J. Tang - The CDF Collaboration

J. Tang
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J. Tang
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The CDF Collaboration
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Mathematics - Information Theory (8)
 
Computer Science - Information Theory (8)
 
Computer Science - Learning (6)
 
Physics - Materials Science (5)
 
Computer Science - Distributed; Parallel; and Cluster Computing (4)
 
Computer Science - Artificial Intelligence (4)
 
Computer Science - Computer Vision and Pattern Recognition (4)
 
Computer Science - Information Retrieval (3)
 
Statistics - Machine Learning (3)
 
Computer Science - Computation and Language (3)
 
High Energy Physics - Experiment (3)
 
Physics - Optics (3)
 
Physics - Soft Condensed Matter (3)
 
Computer Science - Robotics (3)
 
Physics - Accelerator Physics (2)
 
Quantum Physics (2)
 
Computer Science - Architecture (2)
 
Quantitative Biology - Biomolecules (2)
 
Mathematics - Optimization and Control (2)
 
Cosmology and Nongalactic Astrophysics (2)
 
Astrophysics of Galaxies (2)
 
Nuclear Experiment (1)
 
Physics - Instrumentation and Detectors (1)
 
Computer Science - Computers and Society (1)
 
Physics - Chemical Physics (1)
 
Computer Science - Data Structures and Algorithms (1)
 
Physics - Statistical Mechanics (1)
 
Quantitative Biology - Genomics (1)
 
Quantitative Biology - Quantitative Methods (1)
 
Physics - Biological Physics (1)
 
Physics - Fluid Dynamics (1)
 
Computer Science - Neural and Evolutionary Computing (1)
 
Physics - Mesoscopic Systems and Quantum Hall Effect (1)

Publications Authored By J. Tang

Deep Neural Networks (DNN) achieve human level performance in many image analytics tasks but DNNs are mostly deployed to GPU platforms that consume a considerable amount of power. New hardware platforms using lower precision arithmetic achieve drastic reductions in power consumption. More recently, brain-inspired spiking neuromorphic chips have achieved even lower power consumption, on the order of milliwatts, while still offering real-time processing. Read More

The edit distance under the DCJ model can be computed in linear time for genomes with equal content or with Indels. But it becomes NP-Hard in the presence of duplications, a problem largely unsolved especially when Indels are considered. In this paper, we compare two mainstream methods to deal with duplications and associate them with Indels: one by deletion, namely DCJ-Indel-Exemplar distance; versus the other by gene matching, namely DCJ-Indel-Matching distance. Read More

Sketched gradient algorithms have been recently introduced for efficiently solving the large-scale constrained Least-squares regressions. In this paper we provide novel convergence analysis for the basic method {\it Gradient Projection Classical Sketch} (GPCS) to reveal the fast linear convergence rate of GPCS towards a vicinity of the solution thanks to the intrinsic low-dimensional geometric structure of the solution prompted by constraint set. Similar to our analysis we observe computational and sketch size trade-offs in numerical experiments. Read More

The rise of robotic applications has led to the generation of a huge volume of unstructured data, whereas the current cloud infrastructure was designed to process limited amounts of structured data. To address this problem, we propose a learn-memorize-recall-reduce paradigm for robotic cloud computing. The learning stage converts incoming unstructured data into structured data; the memorization stage provides effective storage for the massive amount of data; the recall stage provides efficient means to retrieve the raw data; while the reduction stage provides means to make sense of this massive amount of unstructured data with limited computing resources. Read More

This paper studies energy efficiency (EE) and average throughput maximization for cognitive radio systems in the presence of unslotted primary users. It is assumed that primary user activity follows an ON-OFF alternating renewal process. Secondary users first sense the channel possibly with errors in the form of miss detections and false alarms, and then start the data transmission only if no primary user activity is detected. Read More

Autonomous driving clouds provide essential services to support autonomous vehicles. Today these services include but not limited to distributed simulation tests for new algorithm deployment, offline deep learning model training, and High-Definition (HD) map generation. These services require infrastructure support including distributed computing, distributed storage, as well as heterogeneous computing. Read More

