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S. Wang
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S. Wang

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Computer Science - Learning (8)
Computer Science - Computer Vision and Pattern Recognition (6)
Quantum Physics (5)
Astrophysics of Galaxies (4)
Computer Science - Distributed; Parallel; and Cluster Computing (4)
High Energy Physics - Theory (3)
Computer Science - Digital Libraries (3)
General Relativity and Quantum Cosmology (3)
Computer Science - Information Retrieval (3)
High Energy Physics - Phenomenology (2)
Statistics - Machine Learning (2)
High Energy Physics - Experiment (2)
Computer Science - Cryptography and Security (2)
Physics - Materials Science (2)
Computer Science - Architecture (2)
Mathematics - Geometric Topology (2)
Mathematics - Numerical Analysis (2)
High Energy Astrophysical Phenomena (2)
Computer Science - Databases (1)
Solar and Stellar Astrophysics (1)
Quantitative Biology - Biomolecules (1)
Computer Science - Computer Science and Game Theory (1)
Computer Science - Numerical Analysis (1)
Physics - Mesoscopic Systems and Quantum Hall Effect (1)
Physics - Superconductivity (1)
Computer Science - Computation and Language (1)
Physics - Statistical Mechanics (1)
Physics - Optics (1)
Computer Science - Networking and Internet Architecture (1)
Mathematics - Differential Geometry (1)
Computer Science - Artificial Intelligence (1)
Statistics - Applications (1)
Instrumentation and Methods for Astrophysics (1)
Cosmology and Nongalactic Astrophysics (1)
Physics - Instrumentation and Detectors (1)
Earth and Planetary Astrophysics (1)

Publications Authored By S. Wang

Authors: Hiroaki Aihara, Robert Armstrong, Steven Bickerton, James Bosch, Jean Coupon, Hisanori Furusawa, Yusuke Hayashi, Hiroyuki Ikeda, Yukiko Kamata, Hiroshi Karoji, Satoshi Kawanomoto, Michitaro Koike, Yutaka Komiyama, Robert H. Lupton, Sogo Mineo, Hironao Miyatake, Satoshi Miyazaki, Tomoki Morokuma, Yoshiyuki Obuchi, Yukie Oishi, Yuki Okura, Paul A. Price, Tadafumi Takata, Manobu M. Tanaka, Masayuki Tanaka, Yoko Tanaka, Tomohisa Uchida, Fumihiro Uraguchi, Yousuke Utsumi, Shiang-Yu Wang, Yoshihiko Yamada, Hitomi Yamanoi, Naoki Yasuda, Nobuo Arimoto, Masashi Chiba, Francois Finet, Hiroki Fujimori, Seiji Fujimoto, Junko Furusawa, Tomotsugu Goto, Andy Goulding, James E. Gunn, Yuichi Harikane, Takashi Hattori, Masao Hayashi, Krzysztof G. Helminiak, Ryo Higuchi, Chiaki Hikage, Paul T. P. Ho, Bau-Ching Hsieh, Kuiyun Huang, Song Huang, Masatoshi Imanishi, Ikuru Iwata, Anton T. Jaelani, Hung-Yu Jian, Nobunari Kashikawa, Nobuhiko Katayama, Takashi Kojima, Akira Konno, Shintaro Koshida, Alexie Leauthaud, C. -H. Lee, Lihwai Lin, Yen-Ting Lin, Rachel Mandelbaum, Yoshiki Matsuoka, Elinor Medezinski, Shoken Miyama, Rieko Momose, Anupreeta More, Surhud More, Shiro Mukae, Ryoma Murata, Hitoshi Murayama, Tohru Nagao, Fumiaki Nakata, Hiroko Niikura, Atsushi J. Nishizawa, Masamune Oguri, Nobuhiro Okabe, Yoshiaki Ono, Masato Onodera, Masafusa Onoue, Masami Ouchi, Tae-Soo Pyo, Takatoshi Shibuya, Kazuhiro Shimasaku, Melanie Simet, Joshua Speagle, David N. Spergel, Michael A. Strauss, Yuma Sugahara, Naoshi Sugiyama, Yasushi Suto, Nao Suzuki, Philip J. Tait, Masahiro Takada, Tsuyoshi Terai, Yoshiki Toba, Edwin L. Turner, Hisakazu Uchiyama, Keiichi Umetsu, Yuji Urata, Tomonori Usuda, Sherry Yeh, Suraphong Yuma

The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) is a three-layered imaging survey aimed at addressing some of the most outstanding questions in astronomy today, including the nature of dark matter and dark energy. The survey has been awarded 300 nights of observing time at the Subaru Telescope. The survey started in March 2014. Read More

After a clustering solution is generated automatically, labelling these clusters becomes important to help understanding the results. In this paper, we propose to use a Mutual Information based method to label clusters of journal articles. Topical terms which have the highest Normalised Mutual Information (NMI) with a certain cluster are selected to be the labels of the cluster. Read More

