J. Gao - SDU

J. Gao
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J. Gao
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SDU
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Computer Science - Computer Vision and Pattern Recognition (13)
 
Computer Science - Computation and Language (12)
 
Computer Science - Learning (12)
 
Computer Science - Artificial Intelligence (8)
 
Quantum Physics (5)
 
Physics - Mesoscopic Systems and Quantum Hall Effect (4)
 
Statistics - Machine Learning (3)
 
Physics - Physics and Society (3)
 
Computer Science - Cryptography and Security (2)
 
High Energy Physics - Phenomenology (2)
 
Physics - Instrumentation and Detectors (2)
 
Computer Science - Information Retrieval (1)
 
Solar and Stellar Astrophysics (1)
 
Physics - Strongly Correlated Electrons (1)
 
Nonlinear Sciences - Adaptation and Self-Organizing Systems (1)
 
Instrumentation and Methods for Astrophysics (1)
 
Cosmology and Nongalactic Astrophysics (1)
 
Physics - Accelerator Physics (1)
 
High Energy Physics - Experiment (1)
 
Computer Science - Data Structures and Algorithms (1)
 
Computer Science - Neural and Evolutionary Computing (1)
 
Computer Science - Robotics (1)
 
Computer Science - Performance (1)
 
Physics - Optics (1)
 
Physics - Computational Physics (1)
 
Physics - Superconductivity (1)
 
Physics - Materials Science (1)
 
Mathematics - Analysis of PDEs (1)
 
Physics - Disordered Systems and Neural Networks (1)
 
Computer Science - Networking and Internet Architecture (1)
 
Computer Science - Computer Science and Game Theory (1)
 
Mathematics - Optimization and Control (1)
 
Nuclear Theory (1)

Publications Authored By J. Gao

Subspace data representation has recently become a common practice in many computer vision tasks. It demands generalizing classical machine learning algorithms for subspace data. Low-Rank Representation (LRR) is one of the most successful models for clustering vectorial data according to their subspace structures. Read More

The observed frequency of the longest proper prefix, the longest proper suffix, and the longest infix of a word $w$ in a given sequence $x$ can be used for classifying $w$ as avoided or overabundant. The definitions used for the expectation and deviation of $w$ in this statistical model were described and biologically justified by Brendel et al. (J Biomol Struct Dyn 1986). Read More

Adaptive traffic signal control, which adjusts traffic signal timing according to real-time traffic, has been shown to be an effective method to reduce traffic congestion. Available works on adaptive traffic signal control make responsive traffic signal control decisions based on human-crafted features (e.g. Read More

This paper focuses on temporal localization of actions in untrimmed videos. Existing methods typically train classifiers for a pre-defined list of actions and apply them in a sliding window fashion. However, activities in the wild consist of a wide combination of actors, actions and objects; it is difficult to design a proper activity list that meets users' needs. Read More

Temporal action detection in long videos is an important problem. State-of-the-art methods address this problem by applying action classifiers on sliding windows. Although sliding windows may contain an identifiable portion of the actions, they may not necessarily cover the entire action instance, which would lead to inferior performance. Read More

Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos. However, such learning algorithms particularly on high-dimensional Grassmann manifold always involve with significantly high computational cost, which seriously limits the applicability of learning on Grassmann manifold in more wide areas. In this research, we propose an unsupervised dimensionality reduction algorithm on Grassmann manifold based on the Locality Preserving Projections (LPP) criterion. Read More

Quantum walks, in virtue of the coherent superposition and quantum interference, possess the exponential superiority over its classical counterpart in applications of quantum searching and quantum simulation. A straitforward physical implementation involving merely photonic source, linear evolution network and detection make it very appealing, in light of the stringent requirements of universal quantum computing. The quantum enhanced power is highly related to the state space of quantum walks, which can be expanded by enlarging the dimension of evolution network and/or photon number. Read More

