Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields

This work presents a first evaluation of using spatio-temporal receptive fields from a recently proposed time-causal spatio-temporal scale-space framework as primitives for video analysis. We propose a new family of video descriptors based on regional statistics of spatio-temporal receptive field responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain and from object recognition to dynamic texture recognition. The time-recursive formulation enables computationally efficient time-causal recognition. The experimental evaluation demonstrates competitive performance compared to state-of-the-art. Especially, it is shown that binary versions of our dynamic texture descriptors achieve improved performance compared to a large range of similar methods using different primitives either handcrafted or learned from data. Further, our qualitative and quantitative investigation into parameter choices and the use of different sets of receptive fields highlights the robustness and flexibility of our approach. Together, these results support the descriptive power of this family of time-causal spatio-temporal receptive fields, validate our approach for dynamic texture recognition and point towards the possibility of designing a range of video analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure

Inability of spatial transformations of CNN feature maps to support invariant recognition

A large number of deep learning architectures use spatial transformations of CNN feature maps or filters to better deal with variability in object appearance caused by natural image transformations. In this paper, we prove that spatial transformations of CNN feature maps cannot align the feature maps of a transformed image to match those of its original, for general affine transformations, unless the extracted features are themselves invariant. Our proof is based on elementary analysis for both the single- and multi-layer network case. The results imply that methods based on spatial transformations of CNN feature maps or filters cannot replace image alignment of the input and cannot enable invariant recognition for general affine transformations, specifically not for scaling transformations or shear transformations. For rotations and reflections, spatially transforming feature maps or filters can enable invariance but only for networks with learnt or hardcoded rotation- or reflection-invariant featuresComment: 22 pages, 3 figure

Mer ekologisk mat

Authorities, municipalities and county councils have great opportunities to pave the way to a sustainable society. In the public sector, the tax revenue is used and therefore it is important that both employees and societal stakeholders feel that they participate in the sector's decisions and fields of application. Municipalities and county councils are primarily service organisations. The employees' commitment is necessary to be able to reach set goals, to develop procedures and policies that support the overall political goals, in other words, to be successful in the environmental work. There are a lot of areas in the public sector that have a high priority, like educational systems and healthcare. The combination of environmental work and the priority of the core activities are not easy, but both areas must be a part of the work for public sector. The purpose of the thesis is to help the management in an organisation within the public sector to motivate their employees to work toward a jointly environmental goal. The study is a comparative case study, in which two county councils serve as the empirical base. The primary focus is the procurement process, how they manage purchases of ecological food-stuffs. The theories in the study frame the phenomena in terms of organisational change in order to understand how the change is managed, what motives the change is driven by and how communication is used to understand the dilemma. The study has the starting-point in the county council of Jämtland who is trying to achieve the governmental goal of 25 % ecological foodstuffs in public sector by year 2010. The study shows that an economic compensation has an important role when it comes to motivation in the county council of Uppsala. Working towards a joint goal which is perceived as possible to achieve has lead to impressive results and an expressed joy by staff "…it is fun to work with ecological food-stuffs". Positive comments expressed by staff in the county council of Uppsala has got is also working as a positive motivational factor. The general perception is that if the county council of Uppsala would not have used a financial incentive structure that rewarded environmental policies to be put in action, they would not have reached this far in such a short period of time. The economical compensation has served as insurance for the head chef. Since the economical compensation has been significant for the county council of Uppsala to reach 25 % in such a short time period. This experience would be possible to transfer to the county council of Jämtland – the need for a financial reward system that serves as an important part of incentives for promoting a changed behaviour. The communication in the county councils is mainly executed through intranet and internal papers. A forum can be created to stimulate discussion about the county council's goals. It is important to discuss about the goals and to educate the employees. It is also important to work towards a jointly goal and that everybody involved has a clear picture of what is happening within the county council. Recurrent in the theories are the importance of giving feedback. The analysis of the county council of Jämtland shows that their efforts to reach environmental goals is not tied to the goals themselves but rather to the process of conducting business in a responsible manor. Their efforts reflect ambitions to construct an incentive structure in which each and every individual participates actively in their daily activities.Myndigheter, kommuner och landsting har stor möjlighet att agera som samhällsföreträdare och därmed visa vägen för ett hållbart samhälle. I och med att det är skattepengar som används i offentlig sektor är det av stor vikt att både anställda och användare känner sig delaktiga i sektorns beslut och användningsområden. Kommuner och landsting är tjänsteproducerande organisationer. För att få personalen inom dessa organisationer att arbeta mot uppsatta mål och lyckas med ett omfattande miljöarbete behövs personalens engagemang. Den offentliga sektorn har många områden som har hög prioritet såsom sjukvård och skola. Att kombinera ett miljöarbete och samtidigt ha kvar prioriteten på kärnverksamheten är inte helt lätt. Syftet med examensarbetet är att hjälpa ledningen i en organisation inom den offentliga sektorn att motivera sina anställda till att arbeta mot ett gemensamt miljömål. Det görs en jämförelse mellan två landsting för att kunna dra slutsatser av hur de ekologiska inköpen sker i de båda landstingen. Att ändra en upphandlings- och inköpsprocess i en organisation kräver många delar för att lyckas och att få med sig personalen. Genom att använda teorier som försöker täcka in ett lyckat förändringsbeteende tillsammans med motivation och kommunikation läggs den teoretiska grunden för att förstå problematiken. Studien har sin utgångspunkt i Jämtlands läns landsting som arbetar för att nå regeringens mål om 25 % ekologiska livsmedel i den offentliga sektorn till år 2010. Efter litteraturstudier och intervjuer med Jämtlands läns landsting och landstinget i Uppsala län har vi kommit fram till att den ekonomiska ersättningen har spelat en betydande roll när det gäller motivationen i landstinget i Uppsala län. Det tillsammans med att gemensamt jobba mot ett mål som de snart har nått, har gjort att de tycker att det är kul att arbeta med ekologiska livsmedel. De har också blivit peppade av att höra positiva kommentarer om att de ligger bra till. Om landstinget i Uppsala län inte haft den ekonomiska ersättningen hade de inte kommit lika långt på så kort tid. Den har också skapat en säkerhet för kökschefen. I och med att den ekonomiska ersättningen har haft stor betydelse för landstinget i Uppsala län att nå upp till 25 % på så kort tid kan slutsatsen dras att det är av vikt också för Jämtlands läns landsting för att klara måluppfyllelsen till år 2010. Den kommunikation som landstingen har sker till stor del genom intranätet och interna tidningar. Där kan ett forum skapas som diskuterar kring landstingets mål. Att en diskussion förs och någon slags utbildning där personalen informeras är betydelsefullt. Det är viktigt att alla strävar mot samma mål och att de inblandade har en klar bild av vad som sker inom landstinget. Återkommande i teorin är att det är av vikt att ge feedback. Det kan ske med sammanställande statistik över hur landstinget ligger till när det gäller målen med ekologiska livsmedel. Efter analys av empirin ses en tydlig bild av Jämtlands läns landsting som ett landsting som vill nå målet för att det är bra och inte bara för målets skull. Jämtlands läns landsting vill ha ett helhetstänk och att alla ska vara med på en förändring innan beslutet tas. Detta har inte varit av lika stor vikt för landstinget i Uppsala län som ser måluppfyllelsen som viktigare