We study the effects of synthetic spin-orbit coupling on the pairing physics in an ultracold quasi-one-dimensional alkaline-earth-like atoms near an orbital Feshbach resonance (OFR). The interplay between spin-orbit coupling and pairing interactions near the OFR leads to an interesting topological Fulde-Ferrell state, where the non-trivial topology of the state is solely encoded in the closed channel with a topologically trivial Fulde-Ferrell pairing in the open channel. We confirm the topological property of the system by characterizing the Zak phase and the edge states. Read More

Crowdsourced mobile video streaming enables nearby mobile video users to aggregate network resources to improve their video streaming performances. However, users are often selfish and may not be willing to cooperate without proper incentives. Designing an incentive mechanism for such a scenario is challenging due to the users' asynchronous downloading behaviors and their private valuations for multi-bitrate coded videos. Read More

Given a connected real Lie group and a contractible homogeneous proper $G$--space $X$ furnished with a $G$--invariant volume form, a real valued volume can be assigned to any representation $\rho\colon \pi_1(M)\to G$ for any oriented closed smooth manifold $M$ of the same dimension as $X$. Suppose that $G$ contains a closed and cocompact semisimple subgroup, it is shown in this paper that the set of volumes is finite for any given $M$. From a perspective of model geometries, examples are investigated and applications with mapping degrees are discussed. Read More

Large-scale deep convolutional neural networks (CNNs) are widely used in machine learning applications. While CNNs involve huge complexity, VLSI (ASIC and FPGA) chips that deliver high-density integration of computational resources are regarded as a promising platform for CNN's implementation. At massive parallelism of computational units, however, the external memory bandwidth, which is constrained by the pin count of the VLSI chip, becomes the system bottleneck. Read More

As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift. It deals with data streams having very skewed class distributions, where concept drift may occur. It has recently received increased research attention; however, very little work addresses the combined problem where both class imbalance and concept drift coexist. Read More

The Principle of Maximum Conformality (PMC) systematically eliminates the renormalization scheme and renormalization scale uncertainties for high-energy processes. The resulting PMC predictions are scheme independent, and the residual renormalization scale dependence due to unknown high-order terms are negligible at the next-to-next-to-leading order level. By applying the PMC scale-setting, one obtains comprehensive and self-consistent pQCD predictions for the top-quark pair total cross-section and the top-quark pair forward-backward asymmetry in agreement with the experimental measurements at the Tevatron and LHC. Read More

This paper proposes the SVDNet for retrieval problems, with focus on the application of person re-identification (re-ID). We view each weight vector within a fully connected (FC) layer in a convolutional neuron network (CNN) as a projection basis. It is observed that the weight vectors are usually highly correlated. Read More

We propose a scheme to simulate topological physics within a single degenerate cavity, whose modes are mapped to lattice sites. A crucial ingredient of the scheme is to construct a sharp boundary so that the open boundary condition can be implemented for this effective lattice system. In doing so, the topological properties of the system can manifest themselves on the edge states, which can be probed from the spectrum of an output cavity field. Read More

We have obtained OH spectra of four transitions in the $^2\Pi_{3/2}$ ground state, at 1612, 1665, 1667, and 1720 MHz, toward 51 sightlines that were observed in the Herschel project Galactic Observations of Terahertz C+. The observations cover the longitude range of (32$^\circ$, 64$^\circ$) and (189$^\circ$, 207$^\circ$) in the northern Galactic plane. All of the diffuse OH emissions conform to the so-called 'Sum Rule' of the four brightness temperatures, indicating optically thin emission condition for OH from diffuse clouds in the Galactic plane. Read More

We put forward a method for achieving fast and robust for magnetization reversal in a nanomagnet, by combining the inverse engineering and composite pulses. The magnetic fields, generated by microwave with time-dependent frequency, are first designed inversely within short operation time, and composite pulses are further incorporated to improve the fidelity through reducing the effect of magnetic anisotropy. The high-fidelity magnetization reversals are illustrated with numerical examples, and visualized on Bloch sphere. Read More

We revisit the problem of work extraction from a system in contact with a heat bath to a work storage system, and the reverse problem of state formation from a thermal system state in single-shot quantum thermodynamics. A physically intuitive and mathematically simple approach using only elementary majorization theory and matrix analysis is developed, and a graphical interpretation of the maximum extractable work, minimum work cost of formation, and corresponding single-shot free energies is presented. This approach provides a bridge between two previous methods based respectively on the concept of thermomajorization and a comparison of subspace dimensions. Read More

Inference of user context information, including user's gender, age, marital status, location and so on, has been proven to be valuable for building context aware recommender system. However, prevalent existing studies on user context inference have two shortcommings: 1. focusing on only a single data source (e. Read More

