S. Chang - Department of Physics, Kyungpook National University, Daegu, Republic of Korea

S. Chang
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Name
S. Chang
Affiliation
Department of Physics, Kyungpook National University, Daegu, Republic of Korea
City
Daegu
Country
South Korea

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Computer Science - Computer Vision and Pattern Recognition (17)
 
Computer Science - Learning (9)
 
Computer Science - Multimedia (4)
 
Physics - Materials Science (4)
 
High Energy Physics - Experiment (3)
 
Computer Science - Computers and Society (3)
 
Computer Science - Artificial Intelligence (3)
 
Physics - Mesoscopic Systems and Quantum Hall Effect (3)
 
Computer Science - Human-Computer Interaction (3)
 
Computer Science - Sound (3)
 
Computer Science - Computation and Language (2)
 
Computer Science - Information Retrieval (2)
 
Mathematics - Statistics (2)
 
Mathematics - Differential Geometry (2)
 
Computer Science - Information Theory (2)
 
High Energy Physics - Phenomenology (2)
 
Mathematics - Information Theory (2)
 
Computer Science - Data Structures and Algorithms (2)
 
Statistics - Theory (2)
 
Computer Science - Cryptography and Security (1)
 
Physics - Instrumentation and Detectors (1)
 
Quantitative Biology - Tissues and Organs (1)
 
Mathematics - Functional Analysis (1)
 
Mathematics - Probability (1)
 
Physics - Biological Physics (1)
 
Instrumentation and Methods for Astrophysics (1)
 
Computer Science - Neural and Evolutionary Computing (1)
 
Computer Science - Robotics (1)
 
Physics - Superconductivity (1)
 
Physics - Strongly Correlated Electrons (1)
 
Physics - Other (1)
 
Solar and Stellar Astrophysics (1)
 
Computer Science - Graphics (1)
 
Statistics - Applications (1)
 
Statistics - Machine Learning (1)
 
Astrophysics of Galaxies (1)
 
Cosmology and Nongalactic Astrophysics (1)

Publications Authored By S. Chang

Visual patterns represent the discernible regularity in the visual world. They capture the essential nature of visual objects or scenes. Understanding and modeling visual patterns is a fundamental problem in visual recognition that has wide ranging applications. Read More

Temporal action localization is an important yet challenging problem. Given a long, untrimmed video consisting of multiple action instances and complex background contents, we need not only to recognize their action categories, but also to localize the start time and end time of each instance. Many state-of-the-art systems use segment-level classifiers to select and rank proposal segments of pre-determined boundaries. Read More

Visual relations, such as "person ride bike" and "bike next to car", offer a comprehensive scene understanding of an image, and have already shown their great utility in connecting computer vision and natural language. However, due to the challenging combinatorial complexity of modeling subject-predicate-object relation triplets, very little work has been done to localize and predict visual relations. Inspired by the recent advances in relational representation learning of knowledge bases and convolutional object detection networks, we propose a Visual Translation Embedding network (VTransE) for visual relation detection. Read More

We have studied ferroelectricity and photovoltaic effects in atomic layer deposited (ALD) 40-nm thick SnTiO$_{x}$ films deposited directly onto p-type (001)Si substrate. These films showed well-saturated, square and repeatable hysteresis loops with remnant polarization of 1.5 $\mu$C/cm$^{2}$ at room temperature, as detected by out-of-plane polarization versus electric field (P-E) and field cycling measurements. Read More

The atomic-scale synthesis of artificial oxide heterostructures offers new opportunities to create novel states that do not occur in nature. The main challenge related to synthesizing these structures is obtaining atomically sharp interfaces with designed termination sequences. Here, we demonstrate that the oxygen pressure (PO2) during growth plays an important role in controlling the interfacial terminations of SrRuO3/BaTiO3/SrRuO3 (SRO/BTO/SRO) ferroelectric capacitors. Read More

Consider the product of $m$ independent $n\times n$ spherical ensembles for $m\ge 1$. The empirical distribution based on the $n$ eigenvalues of the product is called the empirical spectral distribution. A recent paper by Zeng (2016) obtains the limit of the empirical spectral distribution for the product ensemble when $m$ is a fixed integer. Read More

Most of the previous approaches to lyrics-to-audio alignment used a pre-developed automatic speech recognition (ASR) system that innately suffered from several difficulties to adapt the speech model to individual singers. A significant aspect missing in previous works is the self-learnability of repetitive vowel patterns in the singing voice, where the vowel part used is more consistent than the consonant part. Based on this, our system first learns a discriminative subspace of vowel sequences, based on weighted symmetric non-negative matrix factorization (WS-NMF), by taking the self-similarity of a standard acoustic feature as an input. Read More