The SNARE Protein SNAP23 and the SNARE-Interacting Protein Munc18c in Human Skeletal Muscle Are Implicated in Insulin Resistance/Type 2 Diabetes

OBJECTIVE-Our previous studies suggest that the SNARE protein synaptosomal-associated protein of 23 kDa (SNAP23) is involved in the link between increased lipid levels and insulin resistance in cardiomyocytes. The objective was to determine whether SNAP23 may also be involved in the known association between lipid accumulation in skeletal muscle and insulin resistance/type 2 diabetes in humans, as well as to identify a potential regulator of SNAP23. RESEARCH DESIGN AND METHODS-We analyzed skeletal muscle biopsies from patients with type 2 diabetes and healthy, insulin-sensitive control subjects for expression (mRNA and protein) and intracellular localization (subcellular fractionation and immunohistochemistry) of SNAP23, and for expression of proteins known to interact with SNARE proteins. Insulin resistance was determined by a euglycemic hyperinsulinemic clamp Potential mechanisms for regulation of SNAP23 were also investigated in the skeletal muscle cell line L6. RESULTS-We showed increased SNAP23 levels in skeletal muscle from patients with type 2 diabetes compared with that from lean control subjects Moreover, SNAP23 was redistributed from the plasma membrane to the microsomal/cytosolic compartment in the patients with the type 2 diabetes Expression of the SNARE-interacting protein Munc18c was higher in skeletal muscle from patients with type 2 diabetes Studies in L6 cells showed that Munc18c promoted the expression of SNAP23. CONCLUSIONS-We have translated our previous in vitro results into humans by showing that there is a change in the distribution of SNAP23 to the interior of the cell in skeletal muscle from patients with type 2 diabetes. We also showed that Munc18c is a potential regulator of SNAP23. Diabetes 59: 1870-1878, 201

