DeepChanger

Bruken av AI teknologi er i dagens samfunn voksende, og brukes til mange dagligdagse formål. Med denne bruken kommer også bruk av AI for ondsinned bruk, med digitale angrep med hjelp av AI, eller angrep mot AI systemer. Denne oppgaven utforsker en ny måte digitale angrep mot AI systemer, hvor systemet angripes direkte, for å modifisere det og innstallere et eget nevralt netverk trent av angiperen, noe som fører til at systemet har funksjonalitet fra både originalt og angripers nettverk. Oppgaven ser også på måter man kan forhindre et slikt angrep, ved bruk av integritets-sjekk og autentisering av AI systemets kode og data. En annen måte å forsvare seg på omhandlet at AI systemet kjører tregere etter angrepet, noe som gjør det mulig å oppdage. Siden oppgaven viser til et praktisk fungerende angrep, vil det kunne føre med seg alvorlige konsekvenser dersom systemer med AI ikke forsvarer seg godt nok, som f. eks. selvkjørende biler.Today, the use of Artificial Intelligence (AI) technology is ever-expanding and used in many daily life applications. With this expansion, so does the use of AI in performing cyber attacks and cyber-attacks targeted at AI system to circumvent or disrupt the AI system. This thesis explores a new method of performing an attack against AI systems by directly altering the neural network (NN) the AI system uses. The attack is made by merging a secondary network, trained by the attacker, with the original neural network, which results in a merged network displaying both networks’ functionality. The thesis also explores how this attack can be prevented by implementing integrity checks and authentication on the data and code, which make up the AI system. Another defensive measure is based on the increased execution time of the AI system because of the more extensive network. As the thesis successfully implemented a practical model of this attack, there could be severe consequences if precaution is not taken, especially in safety-critical systems, such as self-driving car