In this paper, we investigate the well-posedness theory for the MHD boundary layer system in two-dimensional space. The boundary layer equations are governed by the Prandtl type equations that are derived from the full incompressible MHD system with non-slip boundary condition on the velocity, perfectly conducting condition on the magnetic field, and Dirichlet boundary condition on the temperature when the viscosity coefficient depends on the temperature. To derive the Prandtl type boundary layer system, we require all the hydrodynamic Reynolds numbers, magnetic Reynolds numbers and Nusselt numbers tend to infinity at the same rate. Read More

Quantum memory, capable of stopping flying photons and storing their quantum coherence, is essential for scalable quantum technologies. A broadband quantum memory operating at room temperature will enable building large-scale quantum systems for real-life applications, for instance, high-speed quantum repeater for long-distance quantum communication and synchronised multi-photon quantum sources for quantum computing and quantum simulation. Albeit advances of pushing bandwidth from narrowband to broadband and storage media from ultra-cold atomic gas to room-temperature atomic vapour, due to either intrinsic high noises or short lifetime, it is still challenging to find a room-temperature broadband quantum memory beyond conceptional demonstration. Read More

Sparse Subspace Clustering (SSC) has been used extensively for subspace identification tasks due to its theoretical guarantees and relative ease of implementation. However SSC has quadratic computation and memory requirements with respect to the number of input data points. This burden has prohibited SSCs use for all but the smallest datasets. Read More

In machine learning it is common to interpret each data point as a vector in Euclidean space. However the data may actually be functional i.e. Read More

Discriminant Correlation Filters (DCF) based methods now become a kind of dominant approach to online object tracking. The features used in these methods, however, are either based on hand-crafted features like HoGs, or convolutional features trained independently from other tasks like image classification. In this work, we present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously. Read More

In this paper we present Collaborative Low-Rank Subspace Clustering. Given multiple observations of a phenomenon we learn a unified representation matrix. This unified matrix incorporates the features from all the observations, thus increasing the discriminative power compared with learning the representation matrix on each observation separately. Read More

Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example, the agent needs to reserve a hotel and book a flight so that there leaves enough time for commute between arrival and hotel check-in. This paper addresses this challenge by formulating the task in the mathematical framework of options over Markov Decision Processes (MDPs), and proposing a hierarchical deep reinforcement learning approach to learning a dialogue manager that operates at different temporal scales. Read More

We present broadband parametric amplifiers based on the kinetic inductance of superconducting NbTiN thin films in an artificial (lumped-element) transmission line architecture. We demonstrate two amplifier designs implementing different phase matching techniques: periodic impedance loadings, and resonator phase shifters placed periodically along the transmission line. Our design offers several advantages over previous CPW-based amplifiers, including intrinsic 50 ohm characteristic impedance, natural suppression of higher pump harmonics, lower required pump power, and shorter total trace length. Read More

The covariant chiral kinetic equation (CCKE) is derived from the 4-dimensional Wigner function by an improved perturbative method under the static equilibrium conditions. The chiral kinetic equation in 3-dimensions can be obtained by intergation over the time component of the 4-momentum. There is freedom to add more terms to the CCKE allowed by conservation laws. Read More

Quantum coherence defined by the superposition behavior of a particle beyond the classical realm, serves as one of the most fundamental features in quantum mechanics. Meanwhile, the wave-particle duality phenomenon, which shares the same origin, therefore has a strong relationship with the quantum coherence. Recently an elegant relation between the quantum coherence and the path information has been theoretically derived [Phys. Read More

Recent studies showed that the in-plane and inter-plane thermal conductivities of two-dimensional (2D) MoS2 are low, posing a significant challenge in heat management in MoS2-based electronic devices. To address this challenge, we design the interfaces between MoS2 and graphene by fully utilizing graphene, a 2D material with an ultra-high thermal conduction. We first perform ab initio atomistic simulations to understand the bonding nature and structure stability of the interfaces. Read More

Language understanding is a key component in a spoken dialogue system. In this paper, we investigate how the language understanding module influences the dialogue system performance by conducting a series of systematic experiments on a task-oriented neural dialogue system in a reinforcement learning based setting. The empirical study shows that among different types of language understanding errors, slot-level errors can have more impact on the overall performance of a dialogue system compared to intent-level errors. Read More

