth. Inendan the kofi panofi, na mwima, like monkeys In the field of bananas. Muima, Muima. Muima nvaimofu mulanda. You are forgetting what you talk. You are forgetting what you talk. You talk about my family, you talk about me. You talk, you talk. Inendan the kofi. Na kufi, na takuwadi kamera. Takuwadi a kuitama kamera.  Honesty nangu fiatisha ni siha nangu futapa soswa. When they are going about doing their business and everything they should they should bear it in mind that they have got a father who is a public figure and whatever they do is going to reflect on the father.  Forget it. Why is he hiding his children. Why he hiding his children. Why is he hiding his children?   Who are the parents of that guy? Who are the parents of that guy? Who are the parents? They were all overshadowed. It is only HH and WTT. Is that a good example of family unity, because when people are getting married two families are coming together. So what values are you showing when only one family is prominent? What are you showing? It looks that the guy is there. We have been told that he is a husband, but I mean the guy is nowhere, he's nowhere, he's just nowhere, the guy is nowhere, we don't even know, I mean does he have a family, does he have parents, I mean because that is supposed to come out, those are our values, in a marriage it's two families coming together, did we see in that wedding, two families coming together or we only saw HHH dancing and we heard on the news, this one has given so many cows, this one has given wateba wateba, that's all, that's all. So, give me a break, ay, give me a break, I haven't done anything wrong, I haven't done anything wrong, don't say blah blah blah, don't talk about family, a family of a public figure is discussed, man is of course is something else, deformation is something else, but if you are really discussing the truth, there is nothing wrong, if you are lying against the family or anybody, that is wrong, if you are being malicious against anyone, that is wrong, but discussing things as they are, there is nothing wrong, there is nothing wrong, but in way you are malicious, you are defamatory, you discuss other people because the family is full of lies, not the truth, and we don't talk, imo to ramdeko, wusha kwa na takuwadi ko smartphone, takuwadi ko ama iphone, ni hako pako ma pictures, asuma, ni hako pa social media, takuwa adeko atiyo stop involving the family, kabian yu komu miki se mwangberisha, bye.


Hello, everybody. Welcome back to another video. In this one, I'm going to show you how to create a custom GIF or GIF just like this. I've got a GIF here running inside Google Chrome and it ends in .gif. That's how you know it's a GIF and I made it. I'm going to show you how to do it in Premiere Pro. It should only take about a minute or two. Let's start from scratch. I'm going to click into Premiere Pro and you'll see here I've got some stuff going on. I'm going to delete all this and show you how to do it one by one, step by step. Okay. First step, grab your Finder or your Windows Explorer. Find the movie or the video that you want to use. In this case, I'm going to use the first five seconds from the movie End of Watch. The reason why I'm doing that is because I like the sailboat slash pirate boat at the beginning. I'm going to drag that into the project panel. Then I drag it into the timeline. Now, the whole video is here and I don't want that. I just want the first five seconds. I'm going to zoom way in and I'm going to go to the first five second mark. I'm just going to move the playhead to where it says five seconds. It's not quite there, so I'm just going to go step back one, two, three, four, first five seconds. That's what I want. Now, I'm going to go ahead and cut it here. So Command K on a Mac, Control K on a PC, or you can just use the Slice tool. And I'm just going to delete the right. And now all I got here is the five seconds that I want to make a GIF of. All right, hopefully you're following along with me. Now, up at the top here, you'll see File, then you'll see Export and then you'll see Media. This is where we want to be. Right now, I've got an animated GIF format selected, but in all probability, you're going to see this here when you first do this. It's going to be Format H264 quality, whatever your video quality was by default. What you need to do is you need to click on this format dropdown, select Animated GIF. Then go down a little further here and we can make a couple of changes. We can go ahead and adjust the frame rate. This is one that I tend to adjust a little bit. The reason why is it comes out, whatever your video that you put in, that's the frame rate that it's going to render at. For animated GIFs, sometimes you want to go for that 90s low quality, before video got compressed, ugly stop motion look. You can do that by selecting a frame rate of let's say 10 or 12.5 or 8, and you can get that old school 90s look. You don't always want it, but if you want it, this is how you do it. You can also adjust the frame size. If you choose, so right now it defaults to whatever your video was. But if you want it to be wider or you want it to be narrower or higher or taller, etc., you can go in here and adjust it. You can also unlock this button here to make it not the aspect ratio can be adjusted. So you can adjust the aspect ratio and the look of it. I'm going to leave it as is. I'm going to go over here to the right side. I'm going to make sure again that it's going in as a GIF. It's going to my desktop. I'm just going to go ahead and click on export takes a few seconds and then presto. I'm going to go over to finder. Then I'm going to switch back to my desktop and that should be done by then when I get here. Where is my desktop? There it is. And then presto end of watch.gif or .gif. Now to just check it, right click on it open with and then I am going to select Google Chrome and it works. You can make a GIF or a GIF of anything inside Premiere Pro. That's how you do it. Thanks for watching.


