arXiv subjects

Taewan Kim

Publications and source records attributed to Taewan Kim.

At least 19 recordsLinked to original sources

arXiv2026

Face and Voice Cross-modal Association with Learning Convex Feature Embedding

Face-and-voice association learning is one of the most challenging tasks in deep learning. In this paper, we propose a simple but powerful cross-modal feature embedding method for the association of faces and voices. Previous work has studied cross-modal association tasks to establish the correlation between voice clips and facial images. These works have addressed cross-modal discrimination but underestimate the importance of handling heterogeneity in inter-modal features between audio and video, resulting in a lot of false positives and false negatives. To tackle the problem, the proposed method learns the embeddings of cross-modal features by making another feature exist between cross-modal features, facilitating the voice and face features of the same person to be embedded in a convex hull. Moreover, the incorporation of cross-modal attention mechanisms with convex embedding techniques represents a highly effective strategy for the attenuation of false positives and false negatives, accomplished via the minimization of inter-class discrepancies. We exhaustively evaluated our method for cross-modal verification, matching, and retrieval tasks on the large-scale VoxCeleb dataset. Extensive experimental results demonstrate that the proposed method achieves notable improvements over existing state-of-the-art methods.

cs.CV↗

arXiv2026

Convolutional Neural Shading for High-Quality 3D Reconstruction from Multi-View Images

We propose a convolutional neural shading (CNS), a novel pipeline to reconstruct high-quality 3D shapes from multi-view images. Several recent studies have used neural radiance fields and other neural differentiable rendering methods to understand 3D geometry. However, these approaches rely on single-point geometric information, such as positions and normals of the surface, leading to a lack of detailed local geometry. Our approach addresses the inherent limitations of single-point information by leveraging a neural shader to capture variations even in dark and textureless regions with a convolutional neural shader, resulting in far more accurate geometry predictions. Additionally, our method mitigates surface irregularities at image boundaries by introducing a fine-detail displacement network, which utilizes spatial information of surface geometry and learns fine displacement details by correlating neighboring values in the rendering coordinates. Through extensive experiments, our proposed method has demonstrated significant quality improvements in the reconstructed shapes and rendered images over current state-of-the-art methods.

cs.CV↗

arXiv2026

Collaborative Feature Aggregation for Face Super-Resolution and Robust Re-Identification

We propose a novel collaborative approach for face super-resolution (SR) and robust person re-identification from sequential or multi-view facial images. Traditional SR methods often suffer from blurring and distortion in faces recovered from poor-quality images due to low resolution. Image- and video-based facial SR methods using facial landmarks or segmentation also have similar challenges. To overcome these limitations, we leverage multiple correlated facial observations, across time or viewpoints, by introducing a transformer-based collaborative feature aggregation method that unifies identity features from multi-sequence or multi-view data. This allows faces in multiple sequences of an individual to contribute to accurately estimating common facial features. Furthermore, we propose a cascade SR network to progressively restore the high-resolution image of the target's face with gradual facial feature unification. The unified identity representation is further utilized in person re-identification scenarios, enabling accurate matching even under severe image degradation. The exhaustive experimental results and comparisons show that our method outperforms other state-of-the-art methods, demonstrating consistent improvements in both face super-resolution and re-identification performance. Our work highlights the effectiveness of joint identity reconstruction and progressive image restoration from multiple facial inputs in enhancing downstream visual recognition tasks.

cs.CV↗

arXiv2026

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding

We study whether quantum learning advantage can persist without fully embedding a large classical input into a highly superposed quantum state. To address this question, we introduce active quantum subspace data-encoding, in which only an information-bearing subset of the input is lifted to a quantum representation while the remaining variables stay classical. For this model, we define a projected hybrid readout and prove three structural results. First, the projected hybrid kernel is positive semidefinite and its sample regularized dimension is bounded by the number of projected observables, so the dimension blow-up of naive global kernels is avoided. Second, we give a necessary and sufficient criterion for improvement over a purely classical predictor in squared loss: the projected quantum sector must contain a direction that lies outside the classical feature span and correlates with the classical residual. Third, in a realizable noisy-oracle setting, we derive a PAC sample-complexity bound proportional to the inverse square of the oracle reliability. We then show, for a canonical Clifford active-subspace family under local dephasing noise, that this reliability can remain inverse-polynomial even when the encoding gate complexity grows polynomially with system size. Hence, the polynomial encoding cost does not by itself destroy the hybrid learning advantage. A sixty-four-qubit family and a synthetic contextual classification task illustrate how one projected quantum feature can compress a useful high-order interaction into a low-dimensional hybrid model. Our results generalize QRAM-free hybrid learning and provide a scalable route toward NISQ-compatible quantum advantage without full quantum data-encoding.