Cognitive inference of user demographics, such as gender and age, plays an important role in creating user profiles for adjusting marketing strategies and generating personalized recommendations because user demographic data is usually not available due to data privacy concerns. At present, users can readily express feedback regarding products or services that they have purchased. During this process, user demographics are concealed, but the data has never yet been successfully utilized to contribute to the cognitive inference of user demographics. Read More

Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making. These applications pose a new set of requirements, none of which are difficult to achieve in isolation, but the combination of which creates a challenge for existing distributed execution frameworks: computation with millisecond latency at high throughput, adaptive construction of arbitrary task graphs, and execution of heterogeneous kernels over diverse sets of resources. We assert that a new distributed execution framework is needed for such ML applications and propose a candidate approach with a proof-of-concept architecture that achieves a 63x performance improvement over a state-of-the-art execution framework for a representative application. Read More

Feature extraction and dimension reduction for networks is critical in a wide variety of domains. Efficiently and accurately learning features for multiple graphs has important applications in statistical inference on graphs. We propose a method to jointly embed multiple undirected graphs. Read More

Data noising is an effective technique for regularizing neural network models. While noising is widely adopted in application domains such as vision and speech, commonly used noising primitives have not been developed for discrete sequence-level settings such as language modeling. In this paper, we derive a connection between input noising in neural network language models and smoothing in $n$-gram models. Read More

Recent studies demonstrate that effective healthcare can benefit from using the human genomic information. For instance, analysis of tumor genomes has revealed 140 genes whose mutations contribute to cancer. As a result, many institutions are using statistical analysis of genomic data, which are mostly based on genome-wide association studies (GWAS). Read More

The direct detection of gravitational wave by LIGO indicates the coming of the era of gravitational wave astronomy and gravitational wave cosmology. It is expected that more and more gravitational wave events will be detected by currently existing and planed gravitational wave detectors. The gravitational waves open a new window to explore the Universe and various mysteries will be disclosed through the gravitational wave detection, combined with other cosmological probes. Read More

A new method for the measurement of sample reactivity worth in a fast neutron reactor named the inverse kinetics method is proposed in the paper. The sample reactivity worth could be obtained by measuring the reactivity step change in the process of sample fetching and placing in the delayed critical reactor. Compared with the traditional period method, the advantage is that the accuracy of reactor reactivity control will not exert any influence on the uncertainty of reactivity worth measurement. Read More

Deep convolutional neural networks (CNN) have shown their good performances in many computer vision tasks. However, the high computational complexity of CNN involves a huge amount of data movements between the computational processor core and memory hierarchy which occupies the major of the power consumption. This paper presents Chain-NN, a novel energy-efficient 1D chain architecture for accelerating deep CNNs. Read More

Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied. Read More

Object detection aims to identify instances of semantic objects of a certain class in images or videos. The success of state-of-the-art approaches is attributed to the significant progress of object proposal and convolutional neural networks (CNNs). Most promising detectors involve multi-task learning with an optimization objective of softmax loss and regression loss. Read More

