BZPEERPapers, people, evidence and research paths
Menu

Discover

SearchFind papers, people, topics, institutions and opportunities from one place.GraphSee how papers, authors, topics and evidence connect.

Research tools

HomeContinue from the papers, people and topics that matter now.CollectionsTurn saved papers into reading lists, evidence maps and shareable context.ResearchReconstruct the path that led you from search to paper to question.CollaborationMove from a paper, topic or expert request into a focused thread.PublishCreate full-text research articles with metadata, formulas and versions.

Network

ExpertsFind specialists whose work and reviews explain why they can help.CommunitiesJoin research groups organized around papers, topics and shared evidence.

Opportunities

ListingsBrowse roles, calls and collaborations connected to real research context.

Account

Log inReturn to your saved papers, graph views and active threads.Create accountStart saving papers, building a research trail and joining teams.
Log inCreate account

Source author record

Youngwook Kim

Youngwook Kim appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
cond-mat.mes-hallcond-mat.mtrl-sciArtificial IntelligenceMachine LearningComputation and LanguageComputer Visioncond-mat.str-elquant-phphysics.ins-det

Catalog footprint

What is connected

15works
9topics
4close collaborators

Actions

Connect this record

Log in to claim
Open graphBrowse works

Research graph

See the researcher in context

Open full explorer

Inspect adjacent papers, topics, institutions and collaborators without losing the researcher page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

15 published item(s)

preprint2026arXiv

FreeTimeGS++: Secrets of Dynamic Gaussian Splatting and Their Principles

The recent surge in 4D Gaussian Splatting (4DGS) has achieved impressive dynamic scene reconstruction. While these methods demonstrate remarkable performance, the specific drivers behind such gains remain less explored, making a systematic understanding of the underlying principles challenging. In this paper, we perform a comprehensive analysis of these hidden factors to provide a clearer perspective on the 4DGS framework. We first establish a controlled baseline, FreeTimeGS_ours, by formalizing and reproducing the heuristics of the state-of-the-art FreeTimeGS. Using this framework, we dissect 4DGS along its fundamental axes and uncover key secrets, including the emergent temporal partitioning driven by Gaussian durations and the discrepancy between photometric fidelity and spatiotemporal consistency. Based on these insights, we propose FreeTimeGS++, a principled method that employs gated marginalization and neural velocity fields to achieve superior stability and robust dynamic representations. Our approach yields reproducible results with reduced run-to-run variance. We will release our implementation to provide a reliable foundation for future 4DGS research.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dual Task Framework for Improving Persona-grounded Dialogue Dataset

This paper introduces a simple yet effective data-centric approach for the task of improving persona-conditioned dialogue agents. Prior model-centric approaches unquestioningly depend on the raw crowdsourced benchmark datasets such as Persona-Chat. In contrast, we aim to fix annotation artifacts in benchmarking, which is orthogonally applicable to any dialogue model. Specifically, we augment relevant personas to improve dialogue dataset/agent, by leveraging the primal-dual structure of the two tasks, predicting dialogue responses and personas based on each other. Experiments on Persona-Chat show that our approach outperforms pre-trained LMs by an 11.7 point gain in terms of accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Large Loss Matters in Weakly Supervised Multi-Label Classification

Weakly supervised multi-label classification (WSML) task, which is to learn a multi-label classification using partially observed labels per image, is becoming increasingly important due to its huge annotation cost. In this work, we first regard unobserved labels as negative labels, casting the WSML task into noisy multi-label classification. From this point of view, we empirically observe that memorization effect, which was first discovered in a noisy multi-class setting, also occurs in a multi-label setting. That is, the model first learns the representation of clean labels, and then starts memorizing noisy labels. Based on this finding, we propose novel methods for WSML which reject or correct the large loss samples to prevent model from memorizing the noisy label. Without heavy and complex components, our proposed methods outperform previous state-of-the-art WSML methods on several partial label settings including Pascal VOC 2012, MS COCO, NUSWIDE, CUB, and OpenImages V3 datasets. Various analysis also show that our methodology actually works well, validating that treating large loss properly matters in a weakly supervised multi-label classification. Our code is available at https://github.com/snucml/LargeLossMatters.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning

Commonsense reasoning systems should be able to generalize to diverse reasoning cases. However, most state-of-the-art approaches depend on expensive data annotations and overfit to a specific benchmark without learning how to perform general semantic reasoning. To overcome these drawbacks, zero-shot QA systems have shown promise as a robust learning scheme by transforming a commonsense knowledge graph (KG) into synthetic QA-form samples for model training. Considering the increasing type of different commonsense KGs, this paper aims to extend the zero-shot transfer learning scenario into multiple-source settings, where different KGs can be utilized synergetically. Towards this goal, we propose to mitigate the loss of knowledge from the interference among the different knowledge sources, by developing a modular variant of the knowledge aggregation as a new zero-shot commonsense reasoning framework. Results on five commonsense reasoning benchmarks demonstrate the efficacy of our framework, improving the performance with multiple KGs.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Preparation of large Cu3Sn single crystal by Czochralski method

Cu3Sn was recently predicted to host topological Dirac fermions, but related research is still in its infancy. The growth of large and high-quality Cu3Sn single crystals is, therefore, highly desired to investigate the possible topological properties. In this work, we report the single crystal growth of Cu3Sn by Czochralski (CZ) method. Crystal structure, chemical composition, and transport properties of Cu3Sn single crystals were analyzed to verify the crystal quality. Notably, compared to the mm-sized crystals from a molten Sn-flux, the cm-sized crystals obtained by the CZ method are free from contamination from flux materials, paving the way for the follow-up works.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

TrustAL: Trustworthy Active Learning using Knowledge Distillation

Active learning can be defined as iterations of data labeling, model training, and data acquisition, until sufficient labels are acquired. A traditional view of data acquisition is that, through iterations, knowledge from human labels and models is implicitly distilled to monotonically increase the accuracy and label consistency. Under this assumption, the most recently trained model is a good surrogate for the current labeled data, from which data acquisition is requested based on uncertainty/diversity. Our contribution is debunking this myth and proposing a new objective for distillation. First, we found example forgetting, which indicates the loss of knowledge learned across iterations. Second, for this reason, the last model is no longer the best teacher -- For mitigating such forgotten knowledge, we select one of its predecessor models as a teacher, by our proposed notion of "consistency". We show that this novel distillation is distinctive in the following three aspects; First, consistency ensures to avoid forgetting labels. Second, consistency improves both uncertainty/diversity of labeled data. Lastly, consistency redeems defective labels produced by human annotators.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Rectification by hydrodynamic flow in an encapsulated graphene Tesla valve

Systems in which interparticle interactions prevail can be described by hydrodynamics. This regime is typically difficult to access in the solid state for electrons. However, the high purity of encapsulated graphene combined with its advantageous phonon properties make it possible, and hydrodynamic corrections to the conductivity of graphene have been observed. Examples include electron whirlpools, enhanced flow through constrictions as well as a Poiseuille flow profile. An electronic device relying specifically on viscous behaviour and acting as a viscometer has however been lacking. Here, we implement the analogue of the Tesla valve. It exhibits nonreciprocal transport and can be regarded as an electronic viscous diode. Rectification occurs at carrier densities and temperatures consistent with the hydrodynamic regime, and disappears both in the ballistic and diffusive transport regimes. In a device in which the electrons are exposed to a Moiré superlattice, the Lifshitz transition when crossing the Van Hove singularity is observed in the rectifying behaviour.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Nanoscale imaging of equilibrium quantum Hall edge currents and of the magnetic monopole response in graphene