Simulations of Characteristic Sand Behavior by DEM

For å utbedre kunnskapen om granulære materialer og deres oppførsel kan diskret elementmetode være et viktig hjelpemiddel. LS-DEM er en variant av diskret elementmetode, men skiller seg fra den tradisjonelle typen ved at nøyaktig kornform inkluderes i koden. Bruk av slike numeriske modeller kan til en viss grad redusere omfanget av nødvendig laboratorietesting, samt de begrensninger disse assosieres med. Videre kan LS-DEM måle rotasjon og geometrisk konfigurasjon av enkelte partikler. Dette er egenskaper som er vanskelige å undersøke i lab, dog betydningsfulle for oppførselen til granulære materialer. I denne oppgaven har resultater fra LS-DEM simuleringer blitt brukt som utgangspunkt for å kalibrere SANISAND-modellen. Simuleringene er utført med prøver av Hostun sand, som består av angulære og ikke-sfæriske korn. Det har spesielt blitt lagt vekt på definere en unik "critical state line", da dette foreløpig er utfordrende å gjøre med eksperimentelle resultater. SANISAND modellparametere er kalibrert ved hjelp av Pythonskript og PLAXIS Soil Test. Alle analysene nådde samme kritiske spenningsforhold. Videre nådde alle et vel definert kritisk poretall, utifra hvilke en "critical state line" ble kalibrert. Verdien av "bounding surface" parameteren er vurdert til å være nokså nøyaktig. Datapunktene nødvendige i kalibreringen av "phase transformation" parameteren var spredt, og det knyttes dermed usikkerhet til denne verdien. "Kinematic hardening" parameterne, kalibrert ved prøve-og-feile metoden, gir SANISAND respons som passer nokså godt med LS-DEM resultatene. Men, stor usikkerhet er knyttet til disse verdiene, noe det anbefales å undersøke videre. Videre vurderes dilatansparameteren å være veldefinert, dette fordi SANISAND korresponderer med LS-DEM resultatene for deviatorisk tøyning mot volumetrisk tøyning. Alt i alt gjengir SANISAND modellen oppførselen som er observert ved LS-DEM relativt nøyaktig. Dette underbygger både at SANISAND kan gjengi sandoppførsel under monotonisk belastning, men også at LS-DEM kan simulere karakteristisk oppførsel av friksjonsjordarter. Ved å erstatte de realistiske kornformene i LS-DEM med sfæriske korn, ble det undersøkt hvorvidt kornformen påvirker resultatene. "Critical state" parametere for Hostun sand ble i den sammenheng sammenliknet med tilsvarende parametere kalibrert fra analysene med sfæriske korn. Denne sammenligningen viser at analysene der kornform er inkludert når høyere kritisk spenningsforhold og poretall enn analysene med sfæriske korn gjør. Flere svakheter ved LS-DEM er belyst. Det er observert hopp i gjennomsnittsspenningene, noe som indikerer numeriske problemer i analysene. Disse variasjonene kan sannsynligvis begrenses ved å redusere tidssteget brukt i koden. I tillegg understrekes det at en betydelig mengde datakraft er nødvendig for å kjøre LS-DEM analysene. Med de kalibrerte modellparameterne korresponderer den oppførselen SANISAND forutsier med LS-DEM resultatene, også for uavhengige analyser. Det understrekes dog at å utføre en skikkelig validering av resultatene er nødvendig. Uansett har LS-DEM, tross sine begrensninger, fortsatt potensiale for å kunne bidra til å utvikle kunnskapen om granulære materialer.The discrete element method is proposed as an important tool in the quest of understanding the fundamental behavior of granular materials. LS-DEM is a discrete element model that, by level set functions, accurately incorporates the true grain shapes in its formulation. By using a numerical model, several limitations associated with conventional laboratory testing can be eliminated. An essential part of soil characterization is to achieve several different stress paths from an identical state. However, such experiments have shown to be extremely difficult to conduct in the lab, due to limitations associated with physical sample preparation and the influence of boundary conditions. This could easily be done with LS-DEM simulations. In addition, the simulation results can be used to investigate quantities like fabric and particle rotations, which are difficult to measure in the lab, and have proven to be important for describing the overall granular behavior. This paper presents how LS-DEM simulations can be used to calibrate the input parameters of the advanced constitutive material model, SANISAND. The simulations have been performed on Hostun sand, characterized by angular grains with low sphericity. Special focus has been set on defining the critical state line, which is currently challenging to establish from conventional laboratory testing. SANISAND input parameters have been calibrated using Python and PLAXIS Soil Test. It was observed that all analyses reached a well defined critical state, and a location of the critical state line is proposed. However, the critical void ratio was expected to be more pressure dependent than what was observed for initial pressures p=10-500 kPa. The bounding surface parameter is considered well defined, whereas the results needed to calibrate the phase transformation surface were scattered, resulting in uncertainty related to the slope of this. The kinematic hardening parameters, calibrated using trial and error, yield adequate average fits between LS-DEM analyses and the SANISAND response. However, the analyses run in this thesis are not sufficient for defining them uniquely. Determining these parameters is therefore proposed as an objective for a future study. The calibrated dilatancy parameter is considered relatively certain as its fit to the LS-DEM simulations appears accurate. To investigate the effect of incorporating realistic grain shapes in the DEM formulation, a selection of the simulations were run with spherical grains. The calibrated critical state line for Hostun sand was then compared with the corresponding critical state line for the analyses run with spherical grains. The simulations performed and investigated in this study shows that the sample consisting of true grain shapes reaches a higher critical stress ratio and void ratio than the sample with spherical grains. Several preliminary limitations regarding the LS-DEM analyses in this study have been discussed. Jumps in the average stresses at the boundaries were detected, indicating the occurrence of numerical instabilities in the analyses. These fluctuations can presumably be limited by reducing the applied time step. Moreover, it needs to be highlighted that the computational costs running LS-DEM constitutes a significant drawback of the method. Finally, with calibrated input parameters, SANISAND response and LS-DEM results correspond, also for independently run numerical analyses. However, it is necessary to perform a proper validation to ensure this correspondence. Regardless, despite its limitations, LS-DEM is still considered a promising tool to understand the nature behind characteristic behavior of granular material

Perceptions of Urban Transformation. A Sociological Analysis

Master i samfunnsvitenskap med fordypning i sosialt arbeid - Nord universitet, 201

Iron and sex matter in severe infections Epidemiological studies of bloodstream infections, sepsis and COVID-19