Physical-layer group secret-key (GSK) generation is an effective way of generating secret keys in wireless networks, wherein the nodes exploit inherent randomness in the wireless channels to generate group keys, which are subsequently applied to secure messages while broadcasting, relaying, and other network-level communications. While existing GSK protocols focus on securing the common source of randomness from external eavesdroppers, they assume that the legitimate nodes of the group are trusted. In this paper, we address insider attacks from the legitimate participants of the wireless network during the key generation process. Read More

The unification scheme of active galactic nuclei (AGNs) invokes an optically thick molecular torus component hiding the broad emission line region. Assuming the presence of a thick neutral component in the molecular torus characterized by a \ion{H}{I} column density > $10^{22}{\rm\ cm^{-2}}$, we propose that far UV radiation around Ly$\alpha$ can be significantly polarized through Rayleigh scattering. Adopting a Monte Carlo technique we compute polarization of Rayleigh scattered radiation near Ly$\alpha$ in a thick neutral region in the shape of a slab and a cylindrical shell. Read More

Electronic structures of Ge$_{1-x}$Sn$_{x}$ alloys (0 $\leq$ $x$ $\leq$ 1) are theoretically studied by nonlocal empirical pseudopotential method. For relaxed Ge$_{1-x}$Sn$_{x}$, a topological semimetal is found for $\textit x$ $>$ 41$\%$ with gapless and band inversion at ${\Gamma}$ point, while there is an indirect-direct bandgap transition at $x$ = 8.5$\%$. Read More

Collider signals of dark photons are an exciting probe for new gauge forces and are characterized by events with boosted lepton jets. Existing techniques are efficient in searching for muonic lepton jets but due to substantial backgrounds have difficulty constraining lepton jets containing only electrons. This is unfortunate since upcoming intensity frontier experiments are sensitive to dark photon masses which only allow electron decays. Read More

This paper presents an efficient implementation of the Wavenet generation process called Fast Wavenet. Compared to a naive implementation that has complexity O(2^L) (L denotes the number of layers in the network), our proposed approach removes redundant convolution operations by caching previous calculations, thereby reducing the complexity to O(L) time. Timing experiments show significant advantages of our fast implementation over a naive one. Read More

The problem of testing mutually exclusive hypotheses with dependent test statistics is considered. Bayesian and frequentist approaches to multiplicity control are studied and compared to help gain understanding as to the effect of test statistic dependence on each approach. The Bayesian approach is shown to have excellent frequentist properties and is argued to be the most effective way of obtaining frequentist multiplicity control, without sacrificing power, when there is considerable test statistic dependence. Read More

The Multilingual Visual Sentiment Ontology (MVSO) consists of 15,600 concepts in 12 different languages that are strongly related to emotions and sentiments expressed in images. These concepts are defined in the form of Adjective-Noun Pair (ANP), which are crawled and discovered from online image forum Flickr. In this work, we used Amazon Mechanical Turk as a crowd-sourcing platform to collect human judgments on sentiments expressed in images that are uniformly sampled over 3,911 English ANPs extracted from a tag-restricted subset of MVSO. Read More

One-dimensional (1D) quantum systems, which are predicted to exhibit novel states of matter in theory, have been elusive in experiment. Here we report a superlattice method of creating artificial 1D quantum stripes, which offers dimensional tunability from two- to one-dimensions. As a model system, we have fabricated 1D iridium (Ir) stripes using a-axis oriented superlattices of a relativistic Mott insulator Sr2IrO4 and a wide bandgap insulator LaSrGaO4, both of which are crystals with layered structure. Read More

Symbiotic stars are regarded as wide binary systems consisting of a hot white dwarf and a mass losing giant. They exhibit unique spectral features at 6825 \AA\ and 7082 \AA, which are formed via Raman scattering of \ion{O}{6}$\lambda\lambda$ 1032 and 1038 with atomic hydrogen. We adopt a Monte Carlo technique to generate the same number of \ion{O}{6}$\lambda$1032 and $\lambda$1038 line photons and compute the flux ratio $F(6825)/F(7082)$ of these Raman scattered \ion{O}{6} features formed in neutral regions with a simple geometric shape as a function of \ion{H}{1} column density $N_{HI}$. Read More

Mobile applications and on-body devices are becoming increasingly ubiquitous tools for physical activity tracking. We propose utilizing a self-tracker's habits to support continuous prediction of whether they will reach their daily step goal, thus enabling a variety of potential persuasive interventions. Our aim is to improve the prediction by leveraging historical data and other qualitative (motivation for using the systems, location, gender) and, quantitative (age) features. Read More