Temporal Action Proposal (TAP) generation is an important problem, as fast and accurate extraction of semantically important (e.g. human actions) segments from untrimmed videos is an important step for large-scale video analysis. Read More

Recently, the existence of local magnetic moment in a hydrogen adatom on graphene has been confirmed experimentally [Gonz\'{a}lez-Herrero et al., Science, 2016, 352, 437]. Inspired by this breakthrough, we theoretically investigate the top-site adatom on trilayer graphene (TLG) by solving the Anderson impurity model via self-consistent mean field method. Read More

Cascading failures in complex systems have been studied extensively using two different models: $k$-core percolation and interdependent networks. We combine the two models into a general model, solve it analytically and validate our theoretical results through extensive simulations. We also study the complete phase diagram of the percolation transition as we tune the average local $k$-core threshold and the coupling between networks. Read More

China has experienced an outstanding economic expansion during the past decades, however, literature on non-monetary metrics that reveal the status of China's regional economic development are still lacking. In this paper, we fill this gap by quantifying the economic complexity of China's provinces through analyzing 25 years' firm data. First, we estimate the regional Economic Complexity Index (ECI), and show that the overall time evolution of provinces' ECI is relatively stable and slow. Read More

Industrial development is the process by which economies learn how to produce new products and services. But how do economies learn? And who do they learn from? The literature on economic geography and economic development has emphasized two learning channels: inter-industry learning, which involves learning from related industries; and inter-regional learning, which involves learning from neighboring regions. Here we use 25 years of data describing the evolution of China's economy between 1990 and 2015--a period when China multiplied its GDP per capita by a factor of ten--to explore how Chinese provinces diversified their economies. Read More

This paper presents an end-to-end learning framework for task-completion neural dialogue systems, which leverages supervised and reinforcement learning with various deep-learning models. The system is able to interface with a structured database, and interact with users for assisting them to access information and complete tasks such as booking movie tickets. Our experiments in a movie-ticket booking domain show the proposed system outperforms a modular-based dialogue system and is more robust to noise produced by other components in the system. Read More

We demonstrate photon counting at 1550 nm wavelength using microwave kinetic inductance detectors (MKIDs) made from TiN/Ti/TiN trilayer films with superconducting transition temperature Tc ~ 1.4 K. The detectors have a lumped-element design with a large interdigitated capacitor (IDC) covered by aluminum and inductive photon absorbers whose volume ranges from 0. Read More

Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such attacks can severely undermine DNN systems, particularly in security-sensitive settings. It was observed that an adversary could easily generate adversarial samples by making a small perturbation on irrelevant feature dimensions that are unnecessary for the current classification task. Read More

In this paper we introduce the novel framework of distributionally robust games. These are multi-player games where each player models the state of nature using a worst-case distribution, also called adversarial distribution. Thus each player's payoff depends on the other players' decisions and on the decision of a virtual player (nature) who selects an adversarial distribution of scenarios. Read More

Estimating multiple sparse Gaussian Graphical Models (sGGMs) jointly for many related tasks (large $K$) under a high-dimensional (large $p$) situation is an important task. Most previous studies for the joint estimation of multiple sGGMs rely on penalized log-likelihood estimators that involve expensive and difficult non-smooth optimizations. We propose a novel approach, FASJEM for \underline{fa}st and \underline{s}calable \underline{j}oint structure-\underline{e}stimation of \underline{m}ultiple sGGMs at a large scale. Read More

Connecting different text attributes associated with the same entity (conflation) is important in business data analytics since it could help merge two different tables in a database to provide a more comprehensive profile of an entity. However, the conflation task is challenging because two text strings that describe the same entity could be quite different from each other for reasons such as misspelling. It is therefore critical to develop a conflation model that is able to truly understand the semantic meaning of the strings and match them at the semantic level. Read More

Neural network models are capable of generating extremely natural sounding conversational interactions. Nevertheless, these models have yet to demonstrate that they can incorporate content in the form of factual information or entity-grounded opinion that would enable them to serve in more task-oriented conversational applications. This paper presents a novel, fully data-driven, and knowledge-grounded neural conversation model aimed at producing more contentful responses without slot filling. Read More

