7,438 research outputs found
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Examples of works to practice staccato technique in clarinet instrument
Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato
geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı
sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de
durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt
çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham
verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her
aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır.
Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine
yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini
içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin
kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür
taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de
kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt
çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve
güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının
girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken
doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir
kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına
bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği
vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan
çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur.
Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir.
Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır.
Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların
yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve
sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır
Image classification over unknown and anomalous domains
A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting.
Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each.
While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so.
In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks
Annals [...].
Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo Simão Diniz Dalmolin
Robustness against adversarial attacks on deep neural networks
While deep neural networks have been successfully applied in several different domains, they exhibit vulnerabilities to artificially-crafted perturbations in data. Moreover, these perturbations have been shown to be transferable across different networks where the same perturbations can be transferred between different models. In response to this problem, many robust learning approaches have emerged. Adversarial training is regarded as a mainstream approach to enhance the robustness of deep neural networks with respect to norm-constrained perturbations. However, adversarial training requires a large number of perturbed examples (e.g., over 100,000 examples are required for MNIST dataset) trained on the deep neural networks before robustness can be considerably enhanced. This is problematic due to the large computational cost of obtaining attacks. Developing computationally effective approaches while retaining robustness against norm-constrained perturbations remains a challenge in the literature.
In this research we present two novel robust training algorithms based on Monte-Carlo Tree Search (MCTS) [1] to enhance robustness under norm-constrained perturbations [2, 3]. The first algorithm searches potential candidates with Scale Invariant Feature Transform method and makes decisions with Monte-Carlo Tree Search method [2]. The second algorithm adopts Decision Tree Search method (DTS) to accelerate the search process while maintaining efficiency [3]. Our overarching objective is to provide computationally effective approaches that can be deployed to train deep neural networks robust against perturbations in data. We illustrate the robustness with these algorithms by studying the resistances to adversarial examples obtained in the context of the MNIST and CIFAR10 datasets. For MNIST, the results showed an average training efforts saving of 21.1\% when compared to Projected Gradient Descent (PGD) and 28.3\% when compared to Fast Gradient Sign Methods (FGSM). For CIFAR10, we obtained an average improvement of efficiency of 9.8\% compared to PGD and 13.8\% compared to FGSM. The results suggest that these two methods here introduced are not only robust to norm-constrained perturbations but also efficient during training.
In regards to transferability of defences, our experiments [4] reveal that across different network architectures, across a variety of attack methods from white-box to black-box and across various datasets including MNIST and CIFAR10, our algorithms outperform other state-of-the-art methods, e.g., PGD and FGSM. Furthermore, the derived attacks and robust models obtained on our framework are reusable in the sense that the same norm-constrained perturbations can facilitate robust training across different networks. Lastly, we investigate the robustness of intra-technique and cross-technique transferability and the relations with different impact factors from adversarial strength to network capacity. The results suggest that known attacks on the resulting models are less transferable than those models trained by other state-of-the-art attack algorithms.
