Institution profile

University of Oxford

Academic institutioneurope · gb
Official website
Research library2,882linked papers
Opportunities0open roles
Selected work

Representative Papers

Manufacturing Revolutions: Industrial Policy and Industrialization in South Korea

Jun 30, 2021Social Science Research Network

This study investigates the causal impact of South Korea’s Heavy and Chemical Industry (HCI) policy (1973–1979) on manufacturing upgrading. Exploiting the exogenous policy discontinuity as a quasi-natural experiment, the authors construct a novel industry-level panel dataset on policy exposure and performance by integrating input-output network analysis to quantify both direct and indirect intersectoral spillovers. Results show that the HCI policy significantly expanded the scale and export capacity of targeted industries and catalyzed downstream upgrading through supply-chain linkages; these effects persisted post-policy termination, facilitating a structural shift toward high-value-added manufacturing. The key contribution lies in pioneering the integration of regression discontinuity design with input-output–based industrial linkage analysis—demonstrating that short-term industrial policies can generate enduring dynamic comparative advantages and coordinated upgrading pathways across interconnected sectors.

67 citations5 influentialRead paper

Nuclear norm regularized estimation of panel regression models

Oct 25, 2018

This paper addresses identification and estimation challenges in interactive fixed-effects panel regressions arising from low-rank covariates and unknown factor dimensions. We propose two convex optimization estimators based on nuclear-norm (trace-norm) regularization and minimization. Unlike conventional non-convex least squares methods—which suffer from local optima and require pre-specified factor numbers—our approach guarantees global optimality, automatically accommodates low-rank structure, and handles unknown factor dimensions. By employing iterative weighted least squares, we construct a convex algorithm asymptotically equivalent to the LS estimators of Bai (2009) and Moon–Weidner (2017). We establish consistency and asymptotic efficiency of the estimators, while significantly reducing computational complexity. Empirically, the method robustly avoids local optima. This work constitutes the first systematic application of nuclear-norm convex optimization to interactive-effect panel estimation, achieving both statistical efficiency and computational feasibility.

48 citations9 influentialRead paper

Reading to Listen at the Cocktail Party: Multi-Modal Speech Separation

Jun 01, 2022Computer Vision and Pattern Recognition

This work addresses speech separation and enhancement under challenging conditions involving multi-speaker overlap and background noise. We propose an end-to-end waveform-domain multimodal approach that fuses asynchronous/synchronous visual, auditory, and textual modalities. Our key contributions are: (1) the first integration of textual semantics—either as an independent or joint conditioning signal—into speech separation; (2) a cross-modal fusion framework robust to audio-visual temporal misalignment; and (3) a unified Transformer-based architecture modeling multimodal temporal dynamics, coupled with a waveform-domain time-domain convolutional encoder-decoder and fine-grained feature alignment. Evaluated on the LRS2 and LRS3 benchmarks, our method achieves state-of-the-art performance, significantly improving separation quality and speech intelligibility—particularly in noisy environments and under lip-audio desynchronization.

23 citations3 influentialRead paper

Challenges for artificial cognitive systems

Dec 31, 2012

The field of artificial cognitive systems lacks a systematic, quantifiable evaluation framework and clearly defined, assessable research objectives. Method: This work establishes an interdisciplinary, measurable challenge framework—integrating cognitive science, artificial intelligence, and systems theory—to concretize abstract cognitive capabilities into empirically testable benchmarks. Contribution/Results: It introduces seven empirically verifiable challenges: autonomous goal generation, multimodal situational understanding, continual lifelong learning, metacognitive regulation, causal reasoning, embodied interaction adaptability, and cross-task knowledge transfer. Designed to be algorithm- and model-agnostic, the framework adopts a capability-oriented evaluation paradigm. It has been adopted as a foundational guideline by major international initiatives—including the EU’s EUCog—and provides a unified metric for tracking progress and fostering coordinated research advancement in artificial cognitive systems.

13 citationsRead paper

On the Decidability of Monadic Second-Order Logic with Arithmetic Predicates

May 13, 2024Logic in Computer Science

This work addresses the decidability of monadic second-order (MSO) logic over the natural number structure ⟨ℕ; <, P₁,…,P_d⟩, where each P_i is a canonical arithmetic predicate (e.g., powers of k, perfect k-th powers ℕ^k, or the Fibonacci sequence Fib). We develop an interdisciplinary decidability framework integrating symbolic dynamical systems, transcendental number theory (including the Schanuel Conjecture), finite automata theory, and logical analysis. Unconditionally—i.e., without unproven hypotheses—we establish MSO decidability for key structures such as (ℕ; <, Pow2, Fib) and (ℕ; <, Pow2, Pow3, Pow6). We further prove Turing equivalence between the MSO theory of (ℕ; <, Pow2, ℕ²) and that of binary normal numbers. Crucially, our approach uncovers deep connections between combinatorial encoding properties of arithmetic predicates and their representability by finite automata, yielding a systematic methodological advance for decidability research at the interface of logic and number theory.

7 citations1 influentialRead paper
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