PolyMaX: General Dense Prediction with Mask Transformer

Dense prediction tasks, such as semantic segmentation, depth estimation, and surface normal prediction, can be easily formulated as per-pixel classification (discrete outputs) or regression (continuous outputs). This per-pixel prediction paradigm has remained popular due to the prevalence of fully convolutional networks. However, on the recent frontier of segmentation task, the community has been witnessing a shift of paradigm from per-pixel prediction to cluster-prediction with the emergence of transformer architectures, particularly the mask transformers, which directly predicts a label for a mask instead of a pixel. Despite this shift, methods based on the per-pixel prediction paradigm still dominate the benchmarks on the other dense prediction tasks that require continuous outputs, such as depth estimation and surface normal prediction. Motivated by the success of DORN and AdaBins in depth estimation, achieved by discretizing the continuous output space, we propose to generalize the cluster-prediction based method to general dense prediction tasks. This allows us to unify dense prediction tasks with the mask transformer framework. Remarkably, the resulting model PolyMaX demonstrates state-of-the-art performance on three benchmarks of NYUD-v2 dataset. We hope our simple yet effective design can inspire more research on exploiting mask transformers for more dense prediction tasks. Code and model will be made available.Comment: WACV 202

Functional Polymers in Protein Detection Platforms: Optical, Electrochemical, Electrical, Mass-Sensitive, and Magnetic Biosensors

The rapidly growing field of proteomics and related applied sectors in the life sciences demands convenient methodologies for detecting and measuring the levels of specific proteins as well as for screening and analyzing for interacting protein systems. Materials utilized for such protein detection and measurement platforms should meet particular specifications which include ease-of-mass manufacture, biological stability, chemical functionality, cost effectiveness, and portability. Polymers can satisfy many of these requirements and are often considered as choice materials in various biological detection platforms. Therefore, tremendous research efforts have been made for developing new polymers both in macroscopic and nanoscopic length scales as well as applying existing polymeric materials for protein measurements. In this review article, both conventional and alternative techniques for protein detection are overviewed while focusing on the use of various polymeric materials in different protein sensing technologies. Among many available detection mechanisms, most common approaches such as optical, electrochemical, electrical, mass-sensitive, and magnetic methods are comprehensively discussed in this article. Desired properties of polymers exploited for each type of protein detection approach are summarized. Current challenges associated with the application of polymeric materials are examined in each protein detection category. Difficulties facing both quantitative and qualitative protein measurements are also identified. The latest efforts on the development and evaluation of nanoscale polymeric systems for improved protein detection are also discussed from the standpoint of quantitative and qualitative measurements. Finally, future research directions towards further advancements in the field are considered

CURATION AND MANAGEMENT OF CULTURAL HERITAGE THROUGH LIBRARIES

Libraries, museums and archives hold valuable collections in a variety of media, presenting a vast body of knowledge rooted in the history of human civilisation. These form the repository of the wisdom of great works by thinkers of past and the present. The holdings of these institutions are priceless heritage of the mankind as they preserve documents, ideas, and the oral and written records. To value the cultural heritage and to care for it as a treasure bequeathed to us by our ancestors is the major responsibility of libraries. The past records constitute a natural resource and are indispensable to the present generation as well as to the generations to come. Libraries preserve the documentary heritage resources for which they are primarily responsible. Any loss of such materials is simply irreplaceable. Therefore, preserving this intellectual, cultural heritage becomes not only the academic commitment but also the moral responsibility of the librarians/information scientists, who are in charge of these repositories. The high quality of the papers and the discussion represent the thinking and experience of experts in their particular fields. The contributed papers also relate to the methodology used in libraries in Asia to provide access to manuscripts and cultural heritage. The volume discusses best practices in Knowledge preservation and how to collaborate and preserve the culture. The book also deals with manuscript and archives issues in the digital era. The approach of this book is concise, comprehensively, covering all major aspects of preservation and conservation through libraries. The readership of the book is not just limited to library and information science professionals, but also for those involved in conservation, preservation, restoration or other related disciplines. The book will be useful for librarians, archivists and conservators. We thank the Sunan Kalijaga University, Special Libraries Association- Asian Chapter for their trust and their constant support, all the contributors for their submissions, the members of the Local and International Committee for their reviewing effort for making this publication possible

Governance and Conservation Effectiveness in Protected Areas and Indigenous and Locally Managed Areas

Increased conservation action to protect more habitat and species is fueling a vigorous debate about the relative effectiveness of different sorts of protected areas. Here we review the literature that compares the effectiveness of protected areas managed by states and areas managed by Indigenous peoples and/or local communities. We argue that these can be hard comparisons to make. Robust comparative case studies are rare, and the epistemic communities producing them are fractured by language, discipline, and geography. Furthermore the distinction between these different forms of protection on the ground can be blurred. We also have to be careful about the value of this sort of comparison as the consequences of different forms of conservation for people and nonhuman nature are messy and diverse. Measures of effectiveness, moreover, focus on specific dimensions of conservation performance, which can omit other important dimensions. With these caveats, we report on findings observed by multiple study groups focusing on different regions and issues whose reports have been compiled into this article. There is a tendency in the data for community-based or co-managed governance arrangements to produce beneficial outcomes for people and nature. These arrangements are often accompanied by struggles between rural groups and powerful states. Findings are highly context specific and global generalizations have limited value

Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation

Domain adaptation deals with training models using large scale labeled data from a specific source domain and then adapting the knowledge to certain target domains that have few or no labels. Many prior works learn domain agnostic feature representations for this purpose using a global distribution alignment objective which does not take into account the finer class specific structure in the source and target domains. We address this issue in our work and propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. ILA-DA simultaneously accounts for intra-class clustering as well as inter-class separation among the categories, resulting in less noisy classifier boundaries, improved transferability and increased accuracy. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets and provide insights into the proposed alignment approach