quant-ph↗

arXiv2026

On the cohomology of negative Tate twists via cyclotomic descent

We show that the Galois cohomology of negative Tate twists can be organized by a single universal cyclotomic complex over the cyclotomic tower of $\mathbb{Q}$. Using cyclotomic descent and Teichm\"uller branch decomposition, we prove that a negative twist contributes only on the corresponding branch and is recovered by specializing the Iwasawa variable at a single point; equivalently, it is computed as the fiber of $\gamma-u^{-m}$, or $T=u^{-m}-1$ in Iwasawa coordinates. In the case $\mathbb{Q}_p/\mathbb{Z}_p$, this gives explicit descriptions of $H^1$ and $H^2$ in terms of the quotient and torsion of the $S$-ramified Iwasawa module.

math.NT↗

arXiv2026

Three-Qubit State Preparation: Classification and Explicit Circuits

We present a deterministic framework for preparing an arbitrary three-qubit pure state. To leverage entanglement structure in the state-preparation task, we classify three-qubit pure states into five types with respect to a $1|2$ bipartition. Given a target state specified by its amplitudes, we provide concrete criteria and concurrence-based tests that determine its type. For each type, we derive an explicit circuit template composed of elementary single-qubit rotations and CNOT gates, with gate parameters determined systematically from the Schmidt decomposition. The full construction is described step by step from the target amplitudes, with no procedural ambiguity. As an application, we further group frequently encountered three-qubit pure states in quantum information into four classes and provide an explicit circuit for each class. Compared with prior approaches, our circuits are designed for practical use: they admit a direct algorithmic instantiation, use only CNOT gates between adjacent qubits, and for certain classes achieve smaller gate counts and circuit depth.

quant-ph↗

arXiv2025

The Redshifts from 122 Bands: Comparative Redshift Forecast for Low-Resolution Spectra from SPHEREx and 7-Dimensional Sky Survey (7DS)

The recently initiated SPHEREx and 7DS surveys will deliver low-resolution spectra ($R\approx 30-130$) for hundreds of millions of galaxies over the optical to near-infrared range ($0.4-5.0\mu m$), covering a wide sky area without sample selection. These unique datasets will improve redshift estimation and provide a rich redshift catalog for the community. In this study, we forecast the performance of widely-used photometric redshift estimation methods using simulated SPHEREx and 7DS data. Four template-fitting approaches and two machine-learning (ML) methods are used to derive photometric redshifts from low-resolution spectrophotometric data. We measure redshifts using mock catalogs based on the GAMA and COSMOS galaxy samples and achieve high precision for bright (13 < i < 18) galaxies, with $\sigma_{NMAD}\lesssim 0.005$, bias $\lesssim 0.005$, and a catastrophic failure rate $\lesssim 0.005$ for all methods employed. We find that the combined SPHEREx + 7DS dataset significantly improves redshift estimation compared to using either the SPHEREx or 7DS datasets alone, highlighting the synergy between the two surveys. Moreover, we compare the redshift estimation performance across magnitude ranges for the different methods and examine the probability distribution functions (PDFs) produced by the template-fitting approaches. As a result, we identify some factors that can affect the redshift measurements, like treatments on dust extinction or inclusion of flux uncertainty in the ML model. We also show that the PDFs are relatively well calibrated, although the confidence intervals are generally underestimated, particularly for bright galaxies in the template-fitting methods. This study demonstrates the strong potential of SPHEREx and 7DS to deliver improved redshift measurements from low-resolution spectrophotometric data, underscoring the scientific value of jointly utilizing both datasets.

