arXiv subjects

Sangjin Lee

Publications and source records attributed to Sangjin Lee.

At least 37 records · Page 2Linked to original sources

arXiv2023

Pseudo-Anosov autoquivalances arising from Symplectic topology and their hyperbolic actions on stability conditions

Within $N$-Calabi-Yau categories associated with quivers whose base graphs form trees, we delve into the study of the asymptotic behaviors of autoequivalences of a specific type. These autoequivalences, which we call "Penner type," exhibit straightforward asymptotic characteristics, making them noteworthy exemplars of "pseudo-Anosov" autoequivalences in the sense of \cite{Fan-Filip-Haiden-Katzarkov-Liu21}, and also in a stronger sense that we define in the present paper. In addition, we provide a practical methodology for calculating the stretching factors of Penner type autoequivalences. We expect that this computational approach can have applications. As an example, we establish a positive lower bound on the translation length of the induced action these autoequivalences have on the space of stability conditions. Our anticipation is that this lower bound is, in fact, exact. Notably, we have observed instances of Penner type $Φ$ where the induced actions align precisely with this lower bound. In other words, these examples induce hyperbolic actions on the space of stability conditions.

math.SG↗

arXiv2023

Atom-level design strategy for hydrogen evolution reaction of transition metal dichalcogenides catalysts

Two-dimensional transition metal dichalcogenides are among the most promising materials for water-splitting catalysts. While a variety of methods have been applied to promote the hydrogen evolution reaction on the transition metal dichalcogenides, doping of transition metal heteroatoms have attracted much attention since it provides effective ways to optimize the hydrogen adsorption and H2 generation reactions. Herein, we provide in-depth and systematic analyses on the trends of the free energy of hydrogen adsorption (ΔGH*), the most well-known descriptor for evaluating hydrogen evolution reaction performance, in the doped transition metal dichalcogenides. Using the total 150 doped transition metal dichalcogenides, we carried out the atom-level analysis on the origin of ΔGH* changes upon the transition metal heteroatom doping, and suggest two key factors that govern the hydrogen adsorption process on the doped transition metal dichalcogenides: 1) the changes in the charge of chalcogen atoms where hydrogen atoms adsorbed for the early transition metal doped structures, and 2) the structural deformation energies accompanying in introduced dopants for the late transition metal doped structures. Based on our findings, we interpret from a new perspective how vacancies in the TM-doped TMDs can provide optimal ΔGH* in HER. We suggest electrostatic control for early TM doped systems and structural control for late TM doped systems as the effective strategies for the thermoneutral ΔGH* in TMD.

cond-mat.mtrl-sci↗

arXiv2023

Barcode entropy for Reeb flows on contact manifolds with Liouville fillings

We study the topological entropy of Reeb flows on contact manifolds with Liouville fillings. With the theory of persistence modules, we define SH-barcode entropy from the symplectic homology of a filling. We prove that the SH-barcode entropy is independent of the choice of the filling and that the barcode entropy provides a lower bound for the topological entropy of the Reeb flow.

math.SG↗

arXiv2023

Exploring Discontinuity for Video Frame Interpolation

Video frame interpolation (VFI) is the task that synthesizes the intermediate frame given two consecutive frames. Most of the previous studies have focused on appropriate frame warping operations and refinement modules for the warped frames. These studies have been conducted on natural videos containing only continuous motions. However, many practical videos contain various unnatural objects with discontinuous motions such as logos, user interfaces and subtitles. We propose three techniques to make the existing deep learning-based VFI architectures robust to these elements. First is a novel data augmentation strategy called figure-text mixing (FTM) which can make the models learn discontinuous motions during training stage without any extra dataset. Second, we propose a simple but effective module that predicts a map called discontinuity map (D-map), which densely distinguishes between areas of continuous and discontinuous motions. Lastly, we propose loss functions to give supervisions of the discontinuous motion areas which can be applied along with FTM and D-map. We additionally collect a special test benchmark called Graphical Discontinuous Motion (GDM) dataset consisting of some mobile games and chatting videos. Applied to the various state-of-the-art VFI networks, our method significantly improves the interpolation qualities on the videos from not only GDM dataset, but also the existing benchmarks containing only continuous motions such as Vimeo90K, UCF101, and DAVIS.