Authors: CDF Collaboration, T. Aaltonen, M. G. Albrow, S. Amerio, D. Amidei, A. Anastassov, A. Annovi, J. Antos, G. Apollinari, J. A. Appel, T. Arisawa, A. Artikov, J. Asaadi, W. Ashmanskas, B. Auerbach, A. Aurisano, F. Azfar, W. Badgett, T. Bae, A. Barbaro-Galtieri, V. E. Barnes, B. A. Barnett, P. Barria, P. Bartos, M. Bauce, F. Bedeschi, S. Behari, G. Bellettini, J. Bellinger, D. Benjamin, A. Beretvas, A. Bhatti, K. R. Bland, B. Blumenfeld, A. Bocci, A. Bodek, D. Bortoletto, J. Boudreau, A. Boveia, L. Brigliadori, C. Bromberg, E. Brucken, J. Budagov, H. S. Budd, K. Burkett, G. Busetto, P. Bussey, P. Butti, A. Buzatu, A. Calamba, S. Camarda, M. Campanelli, F. Canelli, B. Carls, D. Carlsmith, R. Carosi, S. Carrillo, B. Casal, M. Casarsa, A. Castro, P. Catastini, D. Cauz, V. Cavaliere, A. Cerri, L. Cerrito, Y. C. Chen, M. Chertok, G. Chiarelli, G. Chlachidze, K. Cho, D. Chokheli, A. Clark, C. Clarke, M. E. Convery, J. Conway, M. Corbo, M. Cordelli, C. A. Cox, D. J. Cox, M. Cremonesi, D. Cruz, J. Cuevas, R. Culbertson, N. d'Ascenzo, M. Datta, P. de Barbaro, L. Demortier, M. Deninno, F. Devoto, M. D'Errico, A. Di Canto, B. Di Ruzza, J. R. Dittmann, M. D'Onofrio, S. Donati, M. Dorigo, A. Driutti, K. Ebina, R. Edgar, R. Erbacher, S. Errede, B. Esham, S. Farrington, J. P. Fernández Ramos, R. Field, G. Flanagan, R. Forrest, M. Franklin, J. C. Freeman, H. Frisch, Y. Funakoshi, C. Galloni, A. F. Garfinkel, P. Garosi, H. Gerberich, E. Gerchtein, S. Giagu, V. Giakoumopoulou, K. Gibson, C. M. Ginsburg, N. Giokaris, P. Giromini, V. Glagolev, D. Glenzinski, M. Gold, D. Goldin, A. Golossanov, G. Gomez, G. Gomez-Ceballos, M. Goncharov, O. González López, I. Gorelov, A. T. Goshaw, K. Goulianos, E. Gramellini, C. Grosso-Pilcher, J. Guimaraes da Costa, S. R. Hahn, J. Y. Han, F. Happacher, K. Hara, M. Hare, R. F. Harr, T. Harrington-Taber, K. Hatakeyama, C. Hays, J. Heinrich, M. Herndon, A. Hocker, Z. Hong, W. Hopkins, S. Hou, R. E. Hughes, U. Husemann, M. Hussein, J. Huston, G. Introzzi, M. Iori, A. Ivanov, E. James, D. Jang, B. Jayatilaka, E. J. Jeon, S. Jindariani, M. Jones, K. K. Joo, S. Y. Jun, T. R. Junk, M. Kambeitz, T. Kamon, P. E. Karchin, A. Kasmi, Y. Kato, W. Ketchum, J. Keung, B. Kilminster, D. H. Kim, H. S. Kim, J. E. Kim, M. J. Kim, S. B. Kim, S. H. Kim, Y. K. Kim, Y. J. Kim, N. Kimura, M. Kirby, K. Kondo, D. J. Kong, J. Konigsberg, A. V. Kotwal, M. Kreps, J. Kroll, M. Kruse, T. Kuhr, M. Kurata, A. T. Laasanen, S. Lammel, M. Lancaster, K. Lannon, G. Latino, H. S. Lee, J. S. Lee, S. Leo, S. Leone, J. D. Lewis, A. Limosani, E. Lipeles, A. Lister, Q. Liu, T. Liu, S. Lockwitz, A. Loginov, A. Lucà, D. Lucchesi, J. Lueck, P. Lujan, P. Lukens, G. Lungu, J. Lys, R. Lysak, R. Madrak, P. Maestro, S. Malik, G. Manca, A. Manousakis-Katsikakis, L. Marchese, F. Margaroli, P. Marino, K. Matera, M. E. Mattson, A. Mazzacane, P. Mazzanti, R. McNulty, A. Mehta, P. Mehtala, C. Mesropian, T. Miao, D. Mietlicki, A. Mitra, H. Miyake, S. Moed, N. Moggi, C. S. Moon, R. Moore, M. J. Morello, A. Mukherjee, Th. Muller, P. Murat, M. Mussini, J. Nachtman, Y. Nagai, J. Naganoma, I. Nakano, A. Napier, J. Nett, T. Nigmanov, L. Nodulman, S. Y. Noh, O. Norniella, L. Oakes, S. H. Oh, Y. D. Oh, T. Okusawa, R. Orava, L. Ortolan, C. Pagliarone, E. Palencia, P. Palni, V. Papadimitriou, W. Parker, G. Pauletta, M. Paulini, C. Paus, T. J. Phillips, G. Piacentino, E. Pianori, J. Pilot, K. Pitts, C. Plager, L. Pondrom, S. Poprocki, K. Potamianos, F. Prokoshin, A. Pranko, F. Ptohos, G. Punzi, I. Redondo Fernández, P. Renton, M. Rescigno, F. Rimondi, L. Ristori, A. Robson, T. Rodriguez, S. Rolli, M. Ronzani, R. Roser, J. L. Rosner, F. Ruffini, A. Ruiz, J. Russ, V. Rusu, W. K. Sakumoto, Y. Sakurai, L. Santi, K. Sato, V. Saveliev, A. Savoy-Navarro, P. Schlabach, E. E. Schmidt, T. Schwarz, L. Scodellaro, F. Scuri, S. Seidel, Y. Seiya, A. Semenov, F. Sforza, S. Z. Shalhout, T. Shears, P. F. Shepard, M. Shimojima, M. Shochet, I. Shreyber-Tecker, A. Simonenko, P. Sinervo, K. Sliwa, J. R. Smith, F. D. Snider, V. Sorin, H. Song, M. Stancari, R. St. Denis, D. Stentz, J. Strologas, Y. Sudo, A. Sukhanov, I. Suslov, K. Takemasa, Y. Takeuchi, J. Tang, M. Tecchio, P. K. Teng, J. Thom, E. Thomson, V. Thukral, D. Toback, S. Tokar, K. Tollefson, T. Tomura, D. Tonelli, S. Torre, D. Torretta, P. Totaro, M. Trovato, F. Ukegawa, S. Uozumi, F. Vázquez, G. Velev, C. Vellidis, C. Vernieri, M. Vidal, R. Vilar, J. Vizán, M. Vogel, G. Volpi, P. Wagner, R. Wallny, S. M. Wang, D. Waters, W. C. Wester III, D. Whiteson, A. B. Wicklund, S. Wilbur, H. H. Williams, J. S. Wilson, P. Wilson, B. L. Winer, P. Wittich, S. Wolbers, H. Wolfe, T. Wright, X. Wu, Z. Wu, K. Yamamoto, D. Yamato, T. Yang, U. K. Yang, Y. C. Yang, W. -M. Yao, G. P. Yeh, K. Yi, J. Yoh, K. Yorita, T. Yoshida, G. B. Yu, I. Yu, A. M. Zanetti, Y. Zeng, C. Zhou, S. Zucchelli