Increased reflectance from the inclusion of highly scattering particles at low volume fractions in an insulating dielectric offers a promising way to reduce radiative thermal losses at high temperatures. Here, we investigate plasmonic resonance driven enhanced scattering from microinclusions of low-bandgap semiconductors (InP, Si, Ge, PbS, InAs and Te) in an insulating composite to tailor its infrared reflectance for minimizing thermal losses from radiative transfer. To this end, we compute the spectral properties of the microcomposites using Monte Carlo modeling and compare them with results from Fresnel equations. 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

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. Read More

Background: It is necessary and essential to discovery protein function from the novel primary sequences. Wet lab experimental procedures are not only time-consuming, but also costly, so predicting protein structure and function reliably based only on amino acid sequence has significant value. TATA-binding protein (TBP) is a kind of DNA binding protein, which plays a key role in the transcription regulation. Read More

Photons propagating in Laguerre-Gaussian modes have characteristic orbital angular momentums, which are fundamental optical degrees of freedom. The orbital angular momentum of light has potential application in high capacity optical communication and even in quantum information processing. In this work, we experimentally construct a ring cavity with 4 lenses and 4 mirrors that is completely degenerate for Laguerre-Gaussian modes. Read More

Reciprocity in directed networks points to user's willingness to return favors in building mutual interactions. High reciprocity has been widely observed in many directed social media networks such as following relations in Twitter and Tumblr. Therefore, reciprocal relations between users are often regarded as a basic mechanism to create stable social ties and play a crucial role in the formation and evolution of networks. Read More

Recently low displacement rank (LDR) matrices, or so-called structured matrices, have been proposed to compress large-scale neural networks. Empirical results have shown that neural networks with weight matrices of LDR matrices, referred as LDR neural networks, can achieve significant reduction in space and computational complexity while retaining high accuracy. We formally study LDR matrices in deep learning. Read More

2017Mar
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

We study the extent to which we can infer users' geographical locations from social media. Location inference from social media can benefit many applications, such as disaster management, targeted advertising, and news content tailoring. In recent years, a number of algorithms have been proposed for identifying user locations on social media platforms such as Twitter and Facebook from message contents, friend networks, and interactions between users. Read More

We describe the computing tasks involved in autonomous driving, examine existing autonomous driving computing platform implementations. To enable autonomous driving, the computing stack needs to simultaneously provide high performance, low power consumption, and low thermal dissipation, at low cost. We discuss possible approaches to design computing platforms that will meet these needs. Read More

Photosynthetic organisms rely on a series of self-assembled nanostructures with tuned electronic energy levels in order to transport energy from where it is collected by photon absorption, to reaction centers where the energy is used to drive chemical reactions. In the photosynthetic bacteria Chlorobaculum tepidum (Cba. tepidum), a member of the green sulphur bacteria (GSB) family, light is absorbed by large antenna complexes called chlorosomes. Read More

In recent years, we have observed a clear trend in the rapid rise of autonomous vehicles, robotics, virtual reality, and augmented reality. The core technology enabling these applications, Simultaneous Localization And Mapping (SLAM), imposes two main challenges: first, these workloads are computationally intensive and they often have real-time requirements; second, these workloads run on battery-powered mobile devices with limited energy budget. In short, the essence of these challenges is that performance should be improved while simultaneously reducing energy consumption, two rather contradicting goals by conventional wisdom. Read More