cs.CV↗

arXiv2023

One-Shot Video Inpainting

Recently, removing objects from videos and filling in the erased regions using deep video inpainting (VI) algorithms has attracted considerable attention. Usually, a video sequence and object segmentation masks for all frames are required as the input for this task. However, in real-world applications, providing segmentation masks for all frames is quite difficult and inefficient. Therefore, we deal with VI in a one-shot manner, which only takes the initial frame's object mask as its input. Although we can achieve that using naive combinations of video object segmentation (VOS) and VI methods, they are sub-optimal and generally cause critical errors. To address that, we propose a unified pipeline for one-shot video inpainting (OSVI). By jointly learning mask prediction and video completion in an end-to-end manner, the results can be optimal for the entire task instead of each separate module. Additionally, unlike the two stage methods that use the predicted masks as ground truth cues, our method is more reliable because the predicted masks can be used as the network's internal guidance. On the synthesized datasets for OSVI, our proposed method outperforms all others both quantitatively and qualitatively.

cs.CV↗

arXiv2022

Exotic families of symplectic manifolds with Milnor fibers of $ADE$-type

In this paper, we give infinitely many diffeomorphic families of different Weinstein manifolds. The diffeomorphic families consist of the Milnor fibers of $ADE$-type, and a Weinstein manifold constructed by gluing a cotangent bundle to the Milnor fiber of $A$-type. The last-mentioned members of the families have decomposable wrapped Fukaya categories.

math.SG↗

arXiv2022

Metrology of Band Topology via Resonant Inelastic X-ray Scattering

Topology is a central notion in the classification of band insulators and characterization of entangled many-body quantum states. In some cases, it manifests as quantized observables such as quantum Hall conductance. However, being inherently a global property depending on the entirety of the system, its direct measurement has remained elusive to local experimental probes in many cases. Here, we demonstrate that various topological band indices can be directly probed by resonant inelastic x-ray scattering. Specifically, we show that the crystalline symmetry eigenvalues at the high-symmetry momentum points, which determine the band topology, leads to distinct scattering intensity for particular momentum and energy. Our approach can be explicitly demonstrated in several examples such as 1D Su-Schrieffer-Heeger chain, 2D quadrupole insulator, 3D topological band insulator, and chiral hinge insulator. Our result establishes an incisive bulk probe for the measurement of band topology.

cond-mat.str-el↗

arXiv2022

Expanded Adaptive Scaling Normalization for End to End Image Compression

Recently, learning-based image compression methods that utilize convolutional neural layers have been developed rapidly. Rescaling modules such as batch normalization which are often used in convolutional neural networks do not operate adaptively for the various inputs. Therefore, Generalized Divisible Normalization(GDN) has been widely used in image compression to rescale the input features adaptively across both spatial and channel axes. However, the representation power or degree of freedom of GDN is severely limited. Additionally, GDN cannot consider the spatial correlation of an image. To handle the limitations of GDN, we construct an expanded form of the adaptive scaling module, named Expanded Adaptive Scaling Normalization(EASN). First, we exploit the swish function to increase the representation ability. Then, we increase the receptive field to make the adaptive rescaling module consider the spatial correlation. Furthermore, we introduce an input mapping function to give the module a higher degree of freedom. We demonstrate how our EASN works in an image compression network using the visualization results of the feature map, and we conduct extensive experiments to show that our EASN increases the rate-distortion performance remarkably, and even outperforms the VVC intra at a high bit rate.

eess.IV↗

arXiv2022

Homotopy Colimits of DG Categories and Fukaya Categories

We construct a new cylinder object for semifree differential graded (dg) categories in the category of dg categories. Using this, we give a practical formula computing homotopy colimits of semifree dg categories. Combining it with the result of Ganatra, Pardon, and Shende, we get a formula computing wrapped Fukaya categories of Weinstein manifolds using their sectorial coverings. This formula has lots of applications including a practical computation of the wrapped Fukaya category of any cotangent bundle or plumbing space. In this paper, we compute wrapped Fukaya categories of cotangent bundles of lens spaces using their Heegaard decomposition. From the computation, we show that the endomorphism algebra of the cotangent fibre is a full invariant of the homotopy type of lens spaces.

