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20 Nov

Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications

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arXiv:2411.10544v1 Announce Type: new Abstract: Artificial intelligence based predictive models trained on the clinical notes can be demographically biased. This could lead to adverse healthcare disparities in predicting outcomes like length of stay of the patients. Thus, it is necessary to mitigate the demographic biases within these models. We proposed an implicit in-processing debiasing method to combat disparate treatment which occurs when the machine learning model predict different outcomes for individuals based on the sensitive attributes like gender, ethnicity, race, and likewise. For this purpose, we used clinical notes of heart failure patients and used diagnostic codes, procedure reports and…

20 Nov

MpoxVLM: A Vision-Language Model for Diagnosing Skin Lesions from Mpox Virus Infection

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arXiv:2411.10888v1 Announce Type: cross Abstract: In the aftermath of the COVID-19 pandemic and amid accelerating climate change, emerging infectious diseases, particularly those arising from zoonotic spillover, remain a global threat. Mpox (caused by the monkeypox virus) is a notable example of a zoonotic infection that often goes undiagnosed, especially as its rash progresses through stages, complicating detection across diverse populations with different presentations. In August 2024, the WHO Director-General declared the mpox outbreak a public health emergency of international concern for a second time. Despite the deployment of deep learning techniques for detecting diseases from skin lesion images, a robust…

20 Nov

Comparison of Multilingual and Bilingual Models for Satirical News Detection of Arabic and English

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arXiv:2411.10730v1 Announce Type: new Abstract: Satirical news is real news combined with a humorous comment or exaggerated content, and it often mimics the format and style of real news. However, satirical news is often misunderstood as misinformation, especially by individuals from different cultural and social backgrounds. This research addresses the challenge of distinguishing satire from truthful news by leveraging multilingual satire detection methods in English and Arabic. We explore both zero-shot and chain-of-thought (CoT) prompting using two language models, Jais-chat(13B) and LLaMA-2-chat(7B). Our results show that CoT prompting offers a significant advantage for the Jais-chat model over the LLaMA-2-chat model.…

20 Nov

DBHawk Flies with Text-to-SQL, SOC 2 Compliance

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Customers will be able to interact with their database using natural language thanks to the new text-to-SQL function in DBHawk, a database access tool developed by Datasparc. The company also announced its SOC 2 Type II compliance and an expansion of its partnership with IBM. DBHawk is Datasparc’s handy database tool that lets different users accomplish a range of different database tasks. For instance, data analysts can use it to write and execute SQL queries against dozens of databases, including relational and NoSQL databases. Data engineers can use it to perform joins and schedule SQL queries. Administrators can also use…

19 Nov

Unmasking Parkinson’s Disease with Smile: An AI-enabled Screening Framework

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[Submitted on 3 Aug 2023 (v1), last revised 18 Nov 2024 (this version, v2)] View a PDF of the paper titled Unmasking Parkinson's Disease with Smile: An AI-enabled Screening Framework, by Tariq Adnan and 8 other authors View PDF HTML (experimental) Abstract:We present an efficient and accessible PD screening method by leveraging AI-driven models enabled by the largest video dataset of facial expressions from 1,059 unique participants. This dataset includes 256 individuals with PD, 165 clinically diagnosed, and 91 self-reported. Participants used webcams to record themselves mimicking three facial expressions (smile, disgust, and surprise) from diverse sources encompassing their homes…

19 Nov

Any2Any: Incomplete Multimodal Retrieval with Conformal Prediction

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19 Nov

Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts

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[Submitted on 16 Feb 2024 (v1), last revised 18 Nov 2024 (this version, v4)] View a PDF of the paper titled Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts, by Xianzhen Luo and 7 other authors View PDF HTML (experimental) Abstract:Program of Thoughts (PoT) is an approach characterized by its executable intermediate steps, which ensure the accuracy of the logical calculations in the reasoning process. Currently, PoT primarily uses Python. However, relying solely on a single language may result in suboptimal solutions and overlook the potential benefits of other programming languages. In this paper, we conduct…

19 Nov

Zilliz Boasts 10X Performance Boost in Vector Database

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(Tee11/Shutterstock) Companies that are running into performance walls as they scale up their vector databases may want to check out the latest update to Zilliz Cloud, a hosted version of the Milvus database from Zilliz. The database maker says the update brings a 10x boost in throughput and latency, three new search algorithms that improve search accuracy from 70% to 95%, and a new AutoIndexer that eliminates the need to manually configure the database for peak performance on each data set. Interest in vector databases is booming at the moment, thanks in large part to the explosion in use of…

19 Nov

SoftLMs: Efficient Adaptive Low-Rank Approximation of Language Models using Soft-Thresholding Mechanism

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[Submitted on 15 Nov 2024] View a PDF of the paper titled SoftLMs: Efficient Adaptive Low-Rank Approximation of Language Models using Soft-Thresholding Mechanism, by Priyansh Bhatnagar and 2 other authors View PDF HTML (experimental) Abstract:Extensive efforts have been made to boost the performance in the domain of language models by introducing various attention-based transformers. However, the inclusion of linear layers with large dimensions contributes to significant computational and memory overheads. The escalating computational demands of these models necessitate the development of various compression techniques to ensure their deployment on devices, particularly in resource-constrained environments. In this paper, we propose a…

19 Nov

TESGNN: Temporal Equivariant Scene Graph Neural Networks for Efficient and Robust Multi-View 3D Scene Understanding

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arXiv:2411.10509v1 Announce Type: new Abstract: Scene graphs have proven to be highly effective for various scene understanding tasks due to their compact and explicit representation of relational information. However, current methods often overlook the critical importance of preserving symmetry when generating scene graphs from 3D point clouds, which can lead to reduced accuracy and robustness, particularly when dealing with noisy, multi-view data. This work, to the best of our knowledge, presents the first implementation of an Equivariant Scene Graph Neural Network (ESGNN) to generate semantic scene graphs from 3D point clouds, specifically for enhanced scene understanding. Furthermore, a significant limitation…