2017Jan
Authors: MICE Collaboration, M. Bogomilov, R. Tsenov, G. Vankova-Kirilova, Y. Song, J. Tang, Z. Li, R. Bertoni, M. Bonesini, F. Chignoli, R. Mazza, V. Palladino, A. de Bari, G. Cecchet, D. Orestano, L. Tortora, Y. Kuno, S. Ishimoto, F. Filthaut, D. Jokovic, D. Maletic, M. Savic, O. M. Hansen, S. Ramberger, M. Vretenar, R. Asfandiyarov, A. Blondel, F. Drielsma, Y. Karadzhov, G. Charnley, N. Collomb, A. Gallagher, A. Grant, S. Griffiths, T. Hartnett, B. Martlew, A. Moss, A. Muir, I. Mullacrane, A. Oates, P. Owens, G. Stokes, M. Tucker, P. Warburton, C. White, D. Adams, R. J. Anderson, P. Barclay, V. Bayliss, J. Boehm, T. W. Bradshaw, M. Courthold, K. Dumbell, V. Francis, L. Fry, T. Hayler, M. Hills, A. Lintern, C. Macwaters, A. Nichols, R. Preece, S. Ricciardi, C. Rogers, T. Stanley, J. Tarrant, A. Wilson, S. Watson, R. Bayes, J. C. Nugent, F. J. P. Soler, R. Gamet, G. Barber, V. J. Blackmore, D. Colling, A. Dobbs, P. Dornan, C. Hunt, A. Kurup, J-B. Lagrange, K. Long, J. Martyniak, S. Middleton, J. Pasternak, M. A. Uchida, J. H. Cobb, W. Lau, C. N. Booth, P. Hodgson, J. Langlands, E. Overton, M. Robinson, P. J. Smith, S. Wilbur, A. J. Dick, K. Ronald, C. G. Whyte, A. R. Young, S. Boyd, P. Franchini, J. R. Greis, C. Pidcott, I. Taylor, R. B. S. Gardener, P. Kyberd, J. J. Nebrensky, M. Palmer, H. Witte, A. D. Bross, D. Bowring, A. Liu, D. Neuffer, M. Popovic, P. Rubinov, A. DeMello, S. Gourlay, D. Li, S. Prestemon, S. Virostek, B. Freemire, P. Hanlet, D. M. Kaplan, T. A. Mohayai, D. Rajaram, P. Snopok, V. Suezaki, Y. Torun, Y. Onel, L. M. Cremaldi, D. A. Sanders, D. J. Summers, G. G. Hanson, C. Heidt

Muon beams of low emittance provide the basis for the intense, well-characterised neutrino beams necessary to elucidate the physics of flavour at a neutrino factory and to provide lepton-antilepton collisions at energies of up to several TeV at a muon collider. The international Muon Ionization Cooling Experiment (MICE) aims to demonstrate ionization cooling, the technique by which it is proposed to reduce the phase-space volume occupied by the muon beam at such facilities. In an ionization-cooling channel, the muon beam passes through a material in which it loses energy. Read More

A eutectic reaction is a special chemical/physical reaction involving multiple phases, solid or liquid, to form a joint lattice structure with a unique atomic ratio between the components. Visualization of phase reaction of composite nanomaterials with high spatial and temporal resolution provides a key understanding of alloy growth with important industrial applications. However, it has been a rather challenging task. Read More

Dynamics of active or self-propulsive Brownian particles in nonequilibrium status, has recently attracted great interest in many fields including biological entities and artificial micro/nanoscopic motors6. Understanding of their dynamics can provide insight into the statistical properties of biological and physical systems far from equilibrium. Generally, active Brownian particles can involve either translational or rotational motion. Read More

We report on a temporal evolution of photoluminescence (PL) spectroscopy of CuInS$_{2}$/ZnS colloidal quantum dots (QDs) by drop-casting on SiO$_{2}$/Si substrates and high quality factor microdisks (MDs) under different atmospheric conditions. Fast PL decay, peak blueshift and linewidth broadening due to photooxidation have been observed at low excitation power. With further increasing of the excitation power, the PL peak position shows a redshift and linewidth becomes narrow, which is ascribed to the enhanced F$\ddot{o}$rster resonant energy transfer between different QDs by photoinduced agglomeration. Read More

Despite significant progress, image saliency detection still remains a challenging task in complex scenes and environments. Integrating multiple different but complementary cues, like RGB and Thermal (RGB-T), may be an effective way for boosting saliency detection performance. The current research in this direction, however, is limited by the lack of a comprehensive benchmark. Read More

We consider a multi-user multiple-input multiple-output (MIMO) setup where full-duplex (FD) multi-antenna nodes apply linear beamformers to simultaneously transmit and receive multiple streams over Rician fading channels. The exact first and second positive moments of the residual self-interference (SI), involving the squared norm of a sum of non-identically distributed random variables, are derived in closed-form. The method of moments is hence invoked to provide a Gamma approximation for the residual SI distribution. Read More

We describe a soft matter system of self-organized oblate micelles and plasmonic gold nanorods that exhibit a negative orientational order parameter. Because of anisotropic surface anchoring interactions, colloidal gold nanorods tend to align perpendicular to the director describing the average orientation of normals to the discoidal micelles. Helicoidal structures of highly concentrated nanorods with a negative order parameter are realized by adding a chiral additive and are further controlled by means of confinement and mechanical stress. Read More