The recently predicted topological magnetoelectric effect and the response to an electric charge that mimics an induced mirror magnetic monopole are fundamental attributes of topological states of matter with broken time reversal symmetry. Using a SQUID-on-tip, acting simultaneously as a tunable scanning electric charge and as ultrasensitive nanoscale magnetometer, we induce and directly image the microscopic currents generating the magnetic monopole response in a graphene quantum Hall electron system. We find a rich and complex nonlinear behavior governed by coexistence of topological and nontopological equilibrium currents that is not captured by the monopole models. Furthermore, by utilizing a tuning fork that induces nanoscale vibrations of the SQUID-on-tip, we directly image the equilibrium currents of individual quantum Hall edge states for the first time. We reveal that the edge states that are commonly assumed to carry only a chiral downstream current, in fact carry a pair of counterpropagating currents, in which the topological downstream current in the incompressible region is always counterbalanced by heretofore unobserved nontopological upstream current flowing in the adjacent compressible region. The intricate patterns of the counterpropagating equilibrium-state orbital currents provide new insights into the microscopic origins of the topological and nontopological charge and energy flow in quantum Hall systems.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Broken-Symmetry Quantum Hall States in Twisted Bilayer Graphene

Twisted bilayer graphene offers a unique bilayer two-dimensional-electron system where the layer separation is only in sub-nanometer scale. Unlike Bernal-stacked bilayer, the layer degree of freedom is disentangled from spin and valley, providing eight-fold degeneracy in the low energy states. We have investigated broken-symmetry quantum Hall (QH) states and their transitions due to the interplay of the relative strength of valley, spin and layer polarizations in twisted bilayer graphene. The energy gaps of the broken-symmetry QH states show an electron-hole asymmetric behaviour, and their dependence on the induced displacement field are opposite between even and odd filling factor states. These results strongly suggest that the QH states with broken valley and spin symmetries for individual layer become hybridized via interlayer tunnelling, and the hierarchy of the QH states is sensitive to both magnetic field and displacement field due to charge imbalance between layers.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Charge inversion and topological phase transition at a twist angle induced van Hove singularity of bilayer graphene

Van Hove singularities (VHS's) in the density of states play an outstanding and diverse role for the electronic and thermodynamic properties of crystalline solids. At the critical point the Fermi surface connectivity changes and topological properties undergo a transition. Opportunities to systematically pass a VHS at the turn of a voltage knob and study its diverse impact are however rare. With the advent of van der Waals heterostructures, control over the atomic registry of neigbouring graphene layers offers an unprecedented tool to generate a low energy VHS easily accessible with conventional gating. Here we have addressed magnetotransport when the chemical potential crosses the twist angle induced VHS in twisted bilayer graphene. A topological phase transition is experimentally disclosed in the abrupt conversion of electrons to holes or vice versa, a loss of a non-zero Berry phase and distinct sequences of integer quantum Hall states above and below the singularity.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Structural attributes and photo-dynamics of visible spectrum quantum emitters in hexagonal boron nitride

Newly discovered van der Waals materials like MoS$_2$, WSe$_2$, hexagonal boron nitride (h-BN), and recently $\mathrm{C}_2\mathrm{N}$ have sparked intensive research to unveil the quantum behavior associated with their 2D structure. Of great interest are 2D materials that host single quantum emitters. h-BN, with a band gap of 5.95 eV, has been shown to host single quantum emitters which are stable at room temperature in the UV and visible spectral range. In this paper we investigate correlations between h-BN structural features and emitter location from bulk down to the monolayer at room temperature. We demonstrate that chemical etching and ion irradiation can generate emitters in h-BN. We analyze the emitters' spectral features and show that they are dominated by the interaction of their electronic transition with a single Raman active mode of h-BN. Photodynamics analysis reveals diverse rates between the electronic states of the emitter. The emitters show excellent photo stability even under ambient conditions and in monolayers. Comparing the excitation polarization between different emitters unveils a connection between defect orientation and the h-BN hexagonal structure. The sharp spectral features, color diversity, room-temperature stability, long-lived metastable states, ease of fabrication, proximity of the emitters to the environment, outstanding chemical stability, and biocompatibility of h-BN provide a completely new class of systems that can be used for sensing and quantum photonics applications.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Persistent Topological Surface State at the Interface of Bi2Se3 Film Grown on Patterned Graphene