The optimization of joint source and channel coding for a sequence of numerous progressive packets is a challenging problem. Further, the problem becomes more complicated if the space-time coding is also involved with the optimization in a multiple-input multiple-output (MIMO) system. This is because the number of ways of jointly assigning channels codes and space-time codes to progressive packets is much larger than that of solely assigning channel codes to the packets. Read More

Lecture notes are important for students to review and understand the key points in the class. Unfortunately, the students often miss or lose part of the lecture notes. In this paper, we design and implement an infrared sensor based system, InfraNotes, to automatically record the notes on the board by sensing and analyzing hand gestures of the lecturer. Read More

Because of the spread of the Internet, social platforms become big data pools. From there we can learn about the trends, culture and hot topics. This project focuses on analyzing the data from Instagram. Read More

A novel scheme for non-volatile digital computation is proposed using spin-transfer torque (STT) and automotion of magnetic domain walls (DWs). The basic computing element is composed of a lateral spin valve (SV) with two ferromagnetic (FM) wires served as interconnects, where DW automotion is used to propagate the information from one device to another. The non-reciprocity of both device and interconnect is realized by sizing different contact areas at the input and the output as well as enhancing the local damping mechanism. Read More

In this paper, a theoretical approach, comprising the non-equilibrium Green's function method for electronic transport and Landau-Khalatnikov equation for electric polarization dynamics, is presented to describe polarization-dependent tunneling electroresistance (TER) in ferroelectric tunnel junctions. Using appropriate contact, interface, and ferroelectric parameters, measured current-voltage characteristic curves in both inorganic (Co/BaTiO$_{3}$/La$_{0.67}$Sr$_{0. Read More

With the agreement of my coauthors, I Zhangyang Wang would like to withdraw the manuscript "Stacked Approximated Regression Machine: A Simple Deep Learning Approach". Some experimental procedures were not included in the manuscript, which makes a part of important claims not meaningful. In the relevant research, I was solely responsible for carrying out the experiments; the other coauthors joined in the discussions leading to the main algorithm. Read More

The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios. Data streams present distinct properties such as temporally ordered, continuous and high-velocity, which poses tremendous challenges to traditional recommender systems. In this paper, we investigate the problem of recommendation with stream inputs. Read More

Robotic manipulation of deformable objects is a difficult problem especially because of the complexity of the many different ways an object can deform. Searching such a high dimensional state space makes it difficult to recognize, track, and manipulate deformable objects. In this paper, we introduce a predictive, model-driven approach to address this challenge, using a pre-computed, simulated database of deformable object models. Read More

In this work, we consider a manufactory process which can be described by a multiple-instance logistic regression model. In order to compute the maximum likelihood estimation of the unknown coefficient, an expectation-maximization algorithm is proposed, and the proposed modeling approach can be extended to identify the important covariates by adding the coefficient penalty term into the likelihood function. In addition to essential technical details, we demonstrate the usefulness of the proposed method by simulations and real examples. Read More

The physical properties of carbon nanoscoll (CNS) were studied by using the first principles methods within the generalized gradient approximation. Two different kinds of chiral (armchair and zigzag) CNS s are considered and their properties were compared. Bond lengths, ground state energies, band structures, and band gaps are discussed. Read More

In applications involving matching of image sets, the information from multiple images must be effectively exploited to represent each set. State-of-the-art methods use probabilistic distribution or subspace to model a set and use specific distance measure to compare two sets. These methods are slow to compute and not compact to use in a large scale scenario. Read More

Despite the rampant adoption of Enterprise 2.0, there is lack of empirical evidence of how Enterprise 2.0 is aptly supporting the business objectives. Read More

The impact of culture in visual emotion perception has recently captured the attention of multimedia research. In this study, we pro- vide powerful computational linguistics tools to explore, retrieve and browse a dataset of 16K multilingual affective visual concepts and 7.3M Flickr images. Read More

This technical report details several improvements to the visual concept detector banks built on images from the Multilingual Visual Sentiment Ontology (MVSO). The detector banks are trained to detect a total of 9,918 sentiment-biased visual concepts from six major languages: English, Spanish, Italian, French, German and Chinese. In the original MVSO release, adjective-noun pair (ANP) detectors were trained for the six languages using an AlexNet-styled architecture by fine-tuning from DeepSentiBank. Read More

