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Paper Citation Record · LEDGER

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments

As of 21 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 5 inbound Pith citation observations for arXiv:2412.17301.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.17301 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:40:19.821012Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:12:55.707793Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-08T16:19:18.833181Z

Reference resolution

29 of 29 outbound references displayed

  • verified exact0
  • verified fuzzy8
  • unresolved21
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9f4d8d58-d708-4a4b-b255-cc8ef66a6b56 · outbound

This paper cites Enhancing IoT Container Scheduling in the Cloud with Multi-Objective Accelerated PSO,.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Enhancing IoT Container Scheduling in the Cloud with Multi-Objective Accelerated PSO,

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T05:40:19.687991Z digest=sha256:057af6bc3f2fe4f1b6318d9dc403c92a7d9e49f6f062a64c6a82cd78766ba534

Observation a104cb67-d821-43ca-9798-bc25e3ea9c66 · outbound

This paper cites Multi‐objective based container placement strategy in CaaS,.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Multi‐objective based container placement strategy in CaaS,

Reference 2

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raw_fallback, observed 2026-08-11T05:40:20.291993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T05:40:19.692964Z digest=sha256:171fd65bf982076c6f76ad5f93d60a2e6b466f3551dfcf8b1067ed1d42a66f21

Observation 5aaeaf3f-d6c0-4531-b5d9-ff531a9bc333 · outbound

This paper cites Optimized task scheduling approach with fault tolerant load balancing using multi-objective cat swarm optimization for multi-cloud environment,.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Optimized task scheduling approach with fault tolerant load balancing using multi-objective cat swarm optimization for multi-cloud environment,

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T05:40:19.697554Z digest=sha256:4cc5b29ce233b05e4c2563d4dfc56cbc36a0dffc1963d2910534e455e2605bd9

Observation 6aae1d8a-4040-454e-bd1f-c4bda86b8421 · outbound

This paper cites A multiobjective metaheuristic- based container consolidation model for cloud application performance improvement,.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments A multiobjective metaheuristic- based container consolidation model for cloud application performance improvement,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T05:40:19.702425Z digest=sha256:0c27f4ea9e7dc4a1b63da1a400c2429a90ad296bc1292dff6abdddb20b9c80de

Observation 5862092a-189c-45f2-a505-22e6b4de817b · outbound

This paper cites Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:40:19.707171Z digest=sha256:40b934a3b049e0bc7f9a42e8ccbc419534d2b36e1b59a15d020e897939623274

Observation b448ac90-9652-4a6d-89f4-2cca4da75414 · outbound

This paper cites Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification

Reference 6

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source=pdf_text observed=2026-08-11T05:40:19.712158Z digest=sha256:16277f0cfbfe2603ba70bf06d5f6eb9acf83e593ebc1ff2754039d38407fadda

Observation 7cb1ed80-5ba7-4ed6-8dc5-894369cd0a32 · outbound

This paper cites Self-Supervised Credit Scoring with Masked Autoencoders: Addressing Data Gaps and Noise Robustly,.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Self-Supervised Credit Scoring with Masked Autoencoders: Addressing Data Gaps and Noise Robustly,

Reference 7

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source=pdf_text observed=2026-08-11T05:40:19.717414Z digest=sha256:0377898acedc3dc1d9169c95ef8544b5528cf000d6856ae447fadf05fbaf2d66

Observation c5652c82-7568-4c31-a521-782dfcc55b81 · outbound

This paper cites Multi-Source Data-Driven LSTM Framework for Enhanced Stock Price Prediction and Volatility Analysis,.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Multi-Source Data-Driven LSTM Framework for Enhanced Stock Price Prediction and Volatility Analysis,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T05:40:19.721881Z digest=sha256:3cc2899d8af92a701ec15f84f8fd174391a37a08827e890d44eb4ab6f4732667

Observation 8a4fda39-d4ac-4cd6-9d50-374da3807795 · outbound

This paper cites Stock Type Prediction Model Based on Hierarchical Graph Neural Network.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Stock Type Prediction Model Based on Hierarchical Graph Neural Network

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:40:19.726266Z digest=sha256:99a9a17d3b9fd682eb65d92f1721fd14f810ed37b2e98e1895d8aa96da2794b7

Observation 0c2e36eb-14c0-4e96-8242-2f9e70d9fe1a · outbound

This paper cites Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:40:19.730904Z digest=sha256:ed95f7069af263b7eb18e74fd6e9838a15c715e44a47cec4fcfc026d58375140

Observation cb03bff2-2742-4e5f-9bf3-e9deb18e1726 · outbound

This paper cites Comprehensive Evaluation of Multimodal AI Models in Medical Imaging Diagnosis: From Data Augmentation to Preference-Based Comparison.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Comprehensive Evaluation of Multimodal AI Models in Medical Imaging Diagnosis: From Data Augmentation to Preference-Based Comparison