Salivary Biomarkers for Detection of Systemic Diseases

Peer reviewe

Normalisering i en kortikal hypercolumn : Modulerande effekter i ett hårt strukturerat rekurrent spikande neuronnätverk

Normalization is important for a large range of phenomena in biological neural systems such as light adaptation in the retina, context dependent decision making and probabilistic inference. In a normalizing circuit the activity of one neuron/-group of neurons is divisively rescaled in relation to the activity of other neurons/­­groups. This creates neural responses invariant to certain stimulus dimensions and dynamically adapts the range over which a neural system can respond discriminatively on stimuli. This thesis examines whether a biologically realistic normalizing circuit can be implemented by a spiking neural network model based on the columnar structure found in cortex. This was done by constructing and evaluating a highly structured spiking neural network model, modelling layer 2/3 of a cortical hypercolumn using a group of neurons as the basic computational unit. The results show that the structure of this hypercolumn module does not per se create a normalizing network. For most model versions the modulatory effect is better described as subtractive inhibition. However three mechanisms that shift the modulatory effect towards normalization were found: An increase in membrane variance for increased modulatory inputs; variability in neuron excitability and connections; and short-term depression on the driving synapses. Moreover it is shown that by combining those mechanisms it is possible to create a spiking neural network that implements approximate normalization over at least ten times increase in input magnitude. These results point towards possible normalizing mechanisms in a cortical hypercolumn; however more studies are needed to assess whether any of those could in fact be a viable explanation for normalization in the biological nervous system.Normalisering är viktigt för en lång rad fenomen i biologiska nervsystem såsom näthinnans ljusanpassning, kontextberoende beslutsfattande och probabilistisk inferens. I en normaliserande krets skalas aktiviteten hos en nervcell/grupp av nervceller om i relation till aktiviteten hos andra nervceller/grupper. Detta ger neurala svar som är invarianta i förhållande till vissa dimensioner hos stimuli, och anpassar dynamiskt för vilka inputmagnituder ett system kan särskilja mellan stimuli. Den här uppsatsen undersöker huruvida en biologiskt realistisk normal­iserande krets kan implementeras av ett spikande neuronnätverk konstruerat med utgångspunkt från kolumnstrukturen i kortex. Detta gjordes genom att konstruera och utvärdera ett hårt strukturerat rekurrent spikande neuronnätverk, som modellerar lager 2/3 av en kortikal hyperkolumn med en grupp av neuroner som grundläggande beräkningsenhet. Resultaten visar att strukturen i hyperkolumn­modulen inte i sig skapar ett normaliserande nätverk. För de flesta nätverks­versioner implementerar nätverket en modulerande effekt som bättre beskrivs som subtraktiv inhibition. Dock hittades tre mekanismer som skapar ett mer normaliserande nätverk: Ökad membranvarians för större modulerande inputs; variabilitet i excitabilitet och inkommande kopplingar; och korttidsdepression på drivande synapser. Det visas också att genom att kombinera dessa mekanismer är det möjligt att skapa ett spikande neuronnät som approximerar normalisering över ett en åtminstone tio gångers ökning av storleken på input. Detta pekar på möjliga normaliserande mekanismer i en kortikal hyperkolumn, men ytterligare studier är nödvändiga för att avgöra om en eller flera av dessa kan vara en förklaring till hur normalisering är implementerat i biologiska nervsystem

Normalisering i en kortikal hypercolumn : Modulerande effekter i ett hårt strukturerat rekurrent spikande neuronnätverk