Considerable research effort has been devoted to the study of Policy in the domain of Information Security Management (ISM). However, our review of ISM literature identified four key deficiencies that reduce the utility of the guidance to organisations implementing policy management practices. This paper provides a comprehensive overview of the management practices of information security policy and develops a practice-based model that addresses the four aforementioned deficiencies. Read More

EventNet is a large-scale video corpus and event ontology consisting of 500 events associated with event-specific concepts. In order to improve the quality of the current EventNet, we conduct the following steps and introduce EventNet version 1.1: (1) manually verify the correctness of event labels for all videos; (2) remove the YouTube user bias by limiting the maximum number of videos in each event from the same YouTube user as 3; (3) remove the videos which are currently not accessible online; (4) remove the video belonging to multiple event categories. Read More

This paper aims for generic instance search from one example where the instance can be an arbitrary object like shoes, not just near-planar and one-sided instances like buildings and logos. First, we evaluate state-of-the-art instance search methods on this problem. We observe that what works for buildings loses its generality on shoes. Read More

2016Apr
Authors: Z. Deng, Y. Li, Y. Wang, Q. Yue, Z. Yang, J. Apostolakis, G. Folger, C. Grefe, V. Ivantchenko, A. Ribon, V. Uzhinskiy, D. Boumediene, C. Carloganu, V. Français, G. Cho, D-W. Kim, S. C. Lee, W. Park, S. Vallecorsa, S. Cauwenbergh, M. Tytgat, A. Pingault, N. Zaganidis, E. Brianne, A. Ebrahimi, K. Gadow, P. Göttlicher, C. Günter, O. Hartbrich, B. Hermberg, A. Irles, F. Krivan, K. Krüger, J. Kvasnicka, S. Lu, B. Lutz, V. Morgunov, C. Neubüser, A. Provenza, M. Reinecke, F. Sefkow, S. Schuwalow, H. L. Tran, E. Garutti, S. Laurien, M. Matysek, M. Ramilli, S. Schroeder, B. Bilki, E. Norbeck, D. Northacker, Y. Onel, S. Chang, A. Khan, D. H. Kim, D. J. Kong, Y. D. Oh, K. Kawagoe, H. Hirai, Y. Sudo, T. Suehara, H. Sumida, T. Yoshioka, E. Cortina Gil, S. Mannai, V. Buridon, C. Combaret, L. Caponetto, R. Eté, G. Garillot, G. Grenier, R. Han, J. C. Ianigro, R. Kieffer, I. Laktineh, N. Lumb, H. Mathez, L. Mirabito, A. Petrukhin, A. Steen, J. Berenguer Antequera, E. Calvo Alamillo, M. -C. Fouz, J. Marin, J. Puerta-Pelayo, A. Verdugo, M. Chadeeva, M. Danilov, M. Gabriel, P. Goecke, C. Kiesling, N. vanderKolk, F. Simon, M. Szalay, S. Bilokin, J. Bonis, P. Cornebise, F. Richard, R. Pöschl, J. Rouëné, A. Thiebault, D. Zerwas, M. Anduze, V. Balagura, K. Belkadhi, V. Boudry, J-C. Brient, R. Cornat, M. Frotin, F. Gastaldi, Y. Haddad, F. Magniette, M. Ruan, M. Rubio-Roy, K. Shpak, H. Videau, D. Yu, S. Callier, S. Conforti di Lorenzo, F. Dulucq, G. Martin-Chassard, Ch. de la Taille, L. Raux, N. Seguin-Moreau, K. Kotera, H. Ono, T. Takeshita, F. Corriveau

The CALICE Semi-Digital Hadron Calorimeter (SDHCAL) technological prototype is a sampling calorimeter using Glass Resistive Plate Chamber detectors with a three-threshold readout as the active medium. This technology is one of the two options proposed for the hadron calorimeter of the International Large Detector for the International Linear Collider. The prototype was exposed to beams of muons, electrons and pions of different energies at the CERN Super Proton Synchrotron. Read More

Residual learning has recently surfaced as an effective means of constructing very deep neural networks for object recognition. However, current incarnations of residual networks do not allow for the modeling and integration of complex relations between closely coupled recognition tasks or across domains. Such problems are often encountered in multimedia applications involving large-scale content recognition. Read More

We investigate the $\ell_\infty$-constrained representation which demonstrates robustness to quantization errors, utilizing the tool of deep learning. Based on the Alternating Direction Method of Multipliers (ADMM), we formulate the original convex minimization problem as a feed-forward neural network, named \textit{Deep $\ell_\infty$ Encoder}, by introducing the novel Bounded Linear Unit (BLU) neuron and modeling the Lagrange multipliers as network biases. Such a structural prior acts as an effective network regularization, and facilitates the model initialization. Read More