Reference 11

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source=pdf_text observed=2026-08-11T05:40:19.735567Z digest=sha256:f26f388b341fccfc0d74b984e1a5be0fd99dcae0a8e3340c6034869d520c8f7f

Observation e71263b7-5b49-4bbd-b4d6-91978a663552 · outbound

This paper cites Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:40:19.740543Z digest=sha256:b84526087a73eb089e135ccb7a29867635dfc8fd2148e279ca61eb96ad9ed886

Observation bf1a32a8-f443-4379-af17-cac89fa716f2 · outbound

This paper cites Breast cancer image classification method based on deep transfer learning,.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Breast cancer image classification method based on deep transfer learning,

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:40:19.745895Z digest=sha256:adea1fcde5ecfc07f9ddb5942659aeec6b65f1160de25ab19dfcd0611a7baa36

Observation 5a4a0298-8166-48a2-bf1d-9d281d453e1f · outbound

This paper cites A systematic review on recent methods of scheduling and load balancing for containers in distributed environments,.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments A systematic review on recent methods of scheduling and load balancing for containers in distributed environments,

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-11T05:40:20.211622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T05:40:19.749946Z digest=sha256:40b84ed02e3535a76a0a9678a06a2eb00f69acacecc239eeb407d8d59cf5bb73

Observation 144a799a-e9c7-469b-8f44-2286edc940e2 · outbound

This paper cites Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining

Reference 15

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source=pdf_text observed=2026-08-11T05:40:19.753974Z digest=sha256:fee0a6d79a8763d211953620e4839a21267768843c65d6a7af33bd777891abf5

Observation 3c39637e-b17b-496f-a845-184781d65f86 · outbound

This paper cites Self- Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Self- Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks

Reference 16

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no resolver link, observed 2026-08-11T05:40:19.758191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:40:19.758191Z digest=sha256:5fca674c0e8063bf22f9d6b9f8d9d5942382f0a58a9254db0f0b61b04dbfc4e1

Observation 12807b1b-ab13-452f-af40-1fdbfc092a27 · outbound

This paper cites Improving the RAG- based Personalized Discharge Care System by Introducing the Memory Mechanism.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Improving the RAG- based Personalized Discharge Care System by Introducing the Memory Mechanism

Reference 17

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source=pdf_text observed=2026-08-11T05:40:19.762335Z digest=sha256:2669fdfe1e0aebd44b8b87038b01c56cbe4ef393fcfc18ed87920f3c7fb05b92

Observation f903ea88-c39c-4f5f-8b16-21ee06ace20a · outbound

This paper cites Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches

Reference 18

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source=pdf_text observed=2026-08-11T05:40:19.766673Z digest=sha256:aa7063f82fb3e0ee07af96ed75cc0725028bf6d10cfbefc0d1c846efe70951db

Observation ba10ba14-915d-4d13-b3e2-8473f512b40e · outbound

This paper cites Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothing.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothing

Reference 19

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no resolver link, observed 2026-08-11T05:40:19.771118Z

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source=pdf_text observed=2026-08-11T05:40:19.771118Z digest=sha256:8eb452663eecc198b2be3c38e1567cc9a7b2d5aeaca6db1f8f1b8b850fcd0927

Observation fe9d9208-083c-4241-b9e1-48f962f34f2e · outbound

This paper cites Comparison of Tree-Based Feature Selection Algorithms on Biological Omics Dataset,.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Comparison of Tree-Based Feature Selection Algorithms on Biological Omics Dataset,

Reference 20

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source=pdf_text observed=2026-08-11T05:40:19.777098Z digest=sha256:04783d8306a310c06ab345b7d8f16cc781279ac677ba3e0db4857bfb5b203da6

Observation 094c24d3-8fa7-4fe4-bad0-b65108400239 · outbound

This paper cites Optimizing Multi-Task Learning for Enhanced Performance in Large Language Models.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Optimizing Multi-Task Learning for Enhanced Performance in Large Language Models

Reference 21

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:40:19.781886Z digest=sha256:5d57cd93297390ac0d8ca8b091478ae531ce75d3bccdb0b364a6f016d552412d

Observation e54c311d-f24b-4d45-9774-7d8be4e674c2 · outbound

This paper cites An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction

Reference 22

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source=pdf_text observed=2026-08-11T05:40:19.787105Z digest=sha256:e43f3454e6cf453a8fadb6acbb1e0e9ce41d529b6c0188ee93677c23a14bfbf4

Observation bab47f5c-88cd-4b73-9052-c8f38187db5f · outbound

This paper cites Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training

Reference 23

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source=pdf_text observed=2026-08-11T05:40:19.792263Z digest=sha256:4223e966da8589ab76428428a84ea4de856362d417267c846bc4e3e296b99717