We employed graphene as a patternable template to protect the intrinsic surface states of thin films of topological insulators (TIs) from environment. Here we find that the graphene provides high-quality interface so that the Shubnikov de Haas (SdH) oscillation associated with a topological surface state could be observed at the interface of a metallic Bi2Se3 film with a carrier density higher than ~10^19 cm-3. Our in situ X-ray diffraction study shows that the Bi2Se3 film grows epitaxially in a quintuple layer-by-layer fashion from the bottom layer without any structural distortion by interfacial strain. The magnetotransport measurements including SdH oscillations stemming from multiple conductance channels reveal that the topological surface state, with the mobility as high as ~0.5 m^2/Vs, remains intact from the graphene underneath without degradation. Given that the graphene was prepatterned on arbitrary insulating substrates, the TI-based microelectronic design could be exploited. Our study thus provides a step forward to observe the topological surface states at the interface without degradation by tuning the interface between TI and graphene into a measurable current for device application.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Ordered Growth of Topological Insulator Bi2Se3 Thin Films on Dielectric Amorphous SiO2 by MBE

Topological insulators (TIs) are exotic materials which have topologically protected states on the surface due to the strong spin-orbit coupling. However, a lack of ordered growth of TI thin films on amorphous dielectrics and/or insulators presents a challenge for applications of TI-junctions. We report the growth of topological insulator Bi2Se3 thin films on amorphous SiO2 by molecular beam epitaxy (MBE). To achieve the ordered growth of Bi2Se3 on amorphous surface, the formation of other phases at the interface is suppressed by Se passivation. Structural characterizations reveal that Bi2Se3 films are grown along the [001] direction with a good periodicity by van der Waals epitaxy mechanism. Weak anti-localization effect of Bi2Se3 films grown on amorphous SiO2 shows modulated electrical property by the gating response. Our approach for ordered growth of Bi2Se3 on amorphous dielectric surface presents considerable advantages for TI-junctions with amorphous insulator or dielectric thin films.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Breakdown of the interlayer coherence in twisted bilayer graphene

Coherent motion of the electrons in the Bloch states is one of the fundamental concepts of the charge conduction in solid state physics. In layered materials, however, such a condition often breaks down for the interlayer conduction, when the interlayer coupling is significantly reduced by e.g. large interlayer separation. We report that complete suppression of coherent conduction is realized even in an atomic length scale of layer separation in twisted bilayer graphene. The interlayer resistivity of twisted bilayer graphene is much higher than the c-axis resistivity of Bernal-stacked graphite, and exhibits strong dependence on temperature as well as on external electric fields. These results suggest that the graphene layers are significantly decoupled by rotation and incoherent conduction is a main transport channel between the layers of twisted bilayer graphene.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Ballistic transport of graphene pnp junctions with embedded local gates

We fabricated graphene pnp devices, by embedding pre-defined local gates in an oxidized surface layer of a silicon substrate. With neither dielectric-material deposition nor electron-beam irradiation on the graphene, we obtained high-quality graphene pnp devices without degradation of the carrier mobility even in the local-gate region. The corresponding increased mean free path leads to the observation of ballistic and phase-coherent transport across a 130-nm-wide local gate, which is about an order of magnitude wider than reported previously. Furthermore, in our scheme, we demonstrated independent control of the carrier density in the local-gate region, with a conductance map very distinctive from top-gated devices. It was caused as the electric field arising from the global back gate is strongly screened by the embedded local gate. Our scheme allows the realization of ideal multipolar graphene junctions with ballistic carrier transport.

0 citations0 reviewsNo code linkedOpen access