Digital fulltext is not availabl

Simulations of Characteristic Sand Behavior by DEM

For å utbedre kunnskapen om granulære materialer og deres oppførsel kan diskret elementmetode være et viktig hjelpemiddel. LS-DEM er en variant av diskret elementmetode, men skiller seg fra den tradisjonelle typen ved at nøyaktig kornform inkluderes i koden. Bruk av slike numeriske modeller kan til en viss grad redusere omfanget av nødvendig laboratorietesting, samt de begrensninger disse assosieres med. Videre kan LS-DEM måle rotasjon og geometrisk konfigurasjon av enkelte partikler. Dette er egenskaper som er vanskelige å undersøke i lab, dog betydningsfulle for oppførselen til granulære materialer. I denne oppgaven har resultater fra LS-DEM simuleringer blitt brukt som utgangspunkt for å kalibrere SANISAND-modellen. Simuleringene er utført med prøver av Hostun sand, som består av angulære og ikke-sfæriske korn. Det har spesielt blitt lagt vekt på definere en unik "critical state line", da dette foreløpig er utfordrende å gjøre med eksperimentelle resultater. SANISAND modellparametere er kalibrert ved hjelp av Pythonskript og PLAXIS Soil Test. Alle analysene nådde samme kritiske spenningsforhold. Videre nådde alle et vel definert kritisk poretall, utifra hvilke en "critical state line" ble kalibrert. Verdien av "bounding surface" parameteren er vurdert til å være nokså nøyaktig. Datapunktene nødvendige i kalibreringen av "phase transformation" parameteren var spredt, og det knyttes dermed usikkerhet til denne verdien. "Kinematic hardening" parameterne, kalibrert ved prøve-og-feile metoden, gir SANISAND respons som passer nokså godt med LS-DEM resultatene. Men, stor usikkerhet er knyttet til disse verdiene, noe det anbefales å undersøke videre. Videre vurderes dilatansparameteren å være veldefinert, dette fordi SANISAND korresponderer med LS-DEM resultatene for deviatorisk tøyning mot volumetrisk tøyning. Alt i alt gjengir SANISAND modellen oppførselen som er observert ved LS-DEM relativt nøyaktig. Dette underbygger både at SANISAND kan gjengi sandoppførsel under monotonisk belastning, men også at LS-DEM kan simulere karakteristisk oppførsel av friksjonsjordarter. Ved å erstatte de realistiske kornformene i LS-DEM med sfæriske korn, ble det undersøkt hvorvidt kornformen påvirker resultatene. "Critical state" parametere for Hostun sand ble i den sammenheng sammenliknet med tilsvarende parametere kalibrert fra analysene med sfæriske korn. Denne sammenligningen viser at analysene der kornform er inkludert når høyere kritisk spenningsforhold og poretall enn analysene med sfæriske korn gjør. Flere svakheter ved LS-DEM er belyst. Det er observert hopp i gjennomsnittsspenningene, noe som indikerer numeriske problemer i analysene. Disse variasjonene kan sannsynligvis begrenses ved å redusere tidssteget brukt i koden. I tillegg understrekes det at en betydelig mengde datakraft er nødvendig for å kjøre LS-DEM analysene. Med de kalibrerte modellparameterne korresponderer den oppførselen SANISAND forutsier med LS-DEM resultatene, også for uavhengige analyser. Det understrekes dog at å utføre en skikkelig validering av resultatene er nødvendig. Uansett har LS-DEM, tross sine begrensninger, fortsatt potensiale for å kunne bidra til å utvikle kunnskapen om granulære materialer.The discrete element method is proposed as an important tool in the quest of understanding the fundamental behavior of granular materials. LS-DEM is a discrete element model that, by level set functions, accurately incorporates the true grain shapes in its formulation. By using a numerical model, several limitations associated with conventional laboratory testing can be eliminated. An essential part of soil characterization is to achieve several different stress paths from an identical state. However, such experiments have shown to be extremely difficult to conduct in the lab, due to limitations associated with physical sample preparation and the influence of boundary conditions. This could easily be done with LS-DEM simulations. In addition, the simulation results can be used to investigate quantities like fabric and particle rotations, which are difficult to measure in the lab, and have proven to be important for describing the overall granular behavior. This paper presents how LS-DEM simulations can be used to calibrate the input parameters of the advanced constitutive material model, SANISAND. The simulations have been performed on Hostun sand, characterized by angular grains with low sphericity. Special focus has been set on defining the critical state line, which is currently challenging to establish from conventional laboratory testing. SANISAND input parameters have been calibrated using Python and PLAXIS Soil Test. It was observed that all analyses reached a well defined critical state, and a location of the critical state line is proposed. However, the critical void ratio was expected to be more pressure dependent than what was observed for initial pressures p=10-500 kPa. The bounding surface parameter is considered well defined, whereas the results needed to calibrate the phase transformation surface were scattered, resulting in uncertainty related to the slope of this. The kinematic hardening parameters, calibrated using trial and error, yield adequate average fits between LS-DEM analyses and the SANISAND response. However, the analyses run in this thesis are not sufficient for defining them uniquely. Determining these parameters is therefore proposed as an objective for a future study. The calibrated dilatancy parameter is considered relatively certain as its fit to the LS-DEM simulations appears accurate. To investigate the effect of incorporating realistic grain shapes in the DEM formulation, a selection of the simulations were run with spherical grains. The calibrated critical state line for Hostun sand was then compared with the corresponding critical state line for the analyses run with spherical grains. The simulations performed and investigated in this study shows that the sample consisting of true grain shapes reaches a higher critical stress ratio and void ratio than the sample with spherical grains. Several preliminary limitations regarding the LS-DEM analyses in this study have been discussed. Jumps in the average stresses at the boundaries were detected, indicating the occurrence of numerical instabilities in the analyses. These fluctuations can presumably be limited by reducing the applied time step. Moreover, it needs to be highlighted that the computational costs running LS-DEM constitutes a significant drawback of the method. Finally, with calibrated input parameters, SANISAND response and LS-DEM results correspond, also for independently run numerical analyses. However, it is necessary to perform a proper validation to ensure this correspondence. Regardless, despite its limitations, LS-DEM is still considered a promising tool to understand the nature behind characteristic behavior of granular material

Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review

As the adoption of machine learning models increases, ensuring robust models against adversarial attacks is increasingly important. With unsupervised machine learning gaining more attention, ensuring it is robust against attacks is vital. This paper conducts a systematic literature review on the robustness of unsupervised learning, collecting 86 papers. Our results show that most research focuses on privacy attacks, which have effective defenses; however, many attacks lack effective and general defensive measures. Based on the results, we formulate a model on the properties of an attack on unsupervised learning, contributing to future research by providing a model to use.Comment: 38 pages, 11 figure

Hva er de finansielle konsekvensene av å ekskludere selskaper basert på olje og gass fra Statens pensjonsfond utland? - En analyse av hvordan investeringsstrategien til SPU passer inn i Norges portefølje av eiendeler

Master i økonomi og administrasjonI denne oppgaven undersøker vi hvorvidt Norges portefølje er tilstrekkelig diversifisert med hensyn på olje- og gassinvesteringene til Statens Pensjonsfond Utland, og hva som vil være de finansielle konsekvensene av å eventuelt trekke seg ut av slike investeringer. Vi tar utgangspunkt i porteføljeteori og målet Sharpe ratio for å avgjøre hvilken sammensetning av Norges portefølje som gir det beste forholdet mellom risiko og meravkastning. Problemstillingen undersøkes i to ulike modeller. For å oppnå det beste risikoavkastningsforholdet finner vi at SPU bør trekke seg ut av nåværende andel investert i oljeog gassaksjer og samtidig investere mer i en veldiversifisert portefølje av andre aksjer. Resultatene viser at Norge oppnår en forbedret avkastning lik 2,17 og 2,96 milliarder kroner per år, avhengig av hvilken modell vi ser på. At SPU bør trekke seg ut av nåværende andel investert i olje- og gassaksjer er også konsistent når vi tester for underliggende forutsetninger i modellen. Forbedringen i avkastning varierer fra om lag 1 milliard til 2,70 milliarder kroner per år, avhengig av hvilken forutsetning som undersøkes.In this thesis we investigate whether the Norwegian portfolio of assets is sufficiently diversified in terms of the Government Pension Fund Global`s investments in oil and gas stocks, and the financial consequences from withdrawing from such investments. By applying portfolio theory and the measure Sharpe ratio, we attempt to identify the portfolio that provides the best compromise between risk and excess return. The problem is applied in two different models, and the result shows that to achieve the best risk-return relationship, The GPFG should withdraw from its current proportion invested in oil and gas stocks and invest a higher proportion in a well-diversified portfolio of stocks. The improvement in yield from such strategy is equivalent to 2,17 and 2,96 billion NOK per year, dependent on which model we apply. The fact that The GPFG should withdraw from its current proportion invested in oil and gas stocks is consistent when we test for the underlying assumptions in the models. The improved yield range from about 1 to 2.70 billion NOK per year, depending on what assumption we examine

