17,771 research outputs found
Deliberative Democracy, Perspective from Indo-Pacific Blogosphere: A Survey
Deliberation and communication within the national space have had numerous
implications on how citizens online and offline perceive government. It has
also impacted the relationship between opposition and incumbent governments in
the Indo-Pacific region. Authoritarian regimes have historically had control
over the dissemination of information, thereby controlling power and limiting
challenges from citizens who are not comfortable with the status quo. Social
media and blogs have allowed citizens of these countries to find a way to
communicate, and the exchange of information continues to rise. The quest by
both authoritarian and democratic regimes to control or influence the
discussion in the public sphere has given rise to concepts like cybertroopers,
congressional bloggers, and commentator bloggers, among others. Cybertroopers
have become the de facto online soldiers of authoritarian regimes who must
embrace democracy. While commentator and congressional bloggers have acted with
different strategies, commentator bloggers educate online citizens with
knowledgeable information to influence the citizens. Congressional bloggers are
political officeholders who use blogging to communicate their positions on
ongoing national issues. Therefore, this work has explored various concepts
synonymous with the Indo-Pacific public sphere and how it shapes elections and
democracy
GEE Training Manual on Use of Earth Observation data and Google Earth Engine monitoring and early warning of floods and droughts in Zambia
This training manual supported participants in learning the pre-processing tool to provide the user with enhanced time-series processing capabilities and access to various open-source satellite data, learning basic scripts in Google Earth Engine for activities related to floods and drought in showcasing the application of water resource management. Specifically, the experts will give more focus to Google’s Earth Engine platform to showcase large- and small-scale scientific analysis and visualization of geospatial datasets. The codes and step by step procedure are given in the manual
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries
MuRAL: Multi-Scale Region-based Active Learning for Object Detection
Obtaining large-scale labeled object detection dataset can be costly and
time-consuming, as it involves annotating images with bounding boxes and class
labels. Thus, some specialized active learning methods have been proposed to
reduce the cost by selecting either coarse-grained samples or fine-grained
instances from unlabeled data for labeling. However, the former approaches
suffer from redundant labeling, while the latter methods generally lead to
training instability and sampling bias. To address these challenges, we propose
a novel approach called Multi-scale Region-based Active Learning (MuRAL) for
object detection. MuRAL identifies informative regions of various scales to
reduce annotation costs for well-learned objects and improve training
performance. The informative region score is designed to consider both the
predicted confidence of instances and the distribution of each object category,
enabling our method to focus more on difficult-to-detect classes. Moreover,
MuRAL employs a scale-aware selection strategy that ensures diverse regions are
selected from different scales for labeling and downstream finetuning, which
enhances training stability. Our proposed method surpasses all existing
coarse-grained and fine-grained baselines on Cityscapes and MS COCO datasets,
and demonstrates significant improvement in difficult category performance
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions
The robustness of 3D perception systems under natural corruptions from
environments and sensors is pivotal for safety-critical applications. Existing
large-scale 3D perception datasets often contain data that are meticulously
cleaned. Such configurations, however, cannot reflect the reliability of
perception models during the deployment stage. In this work, we present Robo3D,
the first comprehensive benchmark heading toward probing the robustness of 3D
detectors and segmentors under out-of-distribution scenarios against natural
corruptions that occur in real-world environments. Specifically, we consider
eight corruption types stemming from adversarial weather conditions, external
disturbances, and internal sensor failure. We uncover that, although promising
results have been progressively achieved on standard benchmarks,
state-of-the-art 3D perception models are at risk of being vulnerable to
corruptions. We draw key observations on the use of data representations,
augmentation schemes, and training strategies, that could severely affect the
model's performance. To pursue better robustness, we propose a
density-insensitive training framework along with a simple flexible
voxelization strategy to enhance the model resiliency. We hope our benchmark
and approach could inspire future research in designing more robust and
reliable 3D perception models. Our robustness benchmark suite is publicly
available.Comment: 33 pages, 26 figures, 26 tables; code at
https://github.com/ldkong1205/Robo3D project page at
https://ldkong.com/Robo3
Procedure-Aware Pretraining for Instructional Video Understanding
Our goal is to learn a video representation that is useful for downstream
procedure understanding tasks in instructional videos. Due to the small amount
of available annotations, a key challenge in procedure understanding is to be
able to extract from unlabeled videos the procedural knowledge such as the
identity of the task (e.g., 'make latte'), its steps (e.g., 'pour milk'), or
the potential next steps given partial progress in its execution. Our main
insight is that instructional videos depict sequences of steps that repeat
between instances of the same or different tasks, and that this structure can
be well represented by a Procedural Knowledge Graph (PKG), where nodes are
discrete steps and edges connect steps that occur sequentially in the
instructional activities. This graph can then be used to generate pseudo labels
to train a video representation that encodes the procedural knowledge in a more
accessible form to generalize to multiple procedure understanding tasks. We
build a PKG by combining information from a text-based procedural knowledge
database and an unlabeled instructional video corpus and then use it to
generate training pseudo labels with four novel pre-training objectives. We