A measurement of the inclusive production cross section of isolated prompt photons in proton-antiproton collisions at center-of-mass energy $\sqrt{s}$=1.96TeV is presented. The results are obtained using the full Run II data sample collected with the Collider Detector at the Fermilab Tevatron, which corresponds to an integrated luminosity of 9. Read More

Fine-grained recognition is a challenging task due to the small intra-category variances. Most of top-performing fine-grained recognition methods leverage parts of objects for better performance. Therefore, part annotations which are extremely computationally expensive are required. Read More

This paper describes how semantic indexing can help to generate a contextual overview of topics and visually compare clusters of articles. The method was originally developed for an innovative information exploration tool, called Ariadne, which operates on bibliographic databases with tens of millions of records. In this paper, the method behind Ariadne is further developed and applied to the research question of the special issue "Same data, different results" - the better understanding of topic (re-)construction by different bibliometric approaches. Read More

The key finding in the DNA double helix model is the specific pairing or binding between nucleotides A-T and C-G, and the pairing rules are the molecule basis of genetic code. Unfortunately, no such rules have been discovered for proteins. Here we show that similar rules and intrinsic sequence patterns between intra-protein binding peptide fragments do exist, and they can be extracted using a deep learning algorithm. Read More

According to the conjecture "complexity equals action", the complexity of a holographic state is equal to the action of a Wheeler-de Witt (WdW) patch of black holes in anti-de Sitter (AdS) space. In this paper we calculate the action growth of charged black holes with a single horizon, paying attention to the contribution from a spacelike singularity inside the horizon. We consider two kinds of such charged black holes, one is a charged dilaton black hole, and the other is a Born-Infeld black hole with $\beta^2 Q^2<1/4$. Read More

Machine learning techniques, namely convolutional neural networks (CNN) and regression forests, have recently shown great promise in performing 6-DoF localization of monocular images. However, in most cases image-sequences, rather only single images, are readily available. To this extent, none of the proposed learning-based approaches exploit the valuable constraint of temporal smoothness, often leading to situations where the per-frame error is larger than the camera motion. Read More

We theoretically study the width of the s-wave confinement-induced resonance (CIR) in quasi-one-dimensional atomic gases under tunable transversely anisotropic confinement. We find that the width of the CIR can be tuned by varying the transverse anisotropy. The change in the width of the CIR can manifest itself in the position of the discontinuity in the interaction energy density, which can be probed experimentally. Read More

Besides independent learning, human learning process is highly improved by summarizing what has been learned, communicating it with peers, and subsequently fusing knowledge from different sources to assist the current learning goal. This collaborative learning procedure ensures that the knowledge is shared, continuously refined, and concluded from different perspectives to construct a more profound understanding. The idea of knowledge transfer has led to many advances in machine learning and data mining, but significant challenges remain, especially when it comes to reinforcement learning, heterogeneous model structures, and different learning tasks. Read More

In the last two decades, nanostructuring paradigm has been successfully applied in a wide range of thermoelectric materials, resulting in significant reduction in thermal conductivity and superior thermoelectric performance. These advances, however, have been largely accomplished with no direct investigation of the local thermoelectric transport in nanostructured materials, and there still lacks an effective method that directly links the macroscopic thermoelectric performance to the local microstructures and properties. In this work, we show that local thermal conductivity can be mapped quantitatively with high accuracy, nanometer resolution, and one-to-one correspondence to the microstructure, using filled skutterudite with three-phase composition as a model system. Read More

The black hole information paradox is related to the area of event horizon, and potentially to the volume and singularity behind it. One example is the the complexity/volume duality conjectured by Stanford and Susskind. Accepting the proposal of Christodoulou and Rovelli, we calculate the maximal volume inside regular black holes, which are free of curvature singularity, in asymptotically flat and anti-de Sitter spacetimes respectively. Read More

Document clustering is generally the first step for topic identification. Since many clustering methods operate on the similarities between documents, it is important to build representations of these documents which keep their semantics as much as possible and are also suitable for efficient similarity calculation. The metadata of articles in the Astro dataset contribute to a semantic matrix, which uses a vector space to capture the semantics of entities derived from these articles and consequently supports the contextual exploration of these entities in LittleAriadne. Read More