You may excess power, but the question is, will you be able to handle it?  Anticlock-wise of any powerful space is essentially about that. At Matu Tipila, the trail goes around in an anticlockwise direction. So all the visitors are circumambulating in a counter-clockwise direction. Could that have a negative effect on them? So this going around Matu Tipila in an anticlockwise or a production as we call it, in the northern hemisphere it has a certain impact and the reason why any powerful space we say, don't go anticlockwise is, you may excess power, but the question is, will you be able to handle it? The moment you confront or come in contact with such energies, you must be of a certain stability. A certain level of anchoring should be there. Otherwise, the first things that you will encounter will be terrible forms of various kinds. You will see the Tibetan Buddhism also talking about terrible forms that you will encounter on the way of your sadhana and also the Indian tantric sciences also talking about terrible forms that you will encounter and how you need to go through this without flinching a bit.   So, this going around the Vishuddhi energy which has been symbolized as Shiva's throat, symbolized as poison gathered and if it turns the wrong way, vomiting blood could be a natural outcome. I don't know if there is lore and stories in native American culture of talking about such things of failed medicine men who went out to acquire something and they will die vomiting blood because Vishuddhi is in the throat. If it goes wrong, that's how it is. And Shiva is a blue-throated one because all the poison is here. Poison or venom should not be misunderstood as only life-taking.  It is just that some other creature is not able to handle it, that's all it is. So, going anti-clockwise and occult practices are always connected. Occult means another dimension of working our physical energies in a way that could be conducive or not conducive but essentially we are only working physical dimension of energies that will not come under mysticism, that will not come under spiritual process either. So, anti-clockwise of any powerful space is essentially about that. Mato-Tipila just hits you in the throat, leaning strongly. Strongly means very strongly towards Vishuddhi, almost like Shiva's throat. Anybody who lived around Mato-Tipila cannot help activation of their Vishuddhi. This entire tradition of vision quest could have only happened because of this one dimension of Mato-Tipila. There were medicine men who really enjoyed their Vishuddhi's power that everybody is scared of them. Where there is Vishuddhi, their disembodied beings will naturally gather.


And the first thing that I'll look at will be regression objectives. I'll go quickly through that. These are things that I'm sure you know, but it is just to recall that multiple regression, actually why I talk about regression is because it will be the first element of calculation that we will do tomorrow in canonical analysis. The first thing that we'll compute will be multiple regression for all the species in the response matrix. So in multiple regression, we can use it for description. And the aim then is to find the best functional relationship among the variables in the model. In multiple regression, we have one response variable and a lot of explanatory variables. And in my notation, the response variable is called small y and the explanatory variables, they are called x. And it can be, for instance, environmental variables. Tomorrow, we will do the same thing, but with having a whole matrix of y variables like this one. But today, we stick to a single response variable. So that was the first thing that we can do. We may want to find the best equations and then use the parameters, the regression coefficients to describe the effect of the different variables. We may want to use it for inference that means generalizing the results of a set of observations to a target population, the reference population for which the data are a sample. And if it is a representative sample, then we can generalize our results to the target population and test some ecological hypothesis about the target population. It may simply concern the existence of a relationship. Do we have an R square, for instance, different from 0 or for a coefficient, is the slope of that coefficient different from 0. In other instance, we can be concerned with the sign of the relationship and we can also compute confidence intervals instead of doing test of significance. That's another way of testing and so on. Sometimes the ecological hypothesis that we are entertaining specifies specific values of the parameters and that's fine, we can test them in this framework. And the last objective is forecasting or prediction which consists in calculating values of the response variable for points where we don't have an observed value. So we may have observed value for 100 points and in point number 101 we don't have an observed value. We can compute a regression model from the 100 points and then use that model and apply it to point number 101 in order to estimate a value. That's a way of doing imputation in the case of missing values and so on. Professor Scaldi mentioned this in his talk yesterday. So these are the main objectives. But tomorrow what we will do will be to use multiple regression essentially to compute the fitted values. My next point is to mention that we can compute the regression parameters using matrix algebra. I love algebra and in discussions with people during the icebreaker yesterday I realized that at least some of you like the algebra also. So I'm going to talk a little bit about algebra. You will see it is not as bad as it sounds. So this is a typical data set where we have a response variable and a bunch of explanatory variables here. So when we compute our regression equation we want to estimate the b coefficients that when multiplying the x variable will reproduce y as well as possible within the framework of least squares estimation. So usually we want to estimate coefficients b1 to bm for the m variable. And also we want to estimate the coefficient b0 which is the intercept. As Professor Scaldi mentioned yesterday we are often interested in an intercept. And this is done, we can estimate the intercept by adding a column of 1s to the table with the m