The popularity of image sharing on social media and the engagement it creates between users reflects the important role that visual context plays in everyday conversations. We present a novel task, Image-Grounded Conversations (IGC), in which natural-sounding conversations are generated about a shared image. To benchmark progress, we introduce a new multiple-reference dataset of crowd-sourced, event-centric conversations on images. Read More

Robot Coverage Path planning (i.e., provide full coverage of a given domain by one or multiple robots) is a classical problem in the field of robotics and motion planning. Read More

Nonclassical beams in high order spatial modes have attracted much interest but they exhibit much less squeezing and entanglement than the fundamental spatial modes, limiting their applications. We experimentally demonstrate the relation between pump modes and entanglement of first-order HG modes (HG10 entangled states) in a type II OPO and show that the maximum entanglement of high order spatial modes can be obtained by optimizing the pump spatial mode. To our knowledge, this is the first time to report this. Read More

Collective behaviors of populations of coupled oscillators have attracted much attention in recent years. In this paper, an order parameter approach is proposed to study the low-dimensional dynam- ical mechanism of collective synchronizations by adopting the star-topology of coupled oscillators as a prototype system. The order parameter equation of star-linked phase oscillators can be obtained in terms of the Watanabe-Strogatz transformation, Ott-Antonsen ansatz, and the ensemble order parameter approach. Read More

We theoretically examine the possible spin ordered states in zigzag graphene nanoribbon in a large supercell by the self-consistent mean field method as well as the first principle calculation. In addition to the well-known anti-ferromagnetic (AF) and ferromagnetic (FM) edge states, we find that there are also some excited spin density wave (ESDW) states, the energy of which are close to the AF edge state (ground state). We thus argue that these ESDW states may coexist in experiment when the temperature is not too low. Read More

Despite widespread interests in reinforcement-learning for task-oriented dialogue systems, several obstacles can frustrate research and development progress. First, reinforcement learners typically require interaction with the environment, so conventional dialogue corpora cannot be used directly. Second, each task presents specific challenges, requiring separate corpus of task-specific annotated data. Read More

For decades ever since the early detection in the 1990s of the emission spectral features of crystalline silicates in oxygen-rich evolved stars, there is a long-standing debate on whether the crystallinity of the silicate dust correlates with the stellar mass loss rate. To investigate the relation between the silicate crystallinities and the mass loss rates of evolved stars, we carry out a detailed analysis of 28 nearby oxygen-rich stars. We derive the mass loss rates of these sources by modeling their spectral energy distributions from the optical to the far infrared. Read More

Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utterance. Conventional approaches aggregate separate models of natural language understanding (NLU) and system action prediction (SAP) as a pipeline that is sensitive to noisy outputs of error-prone NLU. To address the issues, we propose an end-to-end deep recurrent neural network with limited contextual dialogue memory by jointly training NLU and SAP on DSTC4 multi-domain human-human dialogues. Read More

Most machine learning classifiers, including deep neural networks, are vulnerable to adversarial examples. Such inputs are typically generated by adding small but purposeful modifications that lead to incorrect outputs while imperceptible to human eyes. The goal of this paper is not to introduce a single method, but to make theoretical steps towards fully understanding adversarial examples. Read More

This paper presents our recent work on the design and development of a new, large scale dataset, which we name MS MARCO, for MAchine Reading COmprehension.This new dataset is aimed to overcome a number of well-known weaknesses of previous publicly available datasets for the same task of reading comprehension and question answering. In MS MARCO, all questions are sampled from real anonymized user queries. Read More

A Semantic Compositional Network (SCN) is developed for image captioning, in which semantic concepts (i.e., tags) are detected from the image, and the probability of each tag is used to compose the parameters in a long short-term memory (LSTM) network. Read More

