168 research outputs found
Heterogeneous 360 Degree Videos in Metaverse: Differentiated Reinforcement Learning Approaches
Advanced video technologies are driving the development of the futuristic
Metaverse, which aims to connect users from anywhere and anytime. As such, the
use cases for users will be much more diverse, leading to a mix of 360-degree
videos with two types: non-VR and VR 360-degree videos. This paper presents a
novel Quality of Service model for heterogeneous 360-degree videos with
different requirements for frame rates and cybersickness. We propose a
frame-slotted structure and conduct frame-wise optimization using self-designed
differentiated deep reinforcement learning algorithms. Specifically, we design
two structures, Separate Input Differentiated Output (SIDO) and Merged Input
Differentiated Output (MIDO), for this heterogeneous scenario. We also conduct
comprehensive experiments to demonstrate their effectiveness.Comment: This paper appears in IEEE Global Communications Conference
(GLOBECOM) 202
One-Shot Relational Learning for Knowledge Graphs
Knowledge graphs (KGs) are the key components of various natural language
processing applications. To further expand KGs' coverage, previous studies on
knowledge graph completion usually require a large number of training instances
for each relation. However, we observe that long-tail relations are actually
more common in KGs and those newly added relations often do not have many known
triples for training. In this work, we aim at predicting new facts under a
challenging setting where only one training instance is available. We propose a
one-shot relational learning framework, which utilizes the knowledge extracted
by embedding models and learns a matching metric by considering both the
learned embeddings and one-hop graph structures. Empirically, our model yields
considerable performance improvements over existing embedding models, and also
eliminates the need of re-training the embedding models when dealing with newly
added relations.Comment: EMNLP 201
Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach
The efficient deployment and fine-tuning of foundation models are pivotal in
contemporary artificial intelligence. In this study, we present a
groundbreaking paradigm integrating Mobile Edge Computing (MEC) with foundation
models, specifically designed to enhance local task performance on user
equipment (UE). Central to our approach is the innovative Emulator-Adapter
architecture, segmenting the foundation model into two cohesive modules. This
design not only conserves computational resources but also ensures adaptability
and fine-tuning efficiency for downstream tasks. Additionally, we introduce an
advanced resource allocation mechanism that is fine-tuned to the needs of the
Emulator-Adapter structure in decentralized settings. To address the challenges
presented by this system, we employ a hybrid multi-agent Deep Reinforcement
Learning (DRL) strategy, adept at handling mixed discrete-continuous action
spaces, ensuring dynamic and optimal resource allocations. Our comprehensive
simulations and validations underscore the practical viability of our approach,
demonstrating its robustness, efficiency, and scalability. Collectively, this
work offers a fresh perspective on deploying foundation models and balancing
computational efficiency with task proficiency
Automated Testing and Improvement of Named Entity Recognition Systems
Named entity recognition (NER) systems have seen rapid progress in recent
years due to the development of deep neural networks. These systems are widely
used in various natural language processing applications, such as information
extraction, question answering, and sentiment analysis. However, the complexity
and intractability of deep neural networks can make NER systems unreliable in
certain circumstances, resulting in incorrect predictions. For example, NER
systems may misidentify female names as chemicals or fail to recognize the
names of minority groups, leading to user dissatisfaction. To tackle this
problem, we introduce TIN, a novel, widely applicable approach for
automatically testing and repairing various NER systems. The key idea for
automated testing is that the NER predictions of the same named entities under
similar contexts should be identical. The core idea for automated repairing is
that similar named entities should have the same NER prediction under the same
context. We use TIN to test two SOTA NER models and two commercial NER APIs,
i.e., Azure NER and AWS NER. We manually verify 784 of the suspicious issues
reported by TIN and find that 702 are erroneous issues, leading to high
precision (85.0%-93.4%) across four categories of NER errors: omission,
over-labeling, incorrect category, and range error. For automated repairing,
TIN achieves a high error reduction rate (26.8%-50.6%) over the four systems
under test, which successfully repairs 1,056 out of the 1,877 reported NER
errors.Comment: Accepted by ESEC/FSE'2
CIC Rearrangement Sarcoma: A Case Report and Literature Review
Background: CIC-rearranged sarcoma (capicua transcriptional repressor- rearranged sarcoma, CRS) is a rare type of undifferentiated small round-cell sarcoma. There are few reported cases of CRS; in 2017, 115 cases were reported abroad and 10 cases were reported in China. Case summary: The patient is a 41-year-old male who presented with a mass in the left lumbar region for more than 1 month. Tumor excision was performed at another hospital. Pathology results indicated CRS. PET-CT indicated changes in the left lumbar region, and postoperative tissue repair changes were considered. However, combined with the medical history and imaging features, the clinical diagnosis was considered recurrence of tumor in the left lumbar region. Postoperatively, the patient was transferred to the burn department for pedicled skin-flap repair. Conclusion: CRS is rare, and the prognosis of these patients is poor. Surgical resection of the lesion is the first choice for patients without metastasis
Towards Mitigating Spurious Correlations in the Wild: A Benchmark and a more Realistic Dataset
Deep neural networks often exploit non-predictive features that are
spuriously correlated with class labels, leading to poor performance on groups
of examples without such features. Despite the growing body of recent works on
remedying spurious correlations, the lack of a standardized benchmark hinders
reproducible evaluation and comparison of the proposed solutions. To address
this, we present SpuCo, a python package with modular implementations of
state-of-the-art solutions enabling easy and reproducible evaluation of current
methods. Using SpuCo, we demonstrate the limitations of existing datasets and
evaluation schemes in validating the learning of predictive features over
spurious ones. To overcome these limitations, we propose two new vision
datasets: (1) SpuCoMNIST, a synthetic dataset that enables simulating the
effect of real world data properties e.g. difficulty of learning spurious
feature, as well as noise in the labels and features; (2) SpuCoAnimals, a
large-scale dataset curated from ImageNet that captures spurious correlations
in the wild much more closely than existing datasets. These contributions
highlight the shortcomings of current methods and provide a direction for
future research in tackling spurious correlations. SpuCo, containing the
benchmark and datasets, can be found at https://github.com/BigML-CS-UCLA/SpuCo,
with detailed documentation available at
https://spuco.readthedocs.io/en/latest/.Comment: Package: https://github.com/BigML-CS-UCLA/SpuC
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