astro-ph.GA↗

arXiv2025

Continuous-time Constrained Funnel Synthesis for Incrementally Quadratic Nonlinear Systems

This paper presents a convex optimization-based framework for synthesizing time-varying controlled invariant funnels and associated feedback control around a given nominal trajectory for nonlinear systems subject to bounded disturbances. Nonlinearities are modeled using incremental quadratic constraints, including Lipschitz, L-smooth, and sector-bounded nonlinearities. Funnel invariance is ensured via a DLMI. Together with pointwise-in-time LMIs for state and input constraints, we formulate a continuous-time funnel synthesis problem. To solve it using numerical optimal control techniques, the DLMI is reformulated into a differential matrix equality (DME) and an LMI, where the DME acts as a funnel dynamics equation. We explore different formulations of these funnel dynamics. Continuous-time constraint satisfaction is addressed through two convex methods: one based on intermediate constraint-checking points, and another using a successive convexification method with subgradients to handle nondifferentiable maximum eigenvalue functions. Theoretical justification is provided for the existence of a measurable and integrable subgradient for the latter. The method is demonstrated on two numerical examples: the control of a unicycle and a 6-degree-of-freedom quadrotor for obstacle avoidance.

math.OC↗

arXiv2025

From Generation to Attribution: Music AI Agent Architectures for the Post-Streaming Era

Generative AI is reshaping music creation, but its rapid growth exposes structural gaps in attribution, rights management, and economic models. Unlike past media shifts, from live performance to recordings, downloads, and streaming, AI transforms the entire lifecycle of music, collapsing boundaries between creation, distribution, and monetization. However, existing streaming systems, with opaque and concentrated royalty flows, are ill-equipped to handle the scale and complexity of AI-driven production. We propose a content-based Music AI Agent architecture that embeds attribution directly into the creative workflow through block-level retrieval and agentic orchestration. Designed for iterative, session-based interaction, the system organizes music into granular components (Blocks) stored in BlockDB; each use triggers an Attribution Layer event for transparent provenance and real-time settlement. This framework reframes AI from a generative tool into infrastructure for a Fair AI Media Platform. By enabling fine-grained attribution, equitable compensation, and participatory engagement, it points toward a post-streaming paradigm where music functions not as a static catalog but as a collaborative and adaptive ecosystem.

cs.IR↗

arXiv2025

Onboard Dual Quaternion Guidance for Rocket Landing

The dual quaternion guidance (DQG) algorithm was selected as the candidate 6-DoF powered-descent guidance algorithm for NASA's Safe and Precise Landing -- Integrated Capabilities Evolution (SPLICE) project. DQG is capable of handling state-triggered constraints that are of utmost importance in terms of enabling technologies such as terrain relative navigation. In this work, we develop a custom solver for DQG to enable onboard implementation for future rocket landing missions. We describe the design and implementation of a real-time-capable optimization framework, called sequential conic optimization (SeCO), that blends together sequential convex programming and first-order conic optimization to solve difficult nonconvex trajectory optimization problems, such as DQG, in real-time. A key feature of SeCO is that it leverages a first-order primal-dual conic optimization solver, based on the proportional-integral projected gradient method (PIPG). We describe the implementation of this solver, develop customizable first-order methods, and leverage convergence-accelerating strategies such as warm-starting and extrapolation, to solve the nonconvex DQG optimal control problem in real-time. Finally, in preparation for an upcoming closed-loop flight test campaign, we test our custom solver onboard the NASA SPLICE Descent and Landing Computer in a hardware-in-the-loop setting. We observe that our algorithm is significantly faster than previously reported solve-times using the flight-tested interior point method-based subproblem solver, BSOCP. Furthermore, our custom solver meets (and exceeds) NASA's autonomous precision rocket-landing guidance update-rate requirements for the first time, thus demonstrating the viability of SeCO for real-time, mission-critical applications onboard computationally-constrained flight hardware.