Our results suggest that exploiting these tree search frameworks can result in significant improvements in the robustness of deep neural networks while saving computational cost on robust training. This paves the way for several future directions, both algorithmic and theoretical, as well as numerous applications to establish the robustness of deep neural networks with increasing trust and safety.Open Acces
Exploring the effects of spinal cord stimulation for freezing of gait in parkinsonian patients
Dopaminergic replacement therapies (e.g. levodopa) provide limited to no response for axial motor symptoms including gait dysfunction and freezing of gait (FOG) in Parkinson’s disease (PD) and Richardson’s syndrome progressive supranuclear palsy (PSP-RS) patients. Dopaminergic-resistant FOG may be a sensorimotor processing issue that does not involve basal ganglia (nigrostriatal) impairment. Recent studies suggest that spinal cord stimulation (SCS) has positive yet variable effects for dopaminergic-resistant gait and FOG in parkinsonian patients. Further studies investigating the mechanism of SCS, optimal stimulation parameters, and longevity of effects for alleviating FOG are warranted. The hypothesis of the research described in this thesis is that mid-thoracic, dorsal SCS effectively reduces FOG by modulating the sensory processing system in gait and may have a dopaminergic effect in individuals with FOG. The primary objective was to understand the relationship between FOG reduction, improvements in upper limb visual-motor performance, modulation of cortical activity and striatal dopaminergic innervation in 7 PD participants. FOG reduction was associated with changes in upper limb reaction time, speed and accuracy measured using robotic target reaching choice tasks. Modulation of resting-state, sensorimotor cortical activity, recorded using electroencephalography, was significantly associated with FOG reduction while participants were OFF-levodopa. Thus, SCS may alleviate FOG by modulating cortical activity associated with motor planning and sensory perception. Changes to striatal dopaminergic innervation, measured using a dopamine transporter marker, were associated with visual-motor performance improvements. Axial and appendicular motor features may be mediated by non-dopaminergic and dopaminergic pathways, respectively. The secondary objective was to demonstrate the short- and long-term effects of SCS for alleviating dopaminergic-resistant FOG and gait dysfunction in 5 PD and 3 PSP-RS participants without back/leg pain. SCS programming was individualized based on which setting best improved gait and/or FOG responses per participant using objective gait analysis. Significant improvements in stride velocity, step length and reduced FOG frequency were observed in all PD participants with up to 3-years of SCS. Similar gait and FOG improvements were observed in all PSP-RS participants up to 6-months. SCS is a promising therapeutic option for parkinsonian patients with FOG by possibly influencing cortical and subcortical structures involved in locomotion physiology
AIUCD 2022 - Proceedings
L’undicesima edizione del Convegno Nazionale dell’AIUCD-Associazione di Informatica Umanistica ha per titolo Culture digitali. Intersezioni: filosofia, arti, media. Nel titolo è presente, in maniera esplicita, la richiesta di una riflessione, metodologica e teorica, sull’interrelazione tra tecnologie digitali, scienze dell’informazione, discipline filosofiche, mondo delle arti e cultural studies
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Novel delivery and sample mixing for synchrotron diffraction experiments using acoustic levitation with multi-transducer arrays
Acoustic levitation may utilise standing waves at ultrasonic frequencies to manipulate suspended substances and small objects in a contactless manner. These materials may be levitated in the positions in which the nodes are located, corresponding to positions of low acoustic pressure. In recent years, off the shelf transducer based acoustic levitators have been used for contactless manipulation of liquids. These systems benefit from requiring low power and low-cost components making acoustic levitation more accessible to the masses. Such a system was investigated in this work for presenting protein crystals, within their mother liquor, to the I24 beamline at Diamond Light Source for x-ray diffraction experiments. It was found that the crystals tended to sediment toward the bottom of the droplets, which were oblate in shape. The droplets which were levitated often became unstable and fell from their suspended position, or they would not detach from the pipette tip when they were being injected. To rectify this, a coating of silicone oil was added allowing the droplets to remain stable as well as limit the evaporation of the droplet whilst it was manually inserted and the area cleared of personnel before the x-ray beam was engaged. This silicone oil coating is non-crystalline and thus did not invalidate the results collected which showed the lysozyme crystal structure with a resolution of 1.69 A, confirming acoustic levitation as a good sample presentation method for these types of experiments. To remove the requirement for the silicone oil, a bespoke system was created named the DLS-Lev that allowed top-loading of the sample. The droplets of mother liquor which contained protein crystals were easily detached from the pipette tip into the traps within the DLS-Lev system owing to the increased strength of the traps in the modified design. This system, paired with an automated pipette, facilitated sample mixing experiments whilst the x-ray beam was engaged. The further development of the pipetting system was halted due to the COVID-19 pandemic. However, future work should see the permanent installation of these systems at the I24 beamline at Diamond Light Source, as well as additional bespoke acoustic levitators designed for the other beamlines specialising in the research of protein structure via x-ray scattering techniques
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