We address the statistical and optimization impacts of using classical or Hessian sketch to approximately solve the Matrix Ridge Regression (MRR) problem. Prior research has considered the effects of classical sketch on least squares regression (LSR), a strictly simpler problem. We establish that classical sketch has a similar effect upon the optimization properties of MRR as it does on those of LSR---namely, it recovers nearly optimal solutions. Read More

Today's storage systems expose abstractions which are either too low-level (e.g., key-value store, raw-block store) that they require developers to re-invent the wheels, or too high-level (e. Read More

The Beijing-Arizona Sky Survey (BASS) is a new wide-field legacy imaging survey in the northern Galactic cap using the 2.3m Bok telescope. The survey will cover about 5400 deg$^2$ in the $g$ and $r$ bands, and the expected 5$\sigma$ depths (corrected for the Galactic extinction) in the two bands are 24. Read More

The symmetries of surfaces which can be embedded into the symmetries of the 3-dimensional Euclidean space $\mathbb{R}^3$ are easier to feel by human's intuition. We give the maximum order of finite group actions on $(\mathbb{R}^3, \Sigma)$ among all possible embedded closed/bordered surfaces with given geometric/algebraic genus $>1$ in $\mathbb{R}^3$. We also identify the topological types of the bordered surfaces realizing the maximum order, and find simple representative embeddings for such surfaces. Read More

The electronic structure of a crystalline solid is largely determined by its lattice structure. Recent advances in van der Waals solids, artificial crystals with controlled stacking of two-dimensional (2D) atomic films, have enabled the creation of materials with novel electronic structures. In particular, stacking graphene on hexagonal boron nitride (hBN) introduces moir\'e superlattice that fundamentally modifies graphene's band structure and gives rise to secondary Dirac points (SDPs). Read More

The explosion mechanism of broad-lined type Ic supernovae (SNe Ic-BL) is not very well understood despite their discovery more than two decades ago. Recently a serious confrontation of SNe Ic-BL with the magnetar (plus 56Ni) model was carried out following previous suggestions. Strong evidence for magnetar formation was found for the well-observed SNe Ic-BL 1998bw and 2002ap. Read More

The demand on mobile electronics to continue to shrink in size while increase in efficiency drives the demand on the internal passive components to do the same. Power amplifiers require inductors with small form factors, high quality factors, and high operating frequency in the single-digit GHz range. This work explores the use of magnetic materials to satisfy the needs of power amplifier inductor applications. Read More

This paper is an extension of a recently developed high order finite difference method for the wave equation on a grid with non-conforming interfaces. The stability proof of the previous method relies on the interface operators being norm-contracting, which is satisfied by the second and fourth order operators, but not by the sixth order operator. In this paper, we present a new way of numerical interface couplings to remove the norm-contracting requirement, so that the sixth order accurate scheme is also provably stable. Read More

With the motivation of searching for new superconductors in the Mg-B system, we performed ab initio evolutionary searches for all the stable compounds in this binary system in the pressure range of 0-200 GPa. We found a number of previously unknown, yet thermodynamically stable, compositions: MgB$_3$, Mg$_3$B$_{10}$ and Mg$_2$B$_7$. We observed transition from semiconducting Pnma-MgB$_4$ to superconducting C2/m-MgB$_4$ at the elevated pressure of 31 GPa. Read More

When using a finite difference method to solve an initial-boundary-value problem, the truncation error is often larger at a few grid points near boundaries than in the interior. Normal mode analysis is a powerful tool to analyze the effect of the large truncation error near boundaries on the overall convergence rate, and it has been used in many previous works for different equations. However, existing work only concerns problems in one space dimension. Read More

In this paper we present a novel approach for depth map enhancement from an RGB-D video sequence. The basic idea is to exploit the shading information in the color image. Instead of making assumption about surface albedo or controlled object motion and lighting, we use the lighting variations introduced by casual object movement. Read More

Round-robin-differential-phase (RRDPS) quantum key distribution (QKD) protocol has attracted intensive studies due to its distinct security characteristic, e.g., information leakage in RRDPS can be bounded without learning error rate of key bits. Read More