Observation 2e126de8-bb1f-4890-b744-a31a4b62279a · outbound

This paper cites Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs

Reference 24

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:40:19.796870Z digest=sha256:2421f09121efe39776665e152da8e51199c86c3bdb57e61e05e64c0fe833b2a0

Observation 4c936a38-bb7f-42c9-a218-9b0fff6e8ded · outbound

This paper cites Robust Graph Neural Networks for Stability Analysis in Dynamic Networks.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Robust Graph Neural Networks for Stability Analysis in Dynamic Networks

Reference 25

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no resolver link, observed 2026-08-11T05:40:19.801436Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T05:40:19.801436Z digest=sha256:69bc7401e2cf3a6b8234a3ae38403f63fa5cbe138c0787ca5ffc74c303f4f2be

Observation a6a92116-c4cb-4715-a4dd-2d04c290b05f · outbound

This paper cites Optimizing Gesture Recognition for Seamless UI Interaction Using Convolutional Neural Networks.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Optimizing Gesture Recognition for Seamless UI Interaction Using Convolutional Neural Networks

Reference 26

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no resolver link, observed 2026-08-11T05:40:19.806017Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:40:19.806017Z digest=sha256:693707fcb163bde59c20ee79bfa254daa0ba001b613bb0bb2e14483de5346acd

Observation 9df75f46-213f-466d-9439-890e70088d32 · outbound

This paper cites An Cloud-Based Analysis- Efficient Scheduling,.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments An Cloud-Based Analysis- Efficient Scheduling,

Reference 27

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raw_fallback, observed 2026-08-11T05:40:20.177963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T05:40:19.811620Z digest=sha256:b7b01af0a8d434f4b853ca065c3a95ff2b202bc29559da3db14f10fb664109b6

Observation 8a3ba541-52a8-4a25-8eab-12bc2137fb29 · outbound

This paper cites Accurate Medical Named Entity Recognition Through Specialized NLP Models.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments Accurate Medical Named Entity Recognition Through Specialized NLP Models

Reference 28

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no resolver link, observed 2026-08-11T05:40:19.816325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:40:19.816325Z digest=sha256:62d1dd040b4eb2699051db512ef712f1077b13ccd5493c64786525974180ea0b

Observation 4927312d-1570-406c-8307-0ece869390b8 · outbound

This paper cites A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster,.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster,

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-11T05:40:20.162597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T05:40:19.821012Z digest=sha256:a6b0bed9da976232913417be9f44a4de99d2d1aabbf6538ff7d8fde90e7e748a

Pith citing papers

Observation 1feb927f-f337-4c3a-8126-f50ba0ec7817 · inbound

Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer cites this paper.

Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer Dynamic Scheduling Strategies for Resource Optimization in Computing Environments

Reference 10

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no resolver link, observed 2026-08-10T15:57:30.318383Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:57:30.318383Z digest=sha256:22fb5d3bf7337f96b251b50edaef22e0435f3af7b31caa786b9b72cc75e78e39

Observation 07d05ec1-7652-473f-a3e9-e9b14b7a75f1 · inbound

Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Models cites this paper.

Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Models Dynamic Scheduling Strategies for Resource Optimization in Computing Environments

Reference 10

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no resolver link, observed 2026-08-10T14:55:04.103879Z

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source=pdf_text observed=2026-08-10T14:55:04.103879Z digest=sha256:fafbbf09ea53ae270c49de684a5584b6ca36eeff41bbdc7e7d2cb6751e33b1e4

Observation 4b62e3fb-1ad9-4823-a083-ee191656eca4 · inbound

Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentation cites this paper.

Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentation Dynamic Scheduling Strategies for Resource Optimization in Computing Environments

Reference 14

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no resolver link, observed 2026-08-09T00:40:57.962253Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T00:40:57.962253Z digest=sha256:07967412b65b00f72738498cbf6890451004104315fd5c95e5417bfa1792aecd

Observation 74c85b65-108f-428b-9992-da854289dcc2 · inbound

Multi-Scale Transformer Architecture for Accurate Medical Image Classification cites this paper.

Multi-Scale Transformer Architecture for Accurate Medical Image Classification Dynamic Scheduling Strategies for Resource Optimization in Computing Environments

Reference 3

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local_arxiv, observed 2026-08-08T16:19:18.892731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-08T16:19:18.427629Z digest=sha256:46dd7366396eae55d3323c03f59916d2d950529d49f41b30710b41894abeed99

Observation a55b6a00-a881-4167-95b2-726bee864c82 · inbound

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation cites this paper.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation Dynamic Scheduling Strategies for Resource Optimization in Computing Environments

Reference 13

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:12:55.707793Z digest=sha256:2a530f17d3a2dc307af59c944f4ff019d466c3153fa7635fe7e9b0af01c7b7a0