call this PKG-based pre-training procedure and the resulting model Paprika,
Procedure-Aware PRe-training for Instructional Knowledge Acquisition. We
evaluate Paprika on COIN and CrossTask for procedure understanding tasks such
as task recognition, step recognition, and step forecasting. Paprika yields a
video representation that improves over the state of the art: up to 11.23%
gains in accuracy in 12 evaluation settings. Implementation is available at
https://github.com/salesforce/paprika.Comment: CVPR 202
The cosmic waltz of Coma Berenices and Latyshev 2 (Group X). Membership, phase-space structure, mass, and energy distributions
Context. Open clusters (OCs) are fundamental benchmarks where theories of
star formation and stellar evolution can be tested and validated. Coma Ber and
Latyshev 2 (Group X) are the second and third OCs closest to the Sun, making
them excellent targets to search for low-mass stars and ultra-cool dwarfs. In
addition, this pair will experience a flyby in 10-16 Myr which makes it a
benchmark to test OCs pair interactions. Aims. We aim at analysing the
membership, luminosity, mass, phase-space (i.e., positions and velocities), and
energy distributions for Coma Ber and Latyshev 2 and test the hypothesis of the
mixing of their populations at the encounter time. Methods. We develop a new
phase-space membership methodology and apply it to Gaia data. With the
recovered members we infer the phase-space, luminosity and mass distributions
using publicly available Bayesian inference codes. Then, with a publicly
available orbit integration code and members' positions and velocities, we
integrate their orbits 20 Myr into the future. Results. In Coma Ber, we
identify 302 candidate members distributed in the core and tidal tails. The
tails are dynamically cold and asymmetrically populated. The stellar system
called Group X is made of two structures: the disrupted OC Latyshev 2 (186
candidate members) and a loose stellar association called Mecayotl 1 (146
candidate members), both of them will fly by Coma Ber in Myr and
Myr, respectively, and each other in Myr. Conclusions.
We study the dynamical properties of the core and tails of Coma Ber and also
confirm the existence of the OC Latyshev 2 and its neighbour stellar
association Mecayotl 1. Although these three systems will experience encounters
we find no evidence supporting the mixing of their populations.Comment: 25 pages, 19 figures, accepted for publication in Astronomy &
Astrophysic
Victims' Access to Justice in Trinidad and Tobago: An exploratory study of experiences and challenges of accessing criminal justice in a post-colonial society
This thesis investigates victims' access to justice in Trinidad and Tobago, using their own narratives. It seeks to capture how their experiences affected their identities as victims and citizens, alongside their perceptions of legitimacy regarding the criminal justice system. While there have been some reforms in the administration of criminal justice in Trinidad and Tobago, such reforms have not focused on victims' accessibility to the justice system. Using grounded theory methodology, qualitative data was collected through 31 in-depth interviews with victims and victim advocates. The analysis found that victims experienced interpersonal, structural, and systemic barriers at varying levels throughout the criminal justice system, which manifested as institutionalized secondary victimization, silencing and inequality. This thesis argues that such experiences not only served to appropriate conflict but demonstrates that access is often given in a very narrow sense. Furthermore, it shows a failure to encompass access to justice as appropriated conflicts are left to stagnate in the system as there is often very little resolution. Adopting a postcolonial lens to analyse victims' experiences, the analysis identified othering practices that served to institutionalize the vulnerability and powerlessness associated with victim identities. Here, it is argued that these othering practices also affected the rights consciousness of victims, delegitimating their identities as citizens. Moreover, as a result of their experiences, victims had mixed perceptions of the justice system. It is argued that while the system is a legitimate authority victims' endorsement of the system is questionable, therefore victims' experiences suggest that there is a reinforcement of the system's legal hegemony. The findings suggest that within the legal system of Trinidad and Tobago, legacies of colonialism shape the postcolonial present as the psychology and inequalities of the past are present in the interactions and processes of justice. These findings are relevant for policymakers in Trinidad and Tobago and other regions. From this study it is recognized that, to improve access to justice for victims, there needs to be a move towards victim empowerment that promotes resilience and enhances social capital. Going forward it is noted that there is a need for further research
Interval Type-2 Beta Fuzzy Near Sets Approach to Content-Based Image Retrieval
In computer-based search systems, similarity plays a key role in replicating the human search process. Indeed, the human search process underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. The search for images consists of establishing a correspondence between the available image and that sought by the user, by measuring the similarity between the images. Image search by content is generaly based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends notonly on the criteria of the search but also on the representation of the characteristics of the image. This is the main idea of a content-based image retrieval (CBIR) system. In this article, first, we constructed type-2 beta fuzzy membership of descriptor vectors to help manage inaccuracy and uncertainty of characteristics extracted the feature of images. Subsequently, the retrieved images are ranked according to the novel similarity measure, noted type-2 fuzzy nearness measure (IT2FNM). By analogy to Type-2 Fuzzy Logic and motivated by near sets theory, we advanced a new fuzzy similarity measure (FSM) noted interval type-2 fuzzy nearness measure (IT-2 FNM). Then, we proposed three new IT-2 FSMs and we have provided mathematical justification to demonstrate that the proposed FSMs satisfy proximity properties (i.e. reflexivity, transitivity, symmetry, and overlapping). Experimental results generated using three image databases showing consistent and significant results
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