Authors: M. Ablikim1, M. N. Achasov2, X. C. Ai3, O. Albayrak4, M. Albrecht5, D. J. Ambrose6, A. Amoroso7, F. F. An8, Q. An9, J. Z. Bai10, R. Baldini Ferroli11, Y. Ban12, D. W. Bennett13, J. V. Bennett14, M. Bertani15, D. Bettoni16, J. M. Bian17, F. Bianchi18, E. Boger19, I. Boyko20, R. A. Briere21, H. Cai22, X. Cai23, O. Cakir24, A. Calcaterra25, G. F. Cao26, S. A. Cetin27, J. F. Chang28, G. Chelkov29, G. Chen30, H. S. Chen31, H. Y. Chen32, J. C. Chen33, M. L. Chen34, S. Chen35, S. J. Chen36, X. Chen37, X. R. Chen38, Y. B. Chen39, H. P. Cheng40, X. K. Chu41, G. Cibinetto42, H. L. Dai43, J. P. Dai44, A. Dbeyssi45, D. Dedovich46, Z. Y. Deng47, A. Denig48, I. Denysenko49, M. Destefanis50, F. De Mori51, Y. Ding52, C. Dong53, J. Dong54, L. Y. Dong55, M. Y. Dong56, Z. L. Dou57, S. X. Du58, P. F. Duan59, J. Z. Fan60, J. Fang61, S. S. Fang62, X. Fang63, Y. Fang64, R. Farinelli65, L. Fava66, O. Fedorov67, F. Feldbauer68, G. Felici69, C. Q. Feng70, E. Fioravanti71, M. Fritsch72, C. D. Fu73, Q. Gao74, X. L. Gao75, X. Y. Gao76, Y. Gao77, Z. Gao78, I. Garzia79, K. Goetzen80, L. Gong81, W. X. Gong82, W. Gradl83, M. Greco84, M. H. Gu85, Y. T. Gu86, Y. H. Guan87, A. Q. Guo88, L. B. Guo89, R. P. Guo90, Y. Guo91, Y. P. Guo92, Z. Haddadi93, A. Hafner94, S. Han95, X. Q. Hao96, F. A. Harris97, K. L. He98, T. Held99, Y. K. Heng100, Z. L. Hou101, C. Hu102, H. M. Hu103, J. F. Hu104, T. Hu105, Y. Hu106, G. S. Huang107, J. S. Huang108, X. T. Huang109, X. Z. Huang110, Y. Huang111, Z. L. Huang112, T. Hussain113, Q. Ji114, Q. P. Ji115, X. B. Ji116, X. L. Ji117, L. W. Jiang118, X. S. Jiang119, X. Y. Jiang120, J. B. Jiao121, Z. Jiao122, D. P. Jin123, S. Jin124, T. Johansson125, A. Julin126, N. Kalantar-Nayestanaki127, X. L. Kang128, X. S. Kang129, M. Kavatsyuk130, B. C. Ke131, P. Kiese132, R. Kliemt133, B. Kloss134, O. B. Kolcu135, B. Kopf136, M. Kornicer137, A. Kupsc138, W. Kühn139, J. S. Lange140, M. Lara141, P. Larin142, C. Leng143, C. Li144, Cheng Li145, D. M. Li146, F. Li147, F. Y. Li148, G. Li149, H. B. Li150, H. J. Li151, J. C. Li152, Jin Li153, K. Li154, K. Li155, Lei Li156, P. R. Li157, Q. Y. Li158, T. Li159, W. D. Li160, W. G. Li161, X. L. Li162, X. N. Li163, X. Q. Li164, Y. B. Li165, Z. B. Li166, H. Liang167, Y. F. Liang168, Y. T. Liang169, G. R. Liao170, D. X. Lin171, B. Liu172, B. J. Liu173, C. X. Liu174, D. Liu175, F. H. Liu176, Fang Liu177, Feng Liu178, H. B. Liu179, H. H. Liu180, H. H. Liu181, H. M. Liu182, J. Liu183, J. B. Liu184, J. P. Liu185, J. Y. Liu186, K. Liu187, K. Y. Liu188, L. D. Liu189, P. L. Liu190, Q. Liu191, S. B. Liu192, X. Liu193, Y. B. Liu194, Z. A. Liu195, Zhiqing Liu196, H. Loehner197, X. C. Lou198, H. J. Lu199, J. G. Lu200, Y. Lu201, Y. P. Lu202, C. L. Luo203, M. X. Luo204, T. Luo205, X. L. Luo206, X. R. Lyu207, F. C. Ma208, H. L. Ma209, L. L. Ma210, M. M. Ma211, Q. M. Ma212, T. Ma213, X. N. Ma214, X. Y. Ma215, Y. M. Ma216, F. E. Maas217, M. Maggiora218, Y. J. Mao219, Z. P. Mao220, S. Marcello221, J. G. Messchendorp222, J. Min223, R. E. Mitchell224, X. H. Mo225, Y. J. Mo226, C. Morales