math.SG↗

arXiv2021

Test-Time Adaptation for Out-of-distributed Image Inpainting

Deep learning-based image inpainting algorithms have shown great performance via powerful learned prior from the numerous external natural images. However, they show unpleasant results on the test image whose distribution is far from the that of training images because their models are biased toward the training images. In this paper, we propose a simple image inpainting algorithm with test-time adaptation named AdaFill. Given a single out-of-distributed test image, our goal is to complete hole region more naturally than the pre-trained inpainting models. To achieve this goal, we treat remained valid regions of the test image as another training cues because natural images have strong internal similarities. From this test-time adaptation, our network can exploit externally learned image priors from the pre-trained features as well as the internal prior of the test image explicitly. Experimental results show that AdaFill outperforms other models on the various out-of-distribution test images. Furthermore, the model named ZeroFill, that are not pre-trained also sometimes outperforms the pre-trained models.

cs.CV↗

arXiv2021

Predicting post-operative right ventricular failure using video-based deep learning

Non-invasive and cost effective in nature, the echocardiogram allows for a comprehensive assessment of the cardiac musculature and valves. Despite progressive improvements over the decades, the rich temporally resolved data in echocardiography videos remain underutilized. Human reads of echocardiograms reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. Furthermore, all modern echocardiography artificial intelligence (AI) systems are similarly limited by design - automating measurements of the same reductionist metrics rather than utilizing the wealth of data embedded within each echo study. This underutilization is most evident in situations where clinical decision making is guided by subjective assessments of disease acuity, and tools that predict disease onset within clinically actionable timeframes are unavailable. Predicting the likelihood of developing post-operative right ventricular failure (RV failure) in the setting of mechanical circulatory support is one such clinical example. To address this, we developed a novel video AI system trained to predict post-operative right ventricular failure (RV failure), using the full spatiotemporal density of information from pre-operative echocardiography scans. We achieve an AUC of 0.729, specificity of 52% at 80% sensitivity and 46% sensitivity at 80% specificity. Furthermore, we show that our ML system significantly outperforms a team of human experts tasked with predicting RV failure on independent clinical evaluation. Finally, the methods we describe are generalizable to any cardiac clinical decision support application where treatment or patient selection is guided by qualitative echocardiography assessments.

cs.CV↗

arXiv2020

5W1H-based Expression for the Effective Sharing of Information in Digital Forensic Investigations

Digital forensic investigation is used in various areas related to digital devices including the cyber crime. This is an investigative process using many techniques, which have implemented as tools. The types of files covered by the digital forensic investigation are wide and varied, however, there is no way to express the results into a standardized format. The standardization are different by types of device, file system, or application. Different outputs make it time-consuming and difficult to share information and to implement integration. In addition, it could weaken cyber security. Thus, it is important to define normalization and to present data in the same format. In this paper, a 5W1H-based expression for information sharing for effective digital forensic investigation is proposed to analyze digital forensic information using six questions--what, who, where, when, why and how. Based on the 5W1H-based expression, digital information from different types of files is converted and represented in the same format of outputs. As the 5W1H is the basic writing principle, application of the 5W1H-based expression on the case studies shows that this expression enhances clarity and correctness for information sharing. Furthermore, in the case of security incidents, this expression has an advantage in being compatible with STIX.

cs.CR↗

arXiv2020

Extrapolative-Interpolative Cycle-Consistency Learning for Video Frame Extrapolation

Video frame extrapolation is a task to predict future frames when the past frames are given. Unlike previous studies that usually have been focused on the design of modules or construction of networks, we propose a novel Extrapolative-Interpolative Cycle (EIC) loss using pre-trained frame interpolation module to improve extrapolation performance. Cycle-consistency loss has been used for stable prediction between two function spaces in many visual tasks. We formulate this cycle-consistency using two mapping functions; frame extrapolation and interpolation. Since it is easier to predict intermediate frames than to predict future frames in terms of the object occlusion and motion uncertainty, interpolation module can give guidance signal effectively for training the extrapolation function. EIC loss can be applied to any existing extrapolation algorithms and guarantee consistent prediction in the short future as well as long future frames. Experimental results show that simply adding EIC loss to the existing baseline increases extrapolation performance on both UCF101 and KITTI datasets.

cs.CV↗

arXiv2020

Forensic Analysis of Residual Information in Adobe PDF Files

In recent years, as electronic files include personal records and business activities, these files can be used as important evidences in a digital forensic investigation process. In general, the data that can be verified using its own application programs is largely used in the investigation of document files. However, in the case of the PDF file that has been largely used at the present time, certain data, which include the data before some modifications, exist in electronic document files unintentionally. Because such residual information may present the writing process of a file, it can be usefully used in a forensic viewpoint. This paper introduces why the residual information is stored inside the PDF file and explains a way to extract the information. In addition, we demonstrate the attributes of PDF files can be used to hide data.