In this paper, we propose a novelmethod to search for precise locations of paired note onset and offset in a singing voice signal. In comparison with the existing onset detection algorithms,our approach differs in two key respects. First, we employ Correntropy, a generalized correlation function inspired from Reyni's entropy, as a detection function to capture the instantaneous flux while preserving insensitiveness to outliers. Read More

In this paper, we generalize the Cao-Yau's gradient estimate for the sum of squares of vector fields up to higher step under assumption of the generalized curvature-dimension inequality. With its applications, by deriving a curvature-dimension inequality, we are able to obtain the Li-Yau gradient estimate for the CR heat equation in a closed pseudohermitian manifold of nonvanishing torsion tensors. As consequences, we obtain the Harnack inequality and upper bound estimate for the CR heat kernel. Read More

In this paper, we consider the heat flow for p-pseudoharmonic maps from a closed Sasakian manifold M into a compact Riemannian manifold N. We prove global existence and asymptotic convergence of the solution for the p-pseudoharmonic map heat flow, provided that the sectional curvature of the target manifold N is nonpositive. Moreover, without the curvature assumption on the target manifold, we obtain global existence and asymptotic convergence of the p-pseudoharmonic map heat flow as well when its initial p-energy is sufficiently small. Read More

In this paper, we design a Deep Dual-Domain ($\mathbf{D^3}$) based fast restoration model to remove artifacts of JPEG compressed images. It leverages the large learning capacity of deep networks, as well as the problem-specific expertise that was hardly incorporated in the past design of deep architectures. For the latter, we take into consideration both the prior knowledge of the JPEG compression scheme, and the successful practice of the sparsity-based dual-domain approach. Read More

Visual recognition research often assumes a sufficient resolution of the region of interest (ROI). That is usually violated in practice, inspiring us to explore the Very Low Resolution Recognition (VLRR) problem. Typically, the ROI in a VLRR problem can be smaller than $16 \times 16$ pixels, and is challenging to be recognized even by human experts. Read More

Image aesthetics assessment has been challenging due to its subjective nature. Inspired by the scientific advances in the human visual perception and neuroaesthetics, we design Brain-Inspired Deep Networks (BDN) for this task. BDN first learns attributes through the parallel supervised pathways, on a variety of selected feature dimensions. Read More

We address temporal action localization in untrimmed long videos. This is important because videos in real applications are usually unconstrained and contain multiple action instances plus video content of background scenes or other activities. To address this challenging issue, we exploit the effectiveness of deep networks in temporal action localization via three segment-based 3D ConvNets: (1) a proposal network identifies candidate segments in a long video that may contain actions; (2) a classification network learns one-vs-all action classification model to serve as initialization for the localization network; and (3) a localization network fine-tunes on the learned classification network to localize each action instance. Read More

In this paper we describe a novel framework and algorithms for discovering image patch patterns from a large corpus of weakly supervised image-caption pairs generated from news events. Current pattern mining techniques attempt to find patterns that are representative and discriminative, we stipulate that our discovered patterns must also be recognizable by humans and preferably with meaningful names. We propose a new multimodal pattern mining approach that leverages the descriptive captions often accompanying news images to learn semantically meaningful image patch patterns. Read More

The recent ATLAS and CMS diphoton resonance excesses are explored in a simple $U(1)$ gauge theory extension of the Standard Model where the resonance is the Higgs boson of the $U(1)$ symmetry breaking, $\phi$. This particle couples to exotic quarks which, through loops, can produce a large enough rate to explain the excess. Due to the choice of $U(1)$ charges, flavor constraints are naturally suppressed, allowing arbitrary flavor violation in the decays of the new quarks to up-type quarks, modifying their signal topologies. Read More

In animals, gas exchange between blood and tissues occurs in narrow vessels, whose diameter is typically less than that of a red blood cell. Red blood cells must deform to squeeze through these narrow vessels transiently blocking or occluding the vessels they pass through. Although the dynamics of vessel occlusion have been studied extensively, it remains an open question why microvessels need to be so narrow. Read More

Binary embeddings provide efficient and powerful ways to perform operations on large scale data. However binary embedding typically requires long codes in order to preserve the discriminative power of the input space. Thus binary coding methods traditionally suffer from high computation and storage costs in such a scenario. Read More

In this paper we introduce the concept of a convolution type operation of functionals on Wiener space. It contains several kinds of the concepts of convolution products on Wiener space, which have been studied by many authors. We then investigate fundamental relationships between generalized analytic Fourier--Feynman transforms and convolution type operations. Read More