541 research outputs found
Personalized Recommendation for Balancing Content Generation and Usage on Two-Sided Entertainment Platforms
Online entertainment platforms such as Youtube host a vast amount of user-generated content (UGC). The unique feature of two-sided UGC entertainment platforms is that creators’ content generation and users’ content usage can influence each other. However, traditional recommender systems often emphasize content usage but ignore content generation, leading to a misalignment between these two goals. To address the challenge, this paper proposes a prescriptive uplift framework to balance content generation and usage through personalized recommendations. Specifically, we first predict the heterogeneous treatment effects (HTEs) of recommended contents on creators’ content generation and users’ content usage, then consider these two predicted HTEs simultaneously in an optimization model to determine the recommended contents for each user. Using a large-scale real-world dataset, we demonstrate that the proposed recommendation method better balances content generation and usage and brings a 42% increase in participants’ activity compared to existing benchmark methods
Segue: Side-information Guided Generative Unlearnable Examples for Facial Privacy Protection in Real World
The widespread use of face recognition technology has given rise to privacy
concerns, as many individuals are worried about the collection and utilization
of their facial data. To address these concerns, researchers are actively
exploring the concept of ``unlearnable examples", by adding imperceptible
perturbation to data in the model training stage, which aims to prevent the
model from learning discriminate features of the target face. However, current
methods are inefficient and cannot guarantee transferability and robustness at
the same time, causing impracticality in the real world. To remedy it, we
propose a novel method called Segue: Side-information guided generative
unlearnable examples. Specifically, we leverage a once-trained multiple-used
model to generate the desired perturbation rather than the time-consuming
gradient-based method. To improve transferability, we introduce side
information such as true labels and pseudo labels, which are inherently
consistent across different scenarios. For robustness enhancement, a distortion
layer is integrated into the training pipeline. Extensive experiments
demonstrate that the proposed Segue is much faster than previous methods
(1000) and achieves transferable effectiveness across different
datasets and model architectures. Furthermore, it can resist JPEG compression,
adversarial training, and some standard data augmentations
Local BDNF Delivery to the Injured Cervical Spinal Cord using an Engineered Hydrogel Enhances Diaphragmatic Respiratory Function.
We developed an innovative biomaterial-based approach to repair the critical neural circuitry that controls diaphragm activation by locally delivering brain-derived neurotrophic factor (BDNF) to injured cervical spinal cord. BDNF can be used to restore respiratory function via a number of potential repair mechanisms; however, widespread BDNF biodistribution resulting from delivery methods such as systemic injection or lumbar puncture can lead to inefficient drug delivery and adverse side effects. As a viable alternative, we developed a novel hydrogel-based system loaded with polysaccharide-BDNF particles self-assembled by electrostatic interactions that can be safely implanted in the intrathecal space for achieving local BDNF delivery with controlled dosing and duration. Implantation of BDNF hydrogel after C4/C5 contusion-type spinal cord injury (SCI) in female rats robustly preserved diaphragm function, as assessed b
BLAT: Bootstrapping Language-Audio Pre-training based on AudioSet Tag-guided Synthetic Data
Compared with ample visual-text pre-training research, few works explore
audio-text pre-training, mostly due to the lack of sufficient parallel
audio-text data. Most existing methods incorporate the visual modality as a
pivot for audio-text pre-training, which inevitably induces data noise. In this
paper, we propose BLAT: Bootstrapping Language-Audio pre-training based on
Tag-guided synthetic data. We utilize audio captioning to generate text
directly from audio, without the aid of the visual modality so that potential
noise from modality mismatch is eliminated. Furthermore, we propose caption
generation under the guidance of AudioSet tags, leading to more accurate
captions. With the above two improvements, we curate high-quality, large-scale
parallel audio-text data, based on which we perform audio-text pre-training.
Evaluation on a series of downstream tasks indicates that BLAT achieves SOTA
zero-shot classification performance on most datasets and significant
performance improvement when fine-tuned on downstream tasks, suggesting the
effectiveness of our synthetic data
Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V
In this paper, we critically evaluate the capabilities of the
state-of-the-art multimodal large language model, i.e., GPT-4 with Vision
(GPT-4V), on Visual Question Answering (VQA) task. Our experiments thoroughly
assess GPT-4V's proficiency in answering questions paired with images using
both pathology and radiology datasets from 11 modalities (e.g. Microscopy,
Dermoscopy, X-ray, CT, etc.) and fifteen objects of interests (brain, liver,
lung, etc.). Our datasets encompass a comprehensive range of medical inquiries,
including sixteen distinct question types. Throughout our evaluations, we
devised textual prompts for GPT-4V, directing it to synergize visual and
textual information. The experiments with accuracy score conclude that the
current version of GPT-4V is not recommended for real-world diagnostics due to
its unreliable and suboptimal accuracy in responding to diagnostic medical
questions. In addition, we delineate seven unique facets of GPT-4V's behavior
in medical VQA, highlighting its constraints within this complex arena. The
complete details of our evaluation cases are accessible at
https://github.com/ZhilingYan/GPT4V-Medical-Report
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