variable. So it is another variable that we add here and it is a column of 1 and that is what allows us to estimate the intercept except in the rare cases where we want to force the origin to go through 0 and in that case we don't include that column. When you do a multiple regression using the lm function of r you don't have to include this column of 1 by and it is included automatically by the lm function. So you don't do it yourself, it's included by lm. Now, when we have that how can we solve this problem? We know y, we know x but we don't know b. And so we have to isolate b in some way in order to compute something using x and y. But if it was simple algebra we would divide y by x but division does not exist in matrix algebra for good reasons. There's not some imposition by mathematicians. Conceptually it cannot be done. Okay, so what will we do instead? We will use mathematics that implements the principle of these squares. So this is what we want to do. In order, instead of dividing y by x we can multiply y by the inverse of x just like in ordinary algebra if we have 5 divided by 2 it is the same thing as 5 multiplied by 2 to the exponent minus 1 or 5 multiplied by 1 half if you like. So that's fine. But in order to inverse x to obtain that for an x matrix that matrix has to be square. Oh, another constraint. So we will fabricate a square matrix. As follows, we will pre-multiply by the transpose of x. We take the x matrix, transpose it, turn it like that and pre-multiply here and there. So if this is true then this is still true because we have pre-multiply by the same quantity. Now this is a square matrix with the dimension of the number of columns. If we have m columns in x then x prime x has the size m by m. And this can be inverted because it is a square matrix. So we do that now. We invert this and pre-multiply it here and there. You see, this is the same bit as that. This is the same as that. So we simply add that here and there. And like in ordinary algebra, if you have a quantity multiplied by its inverse these two cancel out and they become one or in this case an identity matrix. So it disappears from the equation and on the left we are left with this and we can isolate b. And this is how we solve the multiple regression problem and it can compute all the b coefficients with one line of matrix algebra. Isn't that beautiful? I think this is great. And this is what we will be using tomorrow as the basis for canonical analysis. That's why it's important that I show you that today. Now of course we could also want to calculate the y-hat, that is the fitted value. That will be produced by taking the original x matrix and multiplying it by these b coefficients that are estimates of the original b. And if we replace b by this, we have x multiplied by b. And so it gives us this where we can compute y-hat directly from again a single line of matrix algebra. And actually this is the equation today in canonical analysis where small y will be replaced by large y and we will obtain on this side whole matrix of fitted values with one line of algebra, one line of r-code. Okay, so I hope I have convinced you that it is at least possible and this is something that you can try tonight in your room if you want to see how regression can be computed. It is really that simple. Now I want to talk about r-square and this sort of things. And this is in my chapter 10. There are a few pages from my chapter 10. Here it is. So I'll put it a bit bigger. You have this document among the documents that I put on the web page. A bit bigger again. So the first thing I want to show you is the r-square. And then we will talk about adjusted r-square. r-square is the basic statistic that comes out of a regression equation. Actually I remember a paper where they were showing 12 different ways of calculating r-square, all leading to the same result. When you have a regression equation then from the regression equation with lm you can produce then the fitted values y-hat using, for instance, the line of algebra that I showed before. So these are the fitted values. And the r-square will be the sum of squares of the y-hat divided by the total sum of squares that you had here. So r-square is sum of squares of the fitted values divided by the sum of squares of the original data. The sum of squares is simply you center the value that is, subtract the mean square the values and sum them. That's all. And this is equal to if you were doing sum of squares of y-hat divided by n-1 here and sum of squares of y divided by n-1 and this would be the variance of y-hat divided by the variance of y. So we don't really need these n-1s. They cancel out and it is more simple to write the equation in this way. But if you compute it with r maybe you want to compute the variance of y-hat at the top and the variance of y at the bottom it will produce the exact same r-square. Okay. So different ways of calculating and essentially if the y-hats are exactly equal to the y then your equation is as a perfect success and you predict the values perfectly. But otherwise in all the real cases the variance of the y-hat is smaller than the variance of the y because in the regression equation your points are not exactly on the regression line if you have your regression line your points will be all around it and there are residuals left so that the variance of the y the original y is here is larger than the variance of the y-hat.  