math.OC↗

arXiv2025

AllBRICQS: The Discovery of Luminous Quasars in the Northern Hemisphere

We present the second catalog of bright quasars from the All-sky BRIght, Complete Quasar Survey (AllBRICQS), focusing on spectroscopically observed quasars in the Northern Hemisphere with Galactic latitude $|b| > 10^\circ$. This catalog includes their spectral data, redshifts, and luminosities. AllBRICQS aims to identify the last remaining optically bright quasars using data from the Wide-field Infrared Survey Explorer (WISE) and Gaia all-sky survey Data Release 3 (DR3). AllBRICQS searches for quasars that are brighter than $B_P = 16.5$ or $R_P = 16$ mag in Gaia DR3, based on simple selection criteria. Here, we report 62 new AllBRICQS quasars spanning various types, which include typical broad emission line quasars and the most luminous iron low-ionization broad absorption line quasars discovered to date. Spectroscopic observations were conducted using the Long-Slit Spectrograph on the 1.8-meter telescope at Bohyunsan Optical Astronomy Observatory, YFOSC on the 2.4-meter telescope at Lijiang Observatory, and BFOSC on the 2.16-meter telescope at Xinglong Observatory. We applied flux calibration using ZTF broadband photometry to correct for attenuation due to intermittent thin clouds during the observations. Redshifts were determined using inverse-variance weighted cross-correlation methods. Our targets span the bolometric luminosity range of $44.9<\log \left( L_{\rm bol} / {\rm erg~s^{-1}} \right)<48.0$ at redshifts between 0.09 and 2.48. These confirmed AllBRICQS quasars provide a valuable resource for future research into quasar evolution, black holes, their environments, and their host galaxies across multiple wavelengths.

astro-ph.GA↗

arXiv2025

RVSNUpy: A Python Package for Spectroscopic Redshift Measurement Based on Cross-Correlation

We introduce RVSNUpy, a new Python package designed to measure spectroscopic redshifts. Based on inverse-variance weighted cross-correlation, RVSNUpy determines the redshifts by comparing observed spectra with various rest-frame template spectra. We test the performance of RVSNUpy based on ~ 6000 objects in the HectoMAP redshift survey observed with both SDSS and MMT/Hectospec. We demonstrate that a slight redshift offset (~ 40 km/s) between SDSS and MMT/Hectospec measurements reported from previous studies results from the small offsets in the redshift template spectra used for SDSS and Hectospec reductions. We construct the universal set of template spectra, including empirical SDSS template spectra, carefully calibrated to the rest frame. Our test for the HectoMAP objects with duplicated observations shows that RVSNUpy with the universal template spectra yields the homogeneous redshift from the spectra obtained with different spectrographs. We highlight that RVSNUpy is a powerful redshift measurement tool for current and future large-scale spectroscopy surveys, including A-SPEC, DESI, 4MOST, and Subaru/PFS.

astro-ph.CO↗

arXiv2025

Predicting Chemical Reaction Outcomes Based on Electron Movements Using Machine Learning

Accurately predicting chemical reaction outcomes and potential byproducts is a fundamental task of modern chemistry, enabling the efficient design of synthetic pathways and driving progress in chemical science. Reaction mechanism, which tracks electron movements during chemical reactions, is critical for understanding reaction kinetics and identifying unexpected products. Here, we present Reactron, the first electron-based machine learning model for general reaction prediction. Reactron integrates electron movement into its predictions, generating detailed arrow-pushing diagrams that elucidate each mechanistic step leading to product formation. We demonstrate the high predictive performance of Reactron over existing product-only models by a large-scale reaction outcome prediction benchmark, and the adaptability of the model to learn new reactivity upon providing a few examples. Furthermore, it explores combinatorial reaction spaces, uncovering novel reactivities beyond its training data. With robust performance in both in- and out-of-distribution predictions, Reactron embodies human-like reasoning in chemistry and opens new frontiers in reaction discovery and synthesis design.

physics.chem-ph↗

arXiv2025

Machine learning based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide field: catalogs of spectroscopic and photometric redshifts