cs.MM↗

arXiv2020

Forensic analysis of the Windows telemetry for diagnostics

Telemetry is the automated sensing and collection of data from a remote device. It is often used to provide better services for users. Microsoft uses telemetry to periodically collect information about Windows systems and to help improve user experience and fix potential issues. Windows telemetry service functions by creating RBS files on the local system to reliably transfer and manage the telemetry data, and these files can provide useful information in a digital forensic investigation. Combined with the information derived from traditional Windows forensics, investigators can have greater confidence in the evidence derived from various artifacts. It is possible to acquire information that can be confirmed only for live systems, such as the computer hardware serial number, the connection records for external storage devices, and traces of executed processes. This information is included in the RBS files that are created for use in Windows telemetry. In this paper, we introduced how to acquire RBS files telemetry and analyzed the data structure of these RBS files, which are able to determine the types of information that Windows OS have been collected. We also discussed the reliability and the novelty by comparing the conventional artifacts with the RBS files, which could be useful in digital forensic investigation.

cs.CR↗

arXiv2019

Towards a higher-dimensional construction of stable/unstable Lagrangian laminations

We generalize some properties of surface automorphisms of pseudo-Anosov type. First, we generalize the Penner construction of a pseudo-Anosov homeomorphism and show that a symplectic automorphism which is constructed by our generalized Penner construction has an invariant Lagrangian branched submanifold and an invariant Lagrangian lamination, which are higher-dimensional generalizations of a train track and a geodesic lamination in the surface case. Moreover, if a pair consisting of a symplectic automorphism $ψ$ and a Lagrangian branched surface $B_ψ$ satisfies some assumptions, we prove that there is an invariant Lagrangian lamination $\mathcal{L}$ which is a higher-dimensional generalization of a geodesic lamination.

math.SG↗

arXiv2019

Multi-scale Quantum Criticality driven by Kondo-lattice Coupling in Pyrochlore Systems

Pyrochlore systems ($A_{2}B_{2}O_{7}$) with $A$-site rare-earth local moments and $B$-site $5d$ conduction electrons offer excellent material platforms for the discovery of exotic quantum many-body ground states. Notable examples include U(1) quantum spin liquid of the local moments and semimetallic non-Fermi liquid of the conduction electrons. Here we investigate emergent quantum phases and their transitions driven by the Kondo lattice coupling between such highly entangled quantum ground states. Using the renormalization group method, it is shown that weak Kondo lattice coupling is irrelevant, leading to a fractionalized semimetal phase with decoupled local moments and conduction electrons. Upon increasing the Kondo lattice coupling, this phase is unstable to the formation of broken symmetry states. Particularly important is the opposing influence of the Kondo lattice coupling and long-range Coulomb interaction. The former prefers to break the particle-hole symmetry while the latter tends to restore it. The characteristic competition leads to possibly multiple phase transitions, first from a fractionalized semimetal phase to a fractionalized Fermi surface state with particle-hole pockets, followed by the second transition to a fractionalized ferromagnetic state. Multiscale quantum critical behaviors appear at nonzero temperatures and with external magnetic field near such quantum phase transitions. We discuss the implication of these results to the experiments on Pr$_2$Ir$_2$O$_7$.

cond-mat.str-el↗

arXiv2019

Spin-lattice Coupling in U(1) Quantum Spin Liquids

Quantum spin liquids (QSLs) are exotic phases with intrinsic massive entanglements. Instead of microscopic spins, fractionalized particles and gauge fluctuations are emergent, revealing QSLs' exotic natures. Quantum spins with strong spin-orbit coupling on a pyrchlore lattice, for example Pr2Zr2O7, are suggested to host a U(1) QSL with emergent photons, gapless excitations without breaking any symmetries, as well as emergent monopoles. One of the key issues in QSLs is an interplay between emergent degrees of freedom of QSLs and conventional degrees of freedom, and we investigate the interplay by constructing a general theory of spin-lattice coupling in U(1) QSLs. We find that the coupling induces characteristic interplay between phonons and photons. For example, photons become qualitatively more stable than phonons at low temperature. We also propose mechanisms to detect emergent photons in experiments such as sound attenuation and thermal transport relying on spin-lattice coupling in U(1) QSLs.

cond-mat.str-el↗