We present a highly frequency multiplexed readout for large-format superconducting detector arrays intended for use in the next generation of balloon-borne and space-based sub-millimeter and far-infrared missions. We will demonstrate this technology on the upcoming NASA Next Generation Balloon-borne Large Aperture Sub-millimeter Telescope (BLAST-TNG) to measure the polarized emission of Galactic dust at wavelengths of 250, 350 and 500 microns. The BLAST-TNG receiver incorporates the first arrays of Lumped Element Kinetic Inductance Detectors (LeKID) along with the first microwave multiplexing readout electronics to fly in a space-like environment and will significantly advance the TRL for these technologies. Read More

Recent studies on knowledge base completion, the task of recovering missing relationships based on recorded relations, demonstrate the importance of learning embeddings from multi-step relations. However, due to the size of knowledge bases, learning multi-step relations directly on top of observed triplets could be costly. Hence, a manually designed procedure is often used when training the models. Read More

In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence - both semantic and syntactic - but might face difficulty remembering long-range dependencies. Intuitively, these long-range dependencies are of semantic nature. Read More

To use deep reinforcement learning in the wild, we might hope for an agent that can avoid catastrophic mistakes. Unfortunately, even in simple environments, the popular deep Q-network (DQN) algorithm is doomed by a Sisyphean curse. Owing to the use of function approximation, these agents may eventually forget experiences as they become exceedingly unlikely under a new policy. Read More

