BCR repertoire clustering approaches. حسب Katharina Waury (14203284)

الموضوعات:

Limitations of clustering approaches. حسب Katharina Waury (14203284)

الموضوعات:

Aggregated substitution likelihoods. حسب Katharina Waury (14203284)

الموضوعات:

DataSheet1_Information guided adaptation of complex biological systems.pdf حسب R. Craig Herndon (16755825)

Integrative bioacoustics discrimination of eight delphinid species in the western South Atlantic Ocean حسب Thiago Orion Simões Amorim (6808244)

"…The correct classification was enhanced by the joint step, given the 5.8% error in the discriminant function analysis and a misclassification rate of 18.8% in the tree model. …"

Phenotypes of the parents with large effect variants in galactose. حسب Andreas Tsouris (10726951)

Left: Quality profile of LAFM images for 1) periplasmic (top) and 2) cytoplasmic (bottom) SecYEG. حسب Creighton M. Lisowski (22756444)

Distributed zero-gradient-sum optimisation algorithm with an edge-based adaptive event-triggered mechanism حسب Jiayun Liu (12651565)

"…<p>This paper addresses the continuous-time distributed optimisation problem over networks, where the global objective function is formed by a sum of convex local objective functions. …"

Image_4_Modeling the development of cortical responses in primate dorsal (“where”) pathway to optic flow using hierarchical neural field models.TIF حسب Anila Gundavarapu (6324917)

"…On the other hand, model-3 consists of the Velocity Selective Mosaic Network (VSMN) followed by a convolutional neural network (CNN) which is trained on radial and rotational sequences using a supervised backpropagation algorithm. The quantitative comparison of response similarity matrices (RSMs), made out of convolution layer and last hidden layer responses, show that model-3 neuron responses are consistent with the idea of functional hierarchy in the macaque motion pathway. …"

Image_3_Modeling the development of cortical responses in primate dorsal (“where”) pathway to optic flow using hierarchical neural field models.TIF حسب Anila Gundavarapu (6324917)

"…On the other hand, model-3 consists of the Velocity Selective Mosaic Network (VSMN) followed by a convolutional neural network (CNN) which is trained on radial and rotational sequences using a supervised backpropagation algorithm. The quantitative comparison of response similarity matrices (RSMs), made out of convolution layer and last hidden layer responses, show that model-3 neuron responses are consistent with the idea of functional hierarchy in the macaque motion pathway. …"

Image_1_Modeling the development of cortical responses in primate dorsal (“where”) pathway to optic flow using hierarchical neural field models.TIF حسب Anila Gundavarapu (6324917)

"…On the other hand, model-3 consists of the Velocity Selective Mosaic Network (VSMN) followed by a convolutional neural network (CNN) which is trained on radial and rotational sequences using a supervised backpropagation algorithm. The quantitative comparison of response similarity matrices (RSMs), made out of convolution layer and last hidden layer responses, show that model-3 neuron responses are consistent with the idea of functional hierarchy in the macaque motion pathway. …"

Image_2_Modeling the development of cortical responses in primate dorsal (“where”) pathway to optic flow using hierarchical neural field models.TIF حسب Anila Gundavarapu (6324917)

"…On the other hand, model-3 consists of the Velocity Selective Mosaic Network (VSMN) followed by a convolutional neural network (CNN) which is trained on radial and rotational sequences using a supervised backpropagation algorithm. The quantitative comparison of response similarity matrices (RSMs), made out of convolution layer and last hidden layer responses, show that model-3 neuron responses are consistent with the idea of functional hierarchy in the macaque motion pathway. …"

Table 1 - Fusing multisensory signals across channels and time حسب Swathi Anil (17382903)

Fig 2 - Fusing multisensory signals across channels and time حسب Swathi Anil (17382903)

Multi-Objective Cooperative Paths Planning for Multiple Parafoils System Using a Genetic Algorithm حسب Chen Qi (2803873)

"…Parafoils’ paths are encoded by real matrix, and the cooperative relationship between parafoils is realized by paths fitness function. The random single point crossover and Gaussian mutation are introduced to accelerate algorithm convergence rate. …"

Definition for symbols used in node matching. حسب Shuwen Wang (457921)

"…The distributed parameter learning and consolidation repeat in an iterative fashion until the algorithm converges or terminates. Many FL methods exist to aggregate weights from distributed sites, but most approaches use a <i>static node alignment</i> approach, where nodes of distributed networks are statically assigned, in advance, to match nodes and aggregate their weights. …"

Node matching result. حسب Shuwen Wang (457921)

"…The distributed parameter learning and consolidation repeat in an iterative fashion until the algorithm converges or terminates. Many FL methods exist to aggregate weights from distributed sites, but most approaches use a <i>static node alignment</i> approach, where nodes of distributed networks are statically assigned, in advance, to match nodes and aggregate their weights. …"

Digitally Assisted Single-Particle Tracking for Accurate Analysis of Complicated Cargo Transport Dynamics in Microtubule Networks حسب Xiaodong Cheng (8947)

"…Intracellular transport is a fundamental process crucial for cellular function, driven by the coordinated action of motor proteins that move cargo along microtubule tracks. …"

Digitally Assisted Single-Particle Tracking for Accurate Analysis of Complicated Cargo Transport Dynamics in Microtubule Networks حسب Xiaodong Cheng (8947)

"…Intracellular transport is a fundamental process crucial for cellular function, driven by the coordinated action of motor proteins that move cargo along microtubule tracks. …"

Results for the Pattern Generation task. حسب Paolo Muratore (8331585)

"…The simulation used a network of <i>N</i> = 500 neurons and a total simulation time of <i>T</i> = 1000 time steps. <b>D)</b> Comparison of different learning algorithms on the Pattern Generation Task. …"