We perform an MMT/Hectospec redshift survey of the North Ecliptic Pole Wide (NEPW) field covering 5.4 square degrees, and use it to estimate the photometric redshifts for the sources without spectroscopic redshifts. By combining 2572 newly measured redshifts from our survey with existing data from the literature, we create a large sample of 4421 galaxies with spectroscopic redshifts in the NEPW field. Using this sample, we estimate photometric redshifts of 77755 sources in the band-merged catalog of the NEPW field with a random forest model. The estimated photometric redshifts are generally consistent with the spectroscopic redshifts, with a dispersion of 0.028, an outlier fraction of 7.3%, and a bias of -0.01. We find that the standard deviation of the prediction from each decision tree in the random forest model can be used to infer the fraction of catastrophic outliers and the measurement uncertainties. We test various combinations of input observables, including colors and magnitude uncertainties, and find that the details of these various combinations do not change the prediction accuracy much. As a result, we provide a catalog of 77755 sources in the NEPW field, which includes both spectroscopic and photometric redshifts up to z~2. This dataset has significant legacy value for studies in the NEPW region, especially with upcoming space missions such as JWST, Euclid, and SPHEREx.

astro-ph.CO↗

arXiv2024

Auto-tuned Primal-dual Successive Convexification for Hypersonic Reentry Guidance

This paper presents auto-tuned primal-dual successive convexification (Auto-SCvx), an algorithm designed to reliably achieve dynamically-feasible trajectory solutions for constrained hypersonic reentry optimal control problems across a large mission parameter space. In Auto-SCvx, we solve a sequence of convex subproblems until convergence to a solution of the original nonconvex problem. This method iteratively optimizes dual variables in closed-form in order to update the penalty hyperparameters used in the primal variable updates. A benefit of this method is that it is auto-tuning, and requires no hand-tuning by the user with respect to the constraint penalty weights. Several example hypersonic reentry problems are posed and solved using this method, and comparative studies are conducted against current methods. In these numerical studies, our algorithm demonstrates equal and often improved performance while not requiring hand-tuning of penalty hyperparameters.

math.OC↗

arXiv2024

Quantum superposing algorithm for quantum encoding

Efficient encoding of classical data into quantum state -- currently referred to as quantum encoding -- holds crucial significance in quantum computation. For finite-size databases and qubit registers, a common strategy of the quantum encoding entails establishing a classical mapping that correlates machine-recognizable data addresses with qubit indices that are subsequently superposed. Herein, the most imperative lies in casting an algorithm for generating the superposition of any given number of qubit indices. This algorithm is formally known as quantum superposing algorithm. In this work, we present an efficient quantum superposing algorithm, affirming its effectiveness and superior computational performance in a practical quantum encoding scenario. Our theoretical and numerical analyses demonstrate a substantial enhancement in computational efficiency compared to existing algorithms. Notably, our algorithm has a maximum of 2n-3 controlled-not (CNOT) counts, representing the most optimized result to date.

quant-ph↗

arXiv2024

Relative-Hyper GAGA Theorem

In this paper, we provide a relative hypercohomology version of Serre's GAGA theorem. We prove that the relative hypercohomology of a complex of sheaves on a complex projective variety is isomorphic to the relative hypercohomology of its analytification, with respect to an open or closed subvariety. This result implies Serre's original GAGA theorem.

math.AG↗

arXiv2024

Six-Degree-of-Freedom Aircraft Landing Trajectory Planning with Runway Alignment

This paper presents a numerical optimization algorithm for generating approach and landing trajectories for a six-degree-of-freedom (6-DoF) aircraft. We improve on the existing research on aircraft landing trajectory generation by formulating the trajectory optimization problem with additional real-world operational constraints, including 6-DoF aircraft dynamics, runway alignment, constant wind field, and obstacle avoidance, to obtain a continuous-time nonconvex optimal control problem. Particularly, the runway alignment constraint enforces the trajectory of the aircraft to be aligned with the runway only during the final approach phase. This is a novel feature that is essential for preventing an approach that is either too steep or too shallow. The proposed method models the runway alignment constraint through a multi-phase trajectory planning scheme, imposing alignment conditions exclusively during the final approach phase. We compare this formulation with the existing state-triggered constraint formulation for runway alignment. To solve the formulated problem, we design a novel sequential convex programming algorithm called xPTR that extends the penalized trust-region (PTR) algorithm by incorporating an extrapolation step to expedite convergence. We validate the proposed method through extensive numerical simulations, including a Monte Carlo study, to evaluate the robustness of the algorithm to varying initial conditions.

math.OC↗