660 research outputs found
Combined Nutrition and Exercise Interventions in Community Groups
Diet and physical activity are two key modifiable lifestyle factors that influence health across the lifespan (prevention and management of chronic diseases and reduction of the risk of premature death through several biological mechanisms). Community-based interventions contribute to public health, as they have the potential to reach high population-level impact, through the focus on groups that share a common culture or identity in their natural living environment. While the health benefits of a balanced diet and regular physical activity are commonly studied separately, interventions that combine these two lifestyle factors have the potential to induce greater benefits in community groups rather than strategies focusing only on one or the other. Thus, this Special Issue entitled “Combined Nutrition and Exercise Interventions in Community Groups” is comprised of manuscripts that highlight this combined approach (balanced diet and regular physical activity) in community settings. The contributors to this Special Issue are well-recognized professionals in complementary fields such as education, public health, nutrition, and exercise. This Special Issue highlights the latest research regarding combined nutrition and exercise interventions among different community groups and includes research articles developed through five continents (Africa, Asia, America, Europe and Oceania), as well as reviews and systematic reviews
On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry
Background: Providing optimal care for trauma, the leading cause of death for young adults, remains a challenge e.g., due to field triage limitations in assessing a patient’s condition and deciding on transport destination. Data-driven On Scene Injury Severity Prediction (OSISP) models for motor vehicle crashes have shown potential for providing real-time decision support. The objective of this study is therefore to evaluate if an Artificial Intelligence (AI) based clinical decision support system can identify severely injured trauma patients in the prehospital setting. Methods: The Swedish Trauma Registry was used to train and validate five models – Logistic Regression, Random Forest, XGBoost, Support Vector Machine and Artificial Neural Network – in a stratified 10-fold cross validation setting and hold-out analysis. The models performed binary classification of the New Injury Severity Score and were evaluated using accuracy metrics, area under the receiver operating characteristic curve (AUC) and Precision-Recall curve (AUCPR), and under- and overtriage rates. Results: There were 75,602 registrations between 2013–2020 and 47,357 (62.6%) remained after eligibility criteria were applied. Models were based on 21 predictors, including injury location. From the clinical outcome, about 40% of patients\ua0were undertriaged and 46% were overtriaged. Models demonstrated potential for improved triaging and yielded AUC between 0.80–0.89 and AUCPR between 0.43–0.62. Conclusions: AI based OSISP models have potential to provide support during assessment of injury severity. The findings may be used for developing tools to complement field triage protocols, with potential to improve prehospital trauma care and thereby reduce morbidity and mortality for a large patient population
Leveraging Large Language Models in Conversational Recommender Systems
A Conversational Recommender System (CRS) offers increased transparency and
control to users by enabling them to engage with the system through a real-time
multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an
unprecedented ability to converse naturally and incorporate world knowledge and
common-sense reasoning into language understanding, unlocking the potential of
this paradigm. However, effectively leveraging LLMs within a CRS introduces new
technical challenges, including properly understanding and controlling a
complex conversation and retrieving from external sources of information. These
issues are exacerbated by a large, evolving item corpus and a lack of
conversational data for training. In this paper, we provide a roadmap for
building an end-to-end large-scale CRS using LLMs. In particular, we propose
new implementations for user preference understanding, flexible dialogue
management and explainable recommendations as part of an integrated
architecture powered by LLMs. For improved personalization, we describe how an
LLM can consume interpretable natural language user profiles and use them to
modulate session-level context. To overcome conversational data limitations in
the absence of an existing production CRS, we propose techniques for building a
controllable LLM-based user simulator to generate synthetic conversations. As a
proof of concept we introduce RecLLM, a large-scale CRS for YouTube videos
built on LaMDA, and demonstrate its fluency and diverse functionality through
some illustrative example conversations
iQPP: A Benchmark for Image Query Performance Prediction
To date, query performance prediction (QPP) in the context of content-based
image retrieval remains a largely unexplored task, especially in the
query-by-example scenario, where the query is an image. To boost the
exploration of the QPP task in image retrieval, we propose the first benchmark
for image query performance prediction (iQPP). First, we establish a set of
four data sets (PASCAL VOC 2012, Caltech-101, ROxford5k and RParis6k) and
estimate the ground-truth difficulty of each query as the average precision or
the precision@k, using two state-of-the-art image retrieval models. Next, we
propose and evaluate novel pre-retrieval and post-retrieval query performance
predictors, comparing them with existing or adapted (from text to image)
predictors. The empirical results show that most predictors do not generalize
across evaluation scenarios. Our comprehensive experiments indicate that iQPP
is a challenging benchmark, revealing an important research gap that needs to
be addressed in future work. We release our code and data as open source at
https://github.com/Eduard6421/iQPP, to foster future research.Comment: Accepted at SIGIR 202
Metric Optimization and Mainstream Bias Mitigation in Recommender Systems
The first part of this thesis focuses on maximizing the overall
recommendation accuracy. This accuracy is usually evaluated with some
user-oriented metric tailored to the recommendation scenario, but because
recommendation is usually treated as a machine learning problem, recommendation
models are trained to maximize some other generic criteria that does not
necessarily align with the criteria ultimately captured by the user-oriented
evaluation metric. Recent research aims at bridging this gap between training
and evaluation via direct ranking optimization, but still assumes that the
metric used for evaluation should also be the metric used for training. We
challenge this assumption, mainly because some metrics are more informative
than others. Indeed, we show that models trained via the optimization of a loss
inspired by Rank-Biased Precision (RBP) tend to yield higher accuracy, even
when accuracy is measured with metrics other than RBP. However, the superiority
of this RBP-inspired loss stems from further benefiting users who are already
well-served, rather than helping those who are not.
