Tootfinder

This https://arxiv.org/abs/2307.15841 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Pulse optimization for high-precision motional-mode characterization in trapped-ion quantum computers
High-fidelity operation of quantum computers requires precise knowledge of the physical system through characterization. For motion-mediated entanglement generation in trapped ions, it is crucial to have precise knowledge of the motional-mode parameters such as the mode frequencies and the Lamb-Dicke parameters. Unfortunately, the state-of-the-art mode-characterization schemes do not easily render the mode parameters in a sufficiently scalable and accurate fashion, due to the unwanted excitatio…

"He is mandated to compile and share the achievements of the Islamic Revolution in culture, science, and art on his internet platform. Furthermore, he is required to prepare a summary of two books regarding women’s status in Islam, produce a song addressing “USA’s atrocities against humanity,” collect cases of human rights violations by U.S. governments over the past century, and participate in behavior and knowledge skills training courses in the field of art."

This https://arxiv.org/abs/2312.08864 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment
Deep learning-based video quality assessment (deep VQA) has demonstrated significant potential in surpassing conventional metrics, with promising improvements in terms of correlation with human perception. However, the practical deployment of such deep VQA models is often limited due to their high computational complexity and large memory requirements. To address this issue, we aim to significantly reduce the model size and runtime of one of the state-of-the-art deep VQA methods, RankDVQA, by e…

VeriEQL: Bounded Equivalence Verification for Complex SQL Queries with Integrity Constraints
Yang He, Pinhan Zhao, Xinyu Wang, Yuepeng Wang
https://arxiv.org/abs/2403.03193

VeriEQL: Bounded Equivalence Verification for Complex SQL Queries with Integrity Constraints
The task of SQL query equivalence checking is important in various real-world applications (including query rewriting and automated grading) that involve complex queries with integrity constraints; yet, state-of-the-art techniques are very limited in their capability of reasoning about complex features (e.g., those that involve sorting, case statement, rich integrity constraints, etc.) in real-life queries. To the best of our knowledge, we propose the first SMT-based approach and its implementa…

Whoop I got accepted to #pifcamp ! Looking forward to it including working with @… and @… on some e-textile sensor sound stuff

"By 1973, the first volume of the defining work of our craft, “The Art of Computer Programming” had been available in bookstores since 1968, and its first chapter literally consisted of a 100-something page long introduction to various mathematical concepts. Induction, logarithms, series, matrices, elementary number theory, permutations and factorials, Fibonacci numbers, are some of the subjects exposed in those beautifully typeset pages."

In Praise Of Mathematics
How much math knowledge does a programmer need to know to fulfill their job? How deep should the mathematical knowledge of full-stack engineers, computer security experts, or large language model engineers be? The spectrum of possible answers to these questions is too large to be useful. Perhaps the eternal answer "it depends" is enough?

For those of you who will still be there at #DHd2024 on Friday morning: if you want to know how to implement a #knowledgegraph based research data ecosystem (not only) for @nfdi4culture, please come and listen to our presentation on "Communities, Harvesting, and CGIF: Building the Research…

Learning general Gaussian mixtures with efficient score matching
Sitan Chen, Vasilis Kontonis, Kulin Shah
https://arxiv.org/abs/2404.18893 https://arxiv.org/pdf/2404.18893
arXiv:2404.18893v1 Announce Type: new
Abstract: We study the problem of learning mixtures of $k$ Gaussians in $d$ dimensions. We make no separation assumptions on the underlying mixture components: we only require that the covariance matrices have bounded condition number and that the means and covariances lie in a ball of bounded radius. We give an algorithm that draws $d^{\mathrm{poly}(k/\varepsilon)}$ samples from the target mixture, runs in sample-polynomial time, and constructs a sampler whose output distribution is $\varepsilon$-far from the unknown mixture in total variation. Prior works for this problem either (i) required exponential runtime in the dimension $d$, (ii) placed strong assumptions on the instance (e.g., spherical covariances or clusterability), or (iii) had doubly exponential dependence on the number of components $k$.
Our approach departs from commonly used techniques for this problem like the method of moments. Instead, we leverage a recently developed reduction, based on diffusion models, from distribution learning to a supervised learning task called score matching. We give an algorithm for the latter by proving a structural result showing that the score function of a Gaussian mixture can be approximated by a piecewise-polynomial function, and there is an efficient algorithm for finding it. To our knowledge, this is the first example of diffusion models achieving a state-of-the-art theoretical guarantee for an unsupervised learning task.