Incidence, recurring admissions and mortality of severe bacterial infections and sepsis over a 22-year period in the population-based HUNT study

Purpose Severe bacterial infections are important causes of hospitalization and loss of health worldwide. In this study we aim to characterize the total burden, recurrence and severity of bacterial infections in the general population during a 22-year period. Methods We investigated hospitalizations due to bacterial infection from eight different foci in the prospective population-based Trøndelag Health Study (the HUNT Study), where all inhabitants aged ≥ 20 in a Norwegian county were invited to participate. Enrollment was between 1995 and 1997, and between 2006 and 2008, and follow-up ended in February 2017. All hospitalizations, positive blood cultures, emigrations and deaths in the follow-up period were captured through registry linkage. Results A total of 79,393 (69.5% and 54.1% of the invited population) people were included, of which 42,237 (53%) were women and mean age was 48.5 years. There were 37,298 hospitalizations due to infection, affecting 15,496 (22% of all included) individuals. The median time of follow-up was 20 years (25th percentile 9.5–75th percentile 20.8). Pneumonia and urinary tract infections were the two dominating foci with incidence rates of 639 and 550 per 100,000 per year, respectively, and with increasing incidence with age. The proportion of recurring admissions ranged from 10.0% (central nervous system) to 30.0% (pneumonia), whilst the proportion with a positive blood culture ranged from 4.7% (skin- and soft tissue infection) to 40.9% (central nervous system). The 30-day mortality varied between 3.2% (skin- and soft tissue infection) and 20.8% (endocarditis). Conclusions In this population-based cohort, we observed a great variation in the incidence, positive blood culture rate, recurrence and mortality between common infectious diseases. These results may help guide policy to reduce the infectious disease burden in the population.publishedVersio

Examining socioeconomic differences in sepsis risk and mediation by modifiable factors: a Mendelian randomization study

publishedVersio

Associations of obesity and lifestyle with the risk and mortality of bloodstream infection in a general population: a 15-year follow-up of 64 027 individuals in the HUNT Study

Background: Bloodstream infections (BSI) cause considerable morbidity and mortality, and primary prevention should be a priority. Lifestyle factors are of particular interest since they represent a modifiable target. Methods: We conducted a prospective cohort study among participants in the population-based Norwegian HUNT2 Survey, where 64 027 participants were followed from 1995–97 through 2011 by linkage to prospectively recorded information on BSI at local and regional hospitals. The exposures were: baseline body mass index (BMI) measurements; and self-reported smoking habits, leisure time physical activity and alcohol intake. The outcomes were hazard ratios (HR) of BSI and BSI mortality. Results: During 810 453 person-years and median follow-up of 14.8 years, 1844 (2.9%) participants experienced at least one BSI and 396 (0.62%) died from BSI. Compared with normal weight participants (BMI 18.5–24.9 kg/m2), the age- and sex-adjusted risk of a first-time BSI was 31% [95% confidence interval (CI) 14–51%] higher at BMI 30.0–34.9 kg/m2, 87% (95% CI 50–135%) higher at BMI 35.0–39.9 kg/m2 and 210% (95% CI 117–341%) higher at BMI ≥ 40.0 kg/m2. The risk of BSI mortality was similarly increased. Compared with never-smokers, current smokers had 51% (95% CI 34–70%) and 75% (95% CI 34–129%) higher risks of BSI and BSI mortality, respectively. Physically inactive participants had 71% (95% CI 42–107%) and 108% (95% CI 37–216%) higher risks of BSI and BSI mortality, respectively, compared with the most physically active.acceptedVersion© The Author 2017; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association. This is the authors' accepted and refereed manuscript to the article. Locked until 15 June 2018 due to copyright restriction