This observation inspires the second part of this thesis, where our focus
turns to helping non-mainstream users. These are users who are difficult to
recommend to either because there is not enough data to model them, or because
they have niche taste and thus few similar users to look at when recommending
in a collaborative way. These differences in mainstreamness introduce a bias
reflected in an accuracy gap between users or user groups, which we try to
narrow.Comment: PhD Thesis defended on Nov 14, 202
Large Language Models for Information Retrieval: A Survey
As a primary means of information acquisition, information retrieval (IR)
systems, such as search engines, have integrated themselves into our daily
lives. These systems also serve as components of dialogue, question-answering,
and recommender systems. The trajectory of IR has evolved dynamically from its
origins in term-based methods to its integration with advanced neural models.
While the neural models excel at capturing complex contextual signals and
semantic nuances, thereby reshaping the IR landscape, they still face
challenges such as data scarcity, interpretability, and the generation of
contextually plausible yet potentially inaccurate responses. This evolution
requires a combination of both traditional methods (such as term-based sparse
retrieval methods with rapid response) and modern neural architectures (such as
language models with powerful language understanding capacity). Meanwhile, the
emergence of large language models (LLMs), typified by ChatGPT and GPT-4, has
revolutionized natural language processing due to their remarkable language
understanding, generation, generalization, and reasoning abilities.
Consequently, recent research has sought to leverage LLMs to improve IR
systems. Given the rapid evolution of this research trajectory, it is necessary
to consolidate existing methodologies and provide nuanced insights through a
comprehensive overview. In this survey, we delve into the confluence of LLMs
and IR systems, including crucial aspects such as query rewriters, retrievers,
rerankers, and readers. Additionally, we explore promising directions within
this expanding field
Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores
Interactive reinforcement learning has shown promise in learning complex
robotic tasks. However, the process can be human-intensive due to the
requirement of large amount of interactive feedback. This paper presents a new
method that uses scores provided by humans, instead of pairwise preferences, to
improve the feedback efficiency of interactive reinforcement learning. Our key
insight is that scores can yield significantly more data than pairwise
preferences. Specifically, we require a teacher to interactively score the full
trajectories of an agent to train a behavioral policy in a sparse reward
environment. To avoid unstable scores given by human negatively impact the
training process, we propose an adaptive learning scheme. This enables the
learning paradigm to be insensitive to imperfect or unreliable scores. We
extensively evaluate our method on robotic locomotion and manipulation tasks.
The results show that the proposed method can efficiently learn near-optimal
policies by adaptive learning from scores, while requiring less feedback
compared to pairwise preference learning methods. The source codes are publicly
available at https://github.com/SSKKai/Interactive-Scoring-IRL.Comment: Accepted by IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS 2023
When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top- Multi-Label Learning
With the great success of deep neural networks, adversarial learning has
received widespread attention in various studies, ranging from multi-class
learning to multi-label learning. However, existing adversarial attacks toward
multi-label learning only pursue the traditional visual imperceptibility but
ignore the new perceptible problem coming from measures such as Precision@
and mAP@. Specifically, when a well-trained multi-label classifier performs
far below the expectation on some samples, the victim can easily realize that
this performance degeneration stems from attack, rather than the model itself.
Therefore, an ideal multi-labeling adversarial attack should manage to not only
deceive visual perception but also evade monitoring of measures. To this end,
this paper first proposes the concept of measure imperceptibility. Then, a
novel loss function is devised to generate such adversarial perturbations that
could achieve both visual and measure imperceptibility. Furthermore, an
efficient algorithm, which enjoys a convex objective, is established to
optimize this objective. Finally, extensive experiments on large-scale
benchmark datasets, such as PASCAL VOC 2012, MS COCO, and NUS WIDE, demonstrate
the superiority of our proposed method in attacking the top- multi-label
systems.Comment: 22 pages, 7 figures, accepted by ACM MM 202
- …