Bioinformatics for comparative cell biology

For hundreds of years biologists have studied the naturally occurring diversity in plant and animal species. The invention of the electron microscope in the rst half of the 1900's reveled that cells also can be incredible complex (and often stunningly beautiful). However, despite the fact that the eld of cell biology has existed for over 100 years we still lack a formal understanding of how cells evolve: It is unclear what the extents are in cell and organelle morphology, if and how diversity might be constrained, and how organelles change morphologically over time.(...

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Visual object discovery and understanding

Learning to recognize objects is a fundamental and essential step in human perception and understanding of the world. Accordingly, research of object discovery across diverse modalities plays a pivotal role in the context of computer vision. This field not only contributes significantly to enhancing our understanding of visual information but also offers a plethora of potential applications, like augmented reality, e-commerce, and robotics, particularly in industrial manipulation scenarios. We first address the task of discovering objects from still images regardless of any predefined categories. We introduce a novel variational relaxation approach tailored to the task. By framing it as an optimization problem for piecewise-constant segmentation, this technique enables direct training of a fully convolutional network (FCN) for predicting object labels on each pixel. Applying our approach to the instance segmentation task achieved results almost as good as mask R-CNN without depending on a two-stage framework. Note that the training of the network does not depend on the category label, enabling our approach to discover objects unbounded by predefined categories. Next, we extend our exploration to video sequences, focusing on the task of unsupervised video object segmentation. Here, we aim to discover and track objects within videos. Noticing that single-frame object proposals often fail to obtain a good proposal due to motion blur, occlusion, and other reasons, our approach involves refining key frame proposals using a Multi-proposal graph constructed from proposals initially generated in nearby frames and then propagated to the key frame. We then compute the maximal cliques within this graph, which contains proposals that represent the same object. Pixel-level voting is performed within each clique to generate the key frame proposals that could be better than any of the single-frame proposals. Then a semi-supervised VOS algorithm subsequently tracks these key frame proposals across the entire video, showcasing the potential for precise and robust object tracking in dynamic visual environments. We further explore into the domain of Vision-Language, where we seek to identify objects associated with a specific textual context. In this multifaceted context, we tackle the intricate challenge of content moderation (CM), which assesses multimodal user-generated content to detect material that is illegal, harmful, or insulting. We present a novel CM model to address the asymmetric in semantics between vision and language. Our model features an innovative asymmetric fusion architecture that not only fuses the common knowledge in both modalities but also leverages the unique information present in each modality. Additionally, we introduce a novel cross-modality contrastive loss to capture knowledge that arises exclusively in multimodal context, which is crucial for addressing harmful intent that may emerge at the intersection of these modalities