Morales227, N. Yu. Muchnoi228, H. Muramatsu229, Y. Nefedov230, F. Nerling231, I. B. Nikolaev232, Z. Ning233, S. Nisar234, S. L. Niu235, X. Y. Niu236, S. L. Olsen237, Q. Ouyang238, S. Pacetti239, Y. Pan240, P. Patteri241, M. Pelizaeus242, H. P. Peng243, K. Peters244, J. Pettersson245, J. L. Ping246, R. G. Ping247, R. Poling248, V. Prasad249, H. R. Qi250, M. Qi251, S. Qian252, C. F. Qiao253, L. Q. Qin254, N. Qin255, X. S. Qin256, Z. H. Qin257, J. F. Qiu258, K. H. Rashid259, C. F. Redmer260, M. Ripka261, G. Rong262, Ch. Rosner263, X. D. Ruan264, A. Sarantsev265, M. Savrié266, K. Schoenning267, S. Schumann268, W. Shan269, M. Shao270, C. P. Shen271, P. X. Shen272, X. Y. Shen273, H. Y. Sheng274, M. Shi275, W. M. Song276, X. Y. Song277, S. Sosio278, S. Spataro279, G. X. Sun280, J. F. Sun281, S. S. Sun282, X. H. Sun283, Y. J. Sun284, Y. Z. Sun285, Z. J. Sun286, Z. T. Sun287, C. J. Tang288, X. Tang289, I. Tapan290, E. H. Thorndike291, M. Tiemens292, M. Ullrich293, I. Uman294, G. S. Varner295, B. Wang296, B. L. Wang297, D. Wang298, D. Y. Wang299, K. Wang300, L. L. Wang301, L. S. Wang302, M. Wang303, P. Wang304, P. L. Wang305, S. G. Wang306, W. Wang307, W. P. Wang308, X. F. Wang309, Y. Wang310, Y. D. Wang311, Y. F. Wang312, Y. Q. Wang313, Z. Wang314, Z. G. Wang315, Z. H. Wang316, Z. Y. Wang317, Z. Y. Wang318, T. Weber319, D. H. Wei320, J. B. Wei321, P. Weidenkaff322, S. P. Wen323, U. Wiedner324, M. Wolke325, L. H. Wu326, L. J. Wu327, Z. Wu328, L. Xia329, L. G. Xia330, Y. Xia331, D. Xiao332, H. Xiao333, Z. J. Xiao334, Y. G. Xie335, Q. L. Xiu336, G. F. Xu337, J. J. Xu338, L. Xu339, Q. J. Xu340, Q. N. Xu341, X. P. Xu342, L. Yan343, W. B. Yan344, W. C. Yan345, Y. H. Yan346, H. J. Yang347, H. X. Yang348, L. Yang349, Y. X. Yang350, M. Ye351, M. H. Ye352, J. H. Yin353, B. X. Yu354, C. X. Yu355, J. S. Yu356, C. Z. Yuan357, W. L. Yuan358, Y. Yuan359, A. Yuncu360, A. A. Zafar361, A. Zallo362, Y. Zeng363, Z. Zeng364, B. X. Zhang365, B. Y. Zhang366, C. Zhang367, C. C. Zhang368, D. H. Zhang369, H. H. Zhang370, H. Y. Zhang371, J. Zhang372, J. J. Zhang373, J. L. Zhang374, J. Q. Zhang375, J. W. Zhang376, J. Y. Zhang377, J. Z. Zhang378, K. Zhang379, L. Zhang380, S. Q. Zhang381, X. Y. Zhang382, Y. Zhang383, Y. H. Zhang384, Y. N. Zhang385, Y. T. Zhang386, Yu Zhang387, Z. H. Zhang388, Z. P. Zhang389, Z. Y. Zhang390, G. Zhao391, J. W. Zhao392, J. Y. Zhao393, J. Z. Zhao394, Lei Zhao395, Ling Zhao396, M. G. Zhao397, Q. Zhao398, Q. W. Zhao399, S. J. Zhao400, T. C. Zhao401, Y. B. Zhao402, Z. G. Zhao403, A. Zhemchugov404, B. Zheng405, J. P. Zheng406, W. J. Zheng407, Y. H. Zheng408, B. Zhong409, L. Zhou410, X. Zhou411, X. K. Zhou412, X. R. Zhou413, X. Y. Zhou414, K. Zhu415, K. J. Zhu416, S. Zhu417, S. H. Zhu418, X. L. Zhu419, Y. C. Zhu420, Y. S. Zhu421, Z. A. Zhu422, J. Zhuang423, L. Zotti424, B. S. Zou425, J. H. Zou426
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We present an amplitude analysis of the decay $D^{0} \rightarrow K^{-} \pi^{+} \pi^{+} \pi^{-}$ based on a data sample of 2.93 ${\mbox{\,fb}^{-1}}$ acquired by the BESIII detector at the $\psi(3770)$ resonance. With a nearly background free sample of about 16000 events, we investigate the substructure of the decay and determine the relative fractions and the phases among the different intermediate processes. Read More

There has been significant interest in studying security games for modeling the interplay of attacks and defenses on various systems involving critical infrastructure, financial system security, political campaigns, and civil safeguarding. However, existing security game models typically either assume additive utility functions, or that the attacker can attack only one target. Such assumptions lead to tractable analysis, but miss key inherent dependencies that exist among different targets in current complex networks. Read More