Integrating A.I. in Higher Education: Protocol for a Pilot Study with 'SAMCares: An Adaptive Learning Hub'
Syed Hasib Akhter Faruqui, Nazia Tasnim, Iftekhar Ibne Basith, Suleiman Obeidat, Faruk Yildiz
https://arxiv.org/abs/2405.00330

Integrating A.I. in Higher Education: Protocol for a Pilot Study with 'SAMCares: An Adaptive Learning Hub'
Learning never ends, and there is no age limit to grow yourself. However, the educational landscape may face challenges in effectively catering to students' inclusion and diverse learning needs. These students should have access to state-of-the-art methods for lecture delivery, online resources, and technology needs. However, with all the diverse learning sources, it becomes harder for students to comprehend a large amount of knowledge in a short period of time. Traditional assistive technologi…

This https://arxiv.org/abs/2402.13178 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Benchmarking Retrieval-Augmented Generation for Medicine
While large language models (LLMs) have achieved state-of-the-art performance on a wide range of medical question answering (QA) tasks, they still face challenges with hallucinations and outdated knowledge. Retrieval-augmented generation (RAG) is a promising solution and has been widely adopted. However, a RAG system can involve multiple flexible components, and there is a lack of best practices regarding the optimal RAG setting for various medical purposes. To systematically evaluate such syst…

A Flow-based Credibility Metric for Safety-critical Pedestrian Detection
Maria Lyssenko, Christoph Gladisch, Christian Heinzemann, Matthias Woehrle, Rudolph Triebel
https://arxiv.org/abs/2402.07642

A Flow-based Credibility Metric for Safety-critical Pedestrian Detection
Safety is of utmost importance for perception in automated driving (AD). However, a prime safety concern in state-of-the art object detection is that standard evaluation schemes utilize safety-agnostic metrics to argue sufficient detection performance. Hence, it is imperative to leverage supplementary domain knowledge to accentuate safety-critical misdetections during evaluation tasks. To tackle the underspecification, this paper introduces a novel credibility metric, called c-flow, for pedestr…

Integrating A.I. in Higher Education: Protocol for a Pilot Study with 'SAMCares: An Adaptive Learning Hub'
Syed Hasib Akhter Faruqui, Nazia Tasnim, Iftekhar Ibne Basith, Suleiman Obeidat, Faruk Yildiz
https://arxiv.org/abs/2405.00330

Integrating A.I. in Higher Education: Protocol for a Pilot Study with 'SAMCares: An Adaptive Learning Hub'
Learning never ends, and there is no age limit to grow yourself. However, the educational landscape may face challenges in effectively catering to students' inclusion and diverse learning needs. These students should have access to state-of-the-art methods for lecture delivery, online resources, and technology needs. However, with all the diverse learning sources, it becomes harder for students to comprehend a large amount of knowledge in a short period of time. Traditional assistive technologi…

This https://arxiv.org/abs/2312.08976 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Dynamic Retrieval-Augmented Generation
Current state-of-the-art large language models are effective in generating high-quality text and encapsulating a broad spectrum of world knowledge. These models, however, often hallucinate and lack locally relevant factual data. Retrieval-augmented approaches were introduced to overcome these problems and provide more accurate responses. Typically, the retrieved information is simply appended to the main request, restricting the context window size of the model. We propose a novel approach for …

NeuralDiffuser: Controllable fMRI Reconstruction with Primary Visual Feature Guided Diffusion
Haoyu Li, Hao Wu, Badong Chen
https://arxiv.org/abs/2402.13809 https://arxiv.org/pdf/2402.13809
arXiv:2402.13809v1 Announce Type: new
Abstract: Reconstructing visual stimuli from functional Magnetic Resonance Imaging (fMRI) based on Latent Diffusion Models (LDM) provides a fine-grained retrieval of the brain. A challenge persists in reconstructing a cohesive alignment of details (such as structure, background, texture, color, etc.). Moreover, LDMs would generate different image results even under the same conditions. For these, we first uncover the neuroscientific perspective of LDM-based methods that is top-down creation based on pre-trained knowledge from massive images but lack of detail-driven bottom-up perception resulting in unfaithful details. We propose NeuralDiffuser which introduces primary visual feature guidance to provide detail cues in the form of gradients, extending the bottom-up process for LDM-based methods to achieve faithful semantics and details. We also developed a novel guidance strategy to ensure the consistency of repeated reconstructions rather than a variety of results. We obtain the state-of-the-art performance of NeuralDiffuser on the Natural Senses Dataset (NSD), which offers more faithful details and consistent results.

HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning
Hanning Chen, Yang Ni, Ali Zakeri, Zhuowen Zou, Sanggeon Yun, Fei Wen, Behnam Khaleghi, Narayan Srinivasa, Hugo Latapie, Mohsen Imani
https://arxiv.org/abs/2403.05763

HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning
In recent times, a plethora of hardware accelerators have been put forth for graph learning applications such as vertex classification and graph classification. However, previous works have paid little attention to Knowledge Graph Completion (KGC), a task that is well-known for its significantly higher algorithm complexity. The state-of-the-art KGC solutions based on graph convolution neural network (GCN) involve extensive vertex/relation embedding updates and complicated score functions, which…

CKERC : Joint Large Language Models with Commonsense Knowledge for Emotion Recognition in Conversation
Yumeng Fu
https://arxiv.org/abs/2403.07260 https://<…

CKERC : Joint Large Language Models with Commonsense Knowledge for Emotion Recognition in Conversation
Emotion recognition in conversation (ERC) is a task which predicts the emotion of an utterance in the context of a conversation. It tightly depends on dialogue context, speaker identity information, multiparty dialogue scenario and so on. However, the state-of-the-art method (instructERC) solely identifying speaker, and ignores commonsense knowledge(i.e., reaction of the listeners and intention of the speaker, etc.) behind speakers during a conversation, which can deeply mine speaker informatio…

Zero-Shot Code Representation Learning via Prompt Tuning
Nan Cui, Xiaodong Gu, Beijun Shen
https://arxiv.org/abs/2404.08947 https://a…

Zero-Shot Code Representation Learning via Prompt Tuning
Learning code representations has been the core prerequisite of many software engineering tasks such as code clone detection and code generation. State-of-the-art program representation techniques mainly utilize pre-trained language models (PLMs) such as CodeBERT. A Transformer encoder is firstly pre-trained on a large-scale code corpus to acquire general knowledge about source code. The pre-trained model is then fine-tuned on specific tasks using an amount of labeled data. However, gathering t…

This https://arxiv.org/abs/2403.03193 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

VeriEQL: Bounded Equivalence Verification for Complex SQL Queries with Integrity Constraints
The task of SQL query equivalence checking is important in various real-world applications (including query rewriting and automated grading) that involve complex queries with integrity constraints; yet, state-of-the-art techniques are very limited in their capability of reasoning about complex features (e.g., those that involve sorting, case statement, rich integrity constraints, etc.) in real-life queries. To the best of our knowledge, we propose the first SMT-based approach and its implementa…

NeuralDiffuser: Controllable fMRI Reconstruction with Primary Visual Feature Guided Diffusion
Haoyu Li, Hao Wu, Badong Chen
https://arxiv.org/abs/2402.13809 https://arxiv.org/pdf/2402.13809
arXiv:2402.13809v1 Announce Type: new
Abstract: Reconstructing visual stimuli from functional Magnetic Resonance Imaging (fMRI) based on Latent Diffusion Models (LDM) provides a fine-grained retrieval of the brain. A challenge persists in reconstructing a cohesive alignment of details (such as structure, background, texture, color, etc.). Moreover, LDMs would generate different image results even under the same conditions. For these, we first uncover the neuroscientific perspective of LDM-based methods that is top-down creation based on pre-trained knowledge from massive images but lack of detail-driven bottom-up perception resulting in unfaithful details. We propose NeuralDiffuser which introduces primary visual feature guidance to provide detail cues in the form of gradients, extending the bottom-up process for LDM-based methods to achieve faithful semantics and details. We also developed a novel guidance strategy to ensure the consistency of repeated reconstructions rather than a variety of results. We obtain the state-of-the-art performance of NeuralDiffuser on the Natural Senses Dataset (NSD), which offers more faithful details and consistent results.

CKERC : Joint Large Language Models with Commonsense Knowledge for Emotion Recognition in Conversation
Yumeng Fu
https://arxiv.org/abs/2403.07260 https://<…

CKERC : Joint Large Language Models with Commonsense Knowledge for Emotion Recognition in Conversation
Emotion recognition in conversation (ERC) is a task which predicts the emotion of an utterance in the context of a conversation. It tightly depends on dialogue context, speaker identity information, multiparty dialogue scenario and so on. However, the state-of-the-art method (instructERC) solely identifying speaker, and ignores commonsense knowledge(i.e., reaction of the listeners and intention of the speaker, etc.) behind speakers during a conversation, which can deeply mine speaker informatio…

This https://arxiv.org/abs/2401.04079 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

RudolfV: A Foundation Model by Pathologists for Pathologists
Histopathology plays a central role in clinical medicine and biomedical research. While artificial intelligence shows promising results on many pathological tasks, generalization and dealing with rare diseases, where training data is scarce, remains a challenge. Distilling knowledge from unlabelled data into a foundation model before learning from, potentially limited, labelled data provides a viable path to address these challenges. In this work, we extend the state of the art of foundation mo…

This https://arxiv.org/abs/2402.03216 has been replaced.
link: https://scholar.google.com/scholar?q=a

BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
In this paper, we present a new embedding model, called M3-Embedding, which is distinguished for its versatility in Multi-Linguality, Multi-Functionality, and Multi-Granularity. It can support more than 100 working languages, leading to new state-of-the-art performances on multi-lingual and cross-lingual retrieval tasks. It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval, which provides a uni…