2016Oct
Authors: D. de Florian1, C. Grojean2, F. Maltoni3, C. Mariotti4, A. Nikitenko5, M. Pieri6, P. Savard7, M. Schumacher8, R. Tanaka9, R. Aggleton10, M. Ahmad11, B. Allanach12, C. Anastasiou13, W. Astill14, S. Badger15, M. Badziak16, J. Baglio17, E. Bagnaschi18, A. Ballestrero19, A. Banfi20, D. Barducci21, M. Beckingham22, C. Becot23, G. Bélanger24, J. Bellm25, N. Belyaev26, F. U. Bernlochner27, C. Beskidt28, A. Biekötter29, F. Bishara30, W. Bizon31, N. E. Bomark32, M. Bonvini33, S. Borowka34, V. Bortolotto35, S. Boselli36, F. J. Botella37, R. Boughezal38, G. C. Branco39, J. Brehmer40, L. Brenner41, S. Bressler42, I. Brivio43, A. Broggio44, H. Brun45, G. Buchalla46, C. D. Burgard47, A. Calandri48, L. Caminada49, R. Caminal Armadans50, F. Campanario51, J. Campbell52, F. Caola53, C. M. Carloni Calame54, S. Carrazza55, A. Carvalho56, M. Casolino57, O. Cata58, A. Celis59, F. Cerutti60, N. Chanon61, M. Chen62, X. Chen63, B. Chokoufé Nejad64, N. Christensen65, M. Ciuchini66, R. Contino67, T. Corbett68, R. Costa69, D. Curtin70, M. Dall'Osso71, A. David72, S. Dawson73, J. de Blas74, W. de Boer75, P. de Castro Manzano76, C. Degrande77, R. L. Delgado78, F. Demartin79, A. Denner80, B. Di Micco81, R. Di Nardo82, S. Dittmaier83, A. Dobado84, T. Dorigo85, F. A. Dreyer86, M. Dührssen87, C. Duhr88, F. Dulat89, K. Ecker90, K. Ellis91, U. Ellwanger92, C. Englert93, D. Espriu94, A. Falkowski95, L. Fayard96, R. Feger97, G. Ferrera98, A. Ferroglia99, N. Fidanza100, T. Figy101, M. Flechl102, D. Fontes103, S. Forte104, P. Francavilla105, E. Franco106, R. Frederix107, A. Freitas108, F. F. Freitas109, F. Frensch110, S. Frixione111, B. Fuks112, E. Furlan113, S. Gadatsch114, J. Gao115, Y. Gao116, M. V. Garzelli117, T. Gehrmann118, R. Gerosa119, M. Ghezzi120, D. Ghosh121, S. Gieseke122, D. Gillberg123, G. F. Giudice124, E. W. N. Glover125, F. Goertz126, D. Gonçalves127, J. Gonzalez-Fraile128, M. Gorbahn129, S. Gori130, C. A. Gottardo131, M. Gouzevitch132, P. Govoni133, D. Gray134, M. Grazzini135, N. Greiner136, A. Greljo137, J. Grigo138, A. V. Gritsan139, R. Gröber140, S. Guindon141, H. E. Haber142, C. Han143, T. Han144, R. Harlander145, M. A. Harrendorf146, H. B. Hartanto147, C. Hays148, S. Heinemeyer149, G. Heinrich150, M. Herrero151, F. Herzog152, B. Hespel153, V. Hirschi154, S. Hoeche155, S. Honeywell156, S. J. Huber157, C. Hugonie158, J. Huston159, A. Ilnicka160, G. Isidori161, B. Jäger162, M. Jaquier163, S. P. Jones164, A. Juste165, S. Kallweit166, A. Kaluza167, A. Kardos168, A. Karlberg169, Z. Kassabov170, N. Kauer171, D. I. Kazakov172, M. Kerner173, W. Kilian174, F. Kling175, K. Köneke176, R. Kogler177, R. Konoplich178, S. Kortner179, S. Kraml180, C. Krause181, F. Krauss182, M. Krawczyk183, A. Kulesza184, S. Kuttimalai185, R. Lane186, A. Lazopoulos187, G. Lee188, P. Lenzi189, I. M. Lewis190, Y. Li191, S. Liebler192, J. Lindert193, X. Liu194, Z. Liu195, F. J. Llanes-Estrada196, H. E. Logan197, D. Lopez-Val198, I. Low199, G. Luisoni200, P. Maierhöfer201, E. Maina202, B. Mansoulié203, H. Mantler204, M. Mantoani205, A. C. Marini206, V. I. Martinez Outschoorn207, S. Marzani208, D. Marzocca209, A. Massironi210, K. Mawatari211, J. Mazzitelli212, A. McCarn213, B. Mellado214, K. Melnikov215, S. B. Menari216, L. Merlo217, C. Meyer218, P. Milenovic219, K. Mimasu220, S. Mishima221, B. Mistlberger222, S. -O. Moch223, A. Mohammadi224, P. F. Monni225, G. Montagna226, M. Moreno Llácer227, N. Moretti228, S. Moretti229, L. Motyka230, A. Mück231, M. Mühlleitner232, S. Munir233, P. Musella234, P. Nadolsky235, D. Napoletano236, M. Nebot237, C. Neu238, M. Neubert239, R. Nevzorov240, O. Nicrosini241, J. Nielsen242, K. Nikolopoulos243, J. M. No244, C. O'Brien245, T. Ohl246, C. Oleari247, T. Orimoto248, D. Pagani249, C. E. Pandini250, A. Papaefstathiou251, A. S. Papanastasiou252, G. Passarino253, B. D. Pecjak254, M. Pelliccioni255, G. Perez256, L. Perrozzi257, F. Petriello258, G. Petrucciani259, E. Pianori260, F. Piccinini261, M. Pierini262, A. Pilkington263, S. Plätzer264, T. Plehn265, R. Podskubka266, C. T. Potter267, S. Pozzorini268, K. Prokofiev269, A. Pukhov270, I. Puljak271, M. Queitsch-Maitland272, J. Quevillon273, D. Rathlev274, M. Rauch275, E. Re276, M. N. Rebelo277, D. Rebuzzi278, L. Reina279, C. Reuschle280, J. Reuter281, M. Riembau282, F. Riva283, A. Rizzi284, T. Robens285, R. Röntsch286, J. Rojo287, J. C. Romão288, N. Rompotis289, J. Roskes290, R. Roth291, G. P. Salam292, R. Salerno293, M. O. P. Sampaio294, R. Santos295, V. Sanz296, J. J. Sanz-Cillero297, H. Sargsyan298, U. Sarica299, P. Schichtel300, J. Schlenk301, T. Schmidt302, C. Schmitt303, M. Schönherr304, U. Schubert305, M. Schulze306, S. Sekula307, M. Sekulla308, E. Shabalina309, H. S. Shao310, J. Shelton311, C. H. Shepherd-Themistocleous312, S. Y. Shim313, F. Siegert314, A. Signer315, J. P. Silva316, L. Silvestrini317, M. Sjodahl318, P. Slavich319, M. Slawinska320, L. Soffi321, M. Spannowsky322, C. Speckner323, D. M. Sperka324, M. Spira325, O. Stål326, F. Staub327, T. Stebel328, T. Stefaniak329, M. Steinhauser330, I. W. Stewart331, M. J. Strassler332, J. Streicher333, D. M. Strom334, S. Su335, X. Sun336, F. J. Tackmann337, K. Tackmann338, A. M. Teixeira339, R. Teixeira de Lima340, V. Theeuwes341, R. Thorne342, D. Tommasini343, P. Torrielli344, M. Tosi345, F. Tramontano346, Z. Trócsányi347, M. Trott348, I. Tsinikos349, M. Ubiali350, P. Vanlaer351, W. Verkerke352, A. Vicini353, L. Viliani354, E. Vryonidou355, D. Wackeroth356, C. E. M. Wagner357, J. Wang358, S. Wayand359, G. Weiglein360, C. Weiss361, M. Wiesemann362, C. Williams363, J. Winter364, D. Winterbottom365, R. Wolf366, M. Xiao367, L. L. Yang368, R. Yohay369, S. P. Y. Yuen370, G. Zanderighi371, M. Zaro372, D. Zeppenfeld373, R. Ziegler374, T. Zirke375, J. Zupan376
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Working Group, 53The LHC Higgs Cross Section Working Group, 54The LHC Higgs Cross Section Working Group, 55The LHC Higgs Cross Section Working Group, 56The LHC Higgs Cross Section Working Group, 57The LHC Higgs Cross Section Working Group, 58The LHC Higgs Cross Section Working Group, 59The LHC Higgs Cross Section Working Group, 60The LHC Higgs Cross Section Working Group, 61The LHC Higgs Cross Section Working Group, 62The LHC Higgs Cross Section Working Group, 63The LHC Higgs Cross Section Working Group, 64The LHC Higgs Cross Section Working Group, 65The LHC Higgs Cross Section Working Group, 66The LHC Higgs Cross Section Working Group, 67The LHC Higgs Cross Section Working Group, 68The LHC Higgs Cross Section Working Group, 69The LHC Higgs Cross Section Working Group, 70The LHC Higgs Cross Section Working Group, 71The LHC Higgs Cross Section Working Group, 72The LHC Higgs Cross Section Working Group, 73The LHC Higgs Cross Section Working Group, 74The LHC Higgs Cross Section Working 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This Report summarizes the results of the activities of the LHC Higgs Cross Section Working Group in the period 2014-2016. The main goal of the working group was to present the state-of-the-art of Higgs physics at the LHC, integrating all new results that have appeared in the last few years. The first part compiles the most up-to-date predictions of Higgs boson production cross sections and decay branching ratios, parton distribution functions, and off-shell Higgs boson production and interference effects. Read More

Halo distribution is a key topic for background study. This paper has developed an analytical method to give an estimation of ATF beam halo distribution. The equilibrium particle distribution of the beam tail in the ATF damping ring is calculated analytically with different emittance and different vacuum degree. Read More

We analyze the impact of the recent HERA run I+II combination of inclusive deep inelastic scattering cross-section data on the CT14 global analysis of PDFs. New PDFs at NLO and NNLO, called CT14$_{\textrm{HERA2}}$, are obtained by a refit of the CT14 data ensembles, in which the HERA run I combined measurements are replaced by the new HERA run I+II combination. The CT14 functional parametrization of PDFs is flexible enough to allow good descriptions of different flavor combinations, so we use the same parametrization for CT14$_{\textrm{HERA2}}$ but with an additional shape parameter for describing the strange quark PDF. Read More

Partial least squares regression (PLSR) has been a popular technique to explore the linear relationship between two datasets. However, most of algorithm implementations of PLSR may only achieve a suboptimal solution through an optimization on the Euclidean space. In this paper, we propose several novel PLSR models on Riemannian manifolds and develop optimization algorithms based on Riemannian geometry of manifolds. Read More