Okay. Now this seems to be a very good coefficient but, but, but, but we also know from simple simulations that this is not the case and here I'm going to show you simulation results in a paper by Pedro Perez Neto who has made strong contribution and a guy named Daniel Barca and Stéphane Deray and myself but all the simulation work was done by Pedro who was then a postdoc in my lab and I want to show you one picture of that paper on page 2617 this the paper is available on our web page. This is the picture. We will look at the left panel here for this study Pedro generated first two variables in columns with 100,000 lines and he fiddled with his data so that there was a known correlation between the two data sets of 0.20 something or 0.60 something 0.61 in one case it was 0.608 in the other case 0.201 so then he divided his 100,000 data into slices of 100 points he had a thousand slices and for each slice he computed a regression equation and found an estimated R squared, estimated by the regression from a sample of the whole data set and repeated that a thousand times and he found that in the case of the points with 0.61 as real R squared he found this value now he started adding columns of random variables this is regenerated in R with the function R norm random normal deviates you ask for 100 R norm and you put it here then you add two such columns 2, 3, 4, up to 20 such columns so 0 is the real values for the real pair of data and then he adds columns into the X matrix here so he adds essentially no information there are random numbers and look at the R square it increases for 100 points if he has a total of 99 explanatory variables so the real one plus 98 of these phonate random variables he will reach an R square of 1 that is perfect fit that's what we should do in ecology we should add random variables and have high R square that would be nice to publish in our papers so we would have explained everything with nothing you see this is the danger of the R square it overestimates the true relationship between Y and X and this is demonstrated with these simulations and same thing here with the simulations starting with a true value of 0.2 20% so the R square increases like that to reach 1 at the end now back in the year 1930 Ezekiel not the prophet but the statistician Ezekiel then described something well known as the adjusted R square this is where it comes into the picture the adjusted R square is produced like this you take your real R square coefficient of determination take 1 minus that so the maximum minus that this is called the coefficient of non-determination in causal modeling now you modify it multiplying by total number of degrees of freedom N minus 1 and residual degrees of freedom minus 1 minus number of explanatory variable and when you have modified it like that you turn it back into R square by taking 1 minus the result and the adjusted R square are also shown in the simulations of Pedro here as these lines here and actually this is the mean over a thousand simulations and we see that the adjusted R square is very stable so it does not respond to the number of non-explanatory variables so that's very nice for us this is a way of removing the effect of variables that are not pertinent in our analysis so Daniel will describe later ways of eliminating these variables by variable selection but for a first approximation especially in canonical analysis we can simply leave all the variables there and compute the adjusted R square and the adjusted R square will be the basis for variation partitioning that Daniel will describe before that I'm almost done a few allow me to adjust a few more minutes so that was the story about the adjusted R square in this slide oh yes, and here in this simulation actually the paper by Pedro was about the use of the adjusted R square as the basis for variation partitioning that Daniel will explain and in this paper he showed after these basic simulations that adjusted R square was the quantity to be used for variation partitioning because it is an unbiased estimate of the explained variation that's the story you also find AIC the Akaike information criterion that belongs to the Bayesian thinking in statistics we use it a lot for variable selection and when you will be using the function rd step I think in the practicals this afternoon perhaps rd step will be based on AIC not this form but this form here at the bottom which is the corrected form corrected for small sample okay so AIC is very useful and the best predictive model is the one with the lowest value of AIC so it is not at all like an R square where we look for the highest R square here we will look for the lowest AIC and that produces that indicates the model when you are comparing different models the one that has the best predictive power another important thing is the F statistic but I will come back to the F statistic in multiple regression and we will use it again tomorrow in canonical analysis the F statistic is constructed from the R square and it is simply in regression and in canonical analysis it is simply R square divided by 1 minus R square and here we divide by the number of explanatory variables and here we divide by residual degrees of freedom that are like this so this is the basis for testing a regression coefficient and it will be also the basis tomorrow for testing the relationship in canonical analysis exactly the same R square okay I think that does it for this handout and the last thing is that I want to mention in multiple regression is that I want you to remember you probably have been told that but sometimes we don't remember that of course in the matrix X we can put quantitative variables but we can also put other things and sometimes people forget about that we get the impression that multiple regression is meant only for quantitative variables no we can put binary variables we can put factors because if you give factors to the LM function it will recode them as a series of binary variables as Daniela has shown you yesterday it will be done automatically now when it comes to geographic analysis we can put different types of geographic information we can use the latitude and longitude information from the sampling points we can use that as X and Y here in the regression equation but this is not very efficient a long time ago we tried to develop the X and Y geographic coordinates this is still useful in some cases to model broad scale spatial structures but it is not feasible to model fine scale spatial structures in that way now sometimes our geographic information comes in the form of regions we know that these points come from this region these points from that other region regions can be represented by a factor and they represent geographic information and finally we will see here I say in chapter 14 but on Friday we will describe these methods that are the spatial Eigen function derived from the geographic coordinates of the point but that are much more efficient than the polynomial to model fine scale geographic information and we will use them as explanatory variables in regression or canonical analysis so you see how things of this course will link to this idea of representing geographic information in the X matrix so I stop here coffee for everyone