Stiffer optical tweezers through real-time feedback control

Using real-time re-programmable signal processing we connect acousto-optic steering and back-focal-plane interferometric position detection in optical tweezers to create a fast feedback controlled instrument. When trapping 3 µm latex beads in water we find that proportional-gain position-clamping increases the effective lateral trap stiffness ~13-fold. A theoretical power spectrum for bead fluctuations during position-clamped trapping is derived and agrees with the experimental data. The loop delay, ~19 µs in our experiment, limits the maximum achievable effective trap stiffness

The Conditional Lucas & Kanade Algorithm

The Lucas & Kanade (LK) algorithm is the method of choice for efficient dense image and object alignment. The approach is efficient as it attempts to model the connection between appearance and geometric displacement through a linear relationship that assumes independence across pixel coordinates. A drawback of the approach, however, is its generative nature. Specifically, its performance is tightly coupled with how well the linear model can synthesize appearance from geometric displacement, even though the alignment task itself is associated with the inverse problem. In this paper, we present a new approach, referred to as the Conditional LK algorithm, which: (i) directly learns linear models that predict geometric displacement as a function of appearance, and (ii) employs a novel strategy for ensuring that the generative pixel independence assumption can still be taken advantage of. We demonstrate that our approach exhibits superior performance to classical generative forms of the LK algorithm. Furthermore, we demonstrate its comparable performance to state-of-the-art methods such as the Supervised Descent Method with substantially less training examples, as well as the unique ability to "swap" geometric warp functions without having to retrain from scratch. Finally, from a theoretical perspective, our approach hints at possible redundancies that exist in current state-of-the-art methods for alignment that could be leveraged in vision systems of the future.Comment: 17 pages, 11 figure

A comparison of small mammal communities in two High-Andean Polylepis woodlands in Ecuador

Polylepis forest, historically widespread throughout high elevations of the central and northern Andes, now remain only in discontinuous small patches.  An expanding agricultural frontier, along with other anthropogenic pressures, imperils these remnants through further isolation and loss of habitat quality. Using two grids of live traps we compared the populations of small nonvolant mammals in an intact Polylepis woodland with one nearby that had been logged 50 years before. Our study is the first to examine the effects of habitat degradation and associated changes to vertical complexity and habitat heterogeneity on mammalian communities in Polylepis woodlands above 3500 m. The intact woodland had significantly more vertical complexity than the mid-successional woodland.  A total of 315 captures of 147 individuals of 9 species were sampled during an intensive trapping effort in 2010.  Trap success was especially high averaging 35.4 % and 28.1 % in the intact and mid-successional woodland, respectively.  Diversity and abundance of small mammals were greater in the intact woodland than the mid-successional site.  Forest specialist species were more abundant in the intact habitat; while Thomasomys paramorum, a habitat generalist, was dominant in both.  Habitat quality affected movement patterns of T. paramorum.  The results affirm a high diversity and density of small mammals in intact Polylepis woodland and indicate that the effects of habitat disturbance are species dependent.  We suggest that habitat specialists are more susceptible to loss of habitat heterogeneity and vertical complexity than habitat generalists.&nbsp

FACTORS INFLUENCING BEST ANNUAL RACING TIME IN FINNISH HORSES

The fixed effects of year of race, season of race, sex, method of start, annual number of starts, length of race and racetrack were evaluated on best annual racing time in Finnish Horses. Data included 1,378 records for 554 horses by 206 sires. Five models were assumed within the age groups from 3 to 6 yr. The annual number of starts, method of start and season of race effects were interrelated. An increase in number of starts was associated with considerable improvement in a horse\u27s best annual racing time. Records should not, however, be adjusted for effect of annual number of starts because it would simultaneously account for part of genetic differences among horses. The largest estimates of heritability were obtained for best annual racing time when the model included the fixed year-season and sex effects. Corresponding to this model, the estimate of repeatability for best annual racing time over the four age groups was .60 ± .03. An example of best linear unbiased predictions of sires\u27 breeding values based on progeny records in one or several ages is presented

Expanding the Family of Grassmannian Kernels: An Embedding Perspective

Modeling videos and image-sets as linear subspaces has proven beneficial for many visual recognition tasks. However, it also incurs challenges arising from the fact that linear subspaces do not obey Euclidean geometry, but lie on a special type of Riemannian manifolds known as Grassmannian. To leverage the techniques developed for Euclidean spaces (e.g, support vector machines) with subspaces, several recent studies have proposed to embed the Grassmannian into a Hilbert space by making use of a positive definite kernel. Unfortunately, only two Grassmannian kernels are known, none of which -as we will show- is universal, which limits their ability to approximate a target function arbitrarily well. Here, we introduce several positive definite Grassmannian kernels, including universal ones, and demonstrate their superiority over previously-known kernels in various tasks, such as classification, clustering, sparse coding and hashing