Normalization is important for a large range of phenomena in biological neural systems such as light adaptation in the retina, context dependent decision making and probabilistic inference. In a normalizing circuit the activity of one neuron/-group of neurons is divisively rescaled in relation to the activity of other neurons/­­groups. This creates neural responses invariant to certain stimulus dimensions and dynamically adapts the range over which a neural system can respond discriminatively on stimuli. This thesis examines whether a biologically realistic normalizing circuit can be implemented by a spiking neural network model based on the columnar structure found in cortex. This was done by constructing and evaluating a highly structured spiking neural network model, modelling layer 2/3 of a cortical hypercolumn using a group of neurons as the basic computational unit. The results show that the structure of this hypercolumn module does not per se create a normalizing network. For most model versions the modulatory effect is better described as subtractive inhibition. However three mechanisms that shift the modulatory effect towards normalization were found: An increase in membrane variance for increased modulatory inputs; variability in neuron excitability and connections; and short-term depression on the driving synapses. Moreover it is shown that by combining those mechanisms it is possible to create a spiking neural network that implements approximate normalization over at least ten times increase in input magnitude. These results point towards possible normalizing mechanisms in a cortical hypercolumn; however more studies are needed to assess whether any of those could in fact be a viable explanation for normalization in the biological nervous system.Normalisering är viktigt för en lång rad fenomen i biologiska nervsystem såsom näthinnans ljusanpassning, kontextberoende beslutsfattande och probabilistisk inferens. I en normaliserande krets skalas aktiviteten hos en nervcell/grupp av nervceller om i relation till aktiviteten hos andra nervceller/grupper. Detta ger neurala svar som är invarianta i förhållande till vissa dimensioner hos stimuli, och anpassar dynamiskt för vilka inputmagnituder ett system kan särskilja mellan stimuli. Den här uppsatsen undersöker huruvida en biologiskt realistisk normal­iserande krets kan implementeras av ett spikande neuronnätverk konstruerat med utgångspunkt från kolumnstrukturen i kortex. Detta gjordes genom att konstruera och utvärdera ett hårt strukturerat rekurrent spikande neuronnätverk, som modellerar lager 2/3 av en kortikal hyperkolumn med en grupp av neuroner som grundläggande beräkningsenhet. Resultaten visar att strukturen i hyperkolumn­modulen inte i sig skapar ett normaliserande nätverk. För de flesta nätverks­versioner implementerar nätverket en modulerande effekt som bättre beskrivs som subtraktiv inhibition. Dock hittades tre mekanismer som skapar ett mer normaliserande nätverk: Ökad membranvarians för större modulerande inputs; variabilitet i excitabilitet och inkommande kopplingar; och korttidsdepression på drivande synapser. Det visas också att genom att kombinera dessa mekanismer är det möjligt att skapa ett spikande neuronnät som approximerar normalisering över ett en åtminstone tio gångers ökning av storleken på input. Detta pekar på möjliga normaliserande mekanismer i en kortikal hyperkolumn, men ytterligare studier är nödvändiga för att avgöra om en eller flera av dessa kan vara en förklaring till hur normalisering är implementerat i biologiska nervsystem

C. H. Dodd. Le fondateur du christianisme

Jaubert A. C. H. Dodd. Le fondateur du christianisme. In: Revue de l'histoire des religions, tome 183, n°2, 1973. p. 206

Scale-invariant scale-channel networks : Deep networks that generalise to previously unseen scales

The ability to handle large scale variations is crucial for many real world visual tasks. A straightforward approach for handling scale in a deep network is toprocess an image at several scales simultaneously in a set of scale channels. Scale invariance can then, in principle, be achieved by using weight sharing between the scale channels together with max or average pooling over the outputs from the scale channels. The ability of such scale-channel networks to generalise to scales not present in the training set over significant scale ranges has, however, not previously been explored.  In this paper, we present a systematic study of this methodology by implementing different types of scale-channel networks and evaluating their ability to generalise to previously unseen scales. We develop a formalism for analysing the covariance and invariance properties of scale-channel networks, including exploring their relations to scale-space theory, and exploring how different design choices, unique to scaling transformations, affect the overall performance of scale-channel networks. We first show that two previously proposed scale-channel network designs, in one case, generalise no better than a standard CNN to scales not present in the training set, and in the second case, have limited scale generalisation ability. We explain theoretically and demonstrate experimentally why generalisation fails or is limited in these cases. We then propose a new type of foveated scale-channel architecture, where the scale channels process increasingly larger parts of the image with decreasing resolution. This new type of scale-channel network is shown to generalise extremely well, provided sufficient image resolution and the absence of boundary effects. Our proposed FovMax and FovAvg networks perform almost identically over a scale range of 8, also when training on single-scale training data, and do also give improved performance  when learning from datasets with large scale variations in the small sample regime.QC 20220530Scale-space theory for covariant and invariant visual perceptio

Exploring the ability of CNNs to generalise to previously unseen scales over wide scale ranges

The ability to handle large scale variations is crucial for many real world visual tasks. A straightforward approach for handling scale in a deep network is to process an image at several scales simultaneously in a set of scale channels. Scale invariance can then, in principle, be achieved by using weight sharing between the scale channels together with max or average pooling over the outputs from the scale channels. The ability of such scale channel networks to generalise to scales not present in the training set over significant scale ranges has, however, not previously been explored. We, therefore, present a theoretical analysis of invariance and covariance properties of scale channel networks and perform an experimental evaluation of the ability of different types of scale channel networks to generalise to previously unseen scales. We identify limitations of previous approaches and propose a new type of foveated scale channel architecture, where the scale channels process increasingly larger parts of the image with decreasing resolution. Our proposed FovMax and FovAvg networks perform almost identically over a scale range of 8 also when training on single scale training data and give improvements in the small sample regime.Not duplicate with 1515273, QC 20220517</p