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Articles by Chi Jin
ISGylation promotes ALDOA stability and PKM2-dependent activation to fuel colorectal cancer metabolism
Abstract ISG15 overexpression in cancers has emerged as a diagnostic marker and therapeutic target. This study identifies ISGylation, a post-translational modification mediated by ISG15, as a critical driver of CRC progression through metabolic reprogramming and oncogenic signaling. Clinical and multi-omics analyses reveal elevated ISG15 and ISGylation levels in CRC tissues, correlating with advanced clinicopathological features and poor prognosis.
Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration
[Submitted on 12 Sep 2025] Title:Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration View a PDF of the paper titled Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration, by Ahmed Khaled and Satyen Kale and Arthur Douillard and Chi Jin and Rob Fergus and Manzil Zaheer View PDF HTML (experimental) Abstract:Modern machine learning often requires training with large batch size, distributed data, and massively parallel compute...
Leave or Stay? Impact of City Attribute Perception and City Image on University Student Stay Intention
CONFLICT OF INTEREST None. References & (2011), The Role of Colleges and Universities in Building Local Human Capital. Current Issues in Economics and Finance 17(6), pp. 1–7. , & (2014), Market Thickness and the Early Labour Market Career of University Graduates: An City Advantage? Spatial Economic Analysis 9(4), pp. 396–419. (2015), Smart Cities: A Conjuncture of Four Forces. Cities 47, pp. 95–106. (2007), Going Global?
[2507.07313] Frontier LLMs Still Struggle with Simple Reasoning Tasks
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PokéChamp: an Expert-level Minimax Language Agent
Papers Published on Mar 6 · Submitted by akhaliq on Mar 7 Abstract We introduce Pok\'eChamp, a minimax agent powered by Large Language Models (LLMs) for Pok\'emon battles. Built on a general framework for two-player competitive games, Pok\'eChamp leverages the generalist capabilities of LLMs to enhance minimax tree search.
PJA2 Suppresses Colorectal Cancer Progression by Controlling HDAC2 Degradation and Stability
1 Introduction CRC ranks as the third most prevalent malignancy and the third leading cause of cancer-related deaths in both males and females, according to the latest statistics.[1] Despite advancements in surgical interventions and chemotherapy, the persistently high global mortality and morbidity rates continue to burden millions of CRC patients.
The Uridylyl Transferase TUT7‐Mediated Accumulation of Exosomal miR‐1246 Reprograms TAMs to Support CRC Progression
1 Introduction Colorectal cancer (CRC) remains one of the most common malignant tumors in China, with increased incidence in recent years.[1] Surgical intervention is currently the main therapeutic approach for CRC, yet for patients with advanced or metastatic cases, it often fails to yield satisfactory outcomes,[2, 3] since as many as 30% of patients experience a recurrence or metastasis in the long term after surgical treatment.[4] Recently, immunotherapy has emerged as a promising...
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
arXiv:2305.16284 (cs) [Submitted on 25 May 2023 (v1), last revised 30 Jan 2024 (this version, v4)] Download PDF HTML (experimental) Comments: Subjects: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML) Cite as: arXiv:2305.16284 [cs.LG] (or arXiv:2305.16284v4 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2305.16284 Submission history From: Ahmed Khaled [ view email] [v1] Thu, 25 May 2023 17:40:43 UTC (303 KB) [v2] Sun, 29 Oct 2023 19:36:37 UTC...
[2209.02257] Faster federated optimization under second-order similarity
arXiv:2209.02257 (cs) [Submitted on 6 Sep 2022 (v1), last revised 23 May 2023 (this version, v2)] Download a PDF of the paper titled Faster federated optimization under second-order similarity, by Ahmed Khaled and Chi Jin Download PDF Submission history From: Ahmed Khaled [ view email] [v1] Tue, 6 Sep 2022 07:09:21 UTC (104 KB) [v2] Tue, 23 May 2023 02:04:17 UTC (489 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?)...
Studying the mechanism underlying lipid metabolism in osteosarcoma based on transcriptomic RNA sequencing and single‐cell data
Background We aimed to provide a new typing method for osteosarcoma (OS) based on single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the perspective of lipid metabolism and examine its potential mechanisms in the onset and progression of OS. Methods Scores for six lipid metabolic pathways were calculated by single-sample gene set enrichment analysis (ssGSEA) based on a scRNA-seq dataset and three microarray expression profiles.
[2011.00583] Game-Theoretic Multiagent Reinforcement Learning
arXiv:2011.00583 (cs) [Submitted on 1 Nov 2020 (v1), last revised 13 Aug 2025 (this version, v5)] View PDF HTML (experimental) Subjects: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI) Cite as: arXiv:2011.00583 [cs.MA] (or arXiv:2011.00583v5 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2011.00583 Submission history From: Dr. Yaodong Yang [ view email] [v1] Sun, 1 Nov 2020 18:24:40 UTC (12,830 KB) [v2] Thu, 3 Dec 2020 17:50:27 UTC (12,831 KB) [v3] Thu, 18 Mar 2021...
Provable Meta-Learning of Linear Representations
Meta-learning, or learning-to-learn, seeks to design algorithms that can utilize previous experience to rapidly learn new skills or adapt to new environments. Representation learning—a key tool for performing meta-learning—learns a data representation that can transfer knowledge across multiple tasks, which is essential in regimes where data is scarce.
[1906.00331] On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
arXiv:1906.00331 (cs) [Submitted on 2 Jun 2019 (v1), last revised 14 Sep 2023 (this version, v9)] Download PDF Comments: Subjects: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML) Cite as: arXiv:1906.00331 [cs.LG] (or arXiv:1906.00331v9 [cs.LG] for this version) https://doi.org/10.48550/arXiv.1906.00331 Submission history From: Tianyi Lin [ view email] [v1] Sun, 2 Jun 2019 03:03:45 UTC (176 KB) [v2] Thu, 17 Oct 2019 06:25:26 UTC (495 KB) [v3] Sun, 10...
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