LBP and irregular graph pyramids

In this paper, a new codification of Local Binary Patterns (LBP) is given using graph pyramids. The LBP code characterizes the topological category (local max, min, slope, saddle) of the gray level landscape around the center region. Given a 2D grayscale image I, our goal is to obtain a simplified image which can be seen as “minimal” representation in terms of topological characterization of I. For this, a method is developed based on merging regions and Minimum Contrast Algorithm

The influence of Ni-rich nanoclusters on the anisotropic magnetic properties of CdSb doped with Ni

The magnetic properties of oriented CdSb single crystals doped with 2 at% of Ni are investigatedyesBelgorod State Universit

Musiikin mahdollisuudet lapsen motorisessa kehityksessä

Tiivistelmä. Kandidaatin tutkielmassani vastaan tutkimuskysymykseeni: Miten musiikkikasvatus tukee lapsen motorista kehitystä? Tutkimusaihe on lähtöisin kiinnostuksestani motoriikkaa ja neurologiaa kohtaan, ja olen usein pohtinut, mitä ihmisen kehossa tapahtuu musisoinnin aikana ja kuinka monipuolisesti musiikki voi vaikuttaa ihmiseen. Musiikin positiivisia vaikutuksia on tutkittu laajasti ja tässä tutkimuksessa syvennyn näistä hyödyistä musiikin mahdollisuuksiin lapsen motorisessa kehityksessä. Tässä systemaattisena kirjallisuuskatsauksena suoritetussa tutkimuksessa avaan motoriikkaa psykologisesta ja neurologisesta näkökulmasta tutkimalla liikkeeseen liittyviä aivoalueita, ja lapsen motorisia kehitysvaiheita ja motorisia taitoja. Käsittelen motoriikan ilmenemistä musiikkipedagogiikassa käymällä läpi perusopetuksen opetussuunnitelman perusteita luokilla 1–6 (taide- ja taitoaineet, erityinen tuki), Dalcroze-pedagogiikkaa ja Orff-pedagogiikkaa. Tarkastelen motoriikkaa myös puhtaasti instrumenttiopintojen ja ammattimuusikkojen näkökulmasta, jotta tutkimuksessa tulee esille myös se, kuinka pitkälle motoriset taidot voivat kehittyä pitkäaikaisen musisoinnin ansiosta ja miten tämä vaikuttaa muihin kognitiivisiin kykyihin. Tutkimukseni perusteella musisointi, musiikin kehollinen ilmaisu ja monipuolinen liikkuminen tilassa vahvistaa ja kehittää motorisia taitoja lapsen kriittisten sekä optimaalisten kehitysjaksojen aikana. Koulussa musiikki tukee lapsen motorista kehitystä pyrkimällä rakentamaan luovaan liikkumiseen kannustavan oppimisympäristön. Tästä huolimatta termi motoriikka paistaa poissaolollaan vuoden 2014 perusopetuksen opetussuunnitelman perusteissa musiikin oppiaineen kohdalla. Motoriikan sijasta käytetään laajempia termejä, liike ja kehollisuus. Hienomotorisia taitoja puolestaan tukee säännöllinen instrumentilla harjoittelu, jonka aikana muut kognitiiviset alueet, kuten kuulo, näkö ja muisti, kehittyvät tekemällä motoriikan kanssa välitöntä yhteistyötä. Tulevana musiikkikasvattajana motoriset taidot, niiden kehittyminen ja ylläpitäminen musiikin parissa ovat oleellisia tuntea. Tämän kandidaatin tutkielman lopussa mietinkin, miten tulevassa ammatissani voisin mahdollistaa musiikin oppilaiden monipuolisen motorisen kehityksen

Asymmetric representation of aversive prediction errors in Pavlovian threat conditioning

Learning to predict threat is important for survival. Such learning may be driven by differences between expected and encountered outcomes, termed prediction errors (PEs). While PEs are crucial for reward learning, the role of putative PE signals in aversive learning is less clear. Here, we used functional magnetic resonance imaging in humans to investigate neural PE signals. Four cues, each with a different probability of being followed by an aversive outcome, were presented multiple times. We found that neural activity only at omission - but not at occurrence - of predicted threat related to PEs in the medial prefrontal cortex. More expected omission was associated with higher neural activity. In no brain region did neural activity fulfill necessary computational criteria for full signed PE representation. Our result suggests that, different from reward learning, aversive learning may not be primarily driven by PE signals in one single brain region

Multi-resolution texture classification based on local image orientation

The aim of this paper is to evaluate quantitatively the discriminative power of the image orientation in the texture classification process. In this regard, we have evaluated the performance of two texture classification schemes where the image orientation is extracted using the partial derivatives of the Gaussian function. Since the texture descriptors are dependent on the observation scale, in this study the main emphasis is placed on the implementation of multi-resolution texture analysis schemes. The experimental results were obtained when the analysed texture descriptors were applied to standard texture databases