Improving the precision of measurements is a prime challenge to the scientific community. Quantum metrology provides methods to overcome the standard quantum limit (SQL) of 1/sqrt{N} and to reach the fundamental Heisenberg-limit (HL) of 1/N. While a lot of theoretical and experimenta works have been dedicated to this task, most of the attempts focused on utilizing NOON and squeezed states, which exhibit unique quantum correlations. Read More

Luminous high-redshift quasars can be used to probe of the intergalactic medium (IGM) in the early universe because their UV light is absorbed by the neutral hydrogen along the line of sight. They help us to measure the neutral hydrogen fraction of the high-z universe, shedding light on the end of reionization epoch. In this paper, we present a discovery of a new quasar (PSO J006. Read More

Systems with coupled cavities and waveguides have been demonstrated as optical switches and optical sensors. To optimize the functionalities of these optical devices, Fano resonance with asymmetric and steep spectral line shape has been used. We theoretically propose a coupled photonic crystal cavity-waveguide structure to achieve Fano resonance by placing partially reflecting elements in waveguide. Read More

Most existing word embedding approaches do not distinguish the same words in different contexts, therefore ignoring their contextual meanings. As a result, the learned embeddings of these words are usually a mixture of multiple meanings. In this paper, we acknowledge multiple identities of the same word in different contexts and learn the \textbf{identity-sensitive} word embeddings. Read More

This paper studied generating natural languages at particular contexts or situations. We proposed two novel approaches which encode the contexts into a continuous semantic representation and then decode the semantic representation into text sequences with recurrent neural networks. During decoding, the context information are attended through a gating mechanism, addressing the problem of long-range dependency caused by lengthy sequences. Read More

Statistical topic models efficiently facilitate the exploration of large-scale data sets. Many models have been developed and broadly used to summarize the semantic structure in news, science, social media, and digital humanities. However, a common and practical objective in data exploration tasks is not to enumerate all existing topics, but to quickly extract representative ones that broadly cover the content of the corpus, i. Read More

This paper proposes a novel collimation method for large hadron colliders by arranging betatron and momentum collimation systems in the same insertion to improve the overall cleaning efficiency. The method has the potential of avoiding beam losses at the downstream dispersion suppression section following the conventional betatron collimation section, which is caused by those particles with single diffractive scattering at the collimators. Evident beam loss in arc sections should be avoided to protect the superconducting magnets from quenching, especially when the stored beam energy is up to hundreds of MJ level or even higher in modern proton-proton collider. Read More

We study the problem of finding appropriate experts who are able to complete timely reviews and would not say "no" to the invitation. The problem is a central issue in many question-and-answer systems, but has received little research attention. Different from most existing studies that focus on expertise matching, we want to further predict the expert's response: given a question, how can we find the expert who is able to provide a quality review and will agree to do it. Read More

In this paper, we provide a theoretical framework for the study of massive multiple-input multiple-output (MIMO)-enabled full-duplex (FD) cellular networks in which the self-interference (SI) channels follow the Rician distribution and other channels are Rayleigh distributed. To facilitate bi-directional wireless functionality, we adopt (i) a downlink (DL) linear zero-forcing with self-interference-nulling (ZF-SIN) precoding scheme at the FD base stations (BSs), and (ii) an uplink (UL) self-interference-aware (SIA) fractional power control mechanism at the FD user equipments (UEs). Linear ZF receivers are further utilized for signal detection in the UL. Read More

Embedding and visualizing large-scale high-dimensional data in a two-dimensional space is an important problem since such visualization can reveal deep insights out of complex data. Most of the existing embedding approaches, however, run on an excessively high precision, ignoring the fact that at the end, embedding outputs are converted into coarse-grained discrete pixel coordinates in a screen space. Motivated by such an observation and directly considering pixel coordinates in an embedding optimization process, we accelerate Barnes-Hut tree-based t-distributed stochastic neighbor embedding (BH-SNE), known as a state-of-the-art 2D embedding method, and propose a novel method called PixelSNE, a highly-efficient, screen resolution-driven 2D embedding method with a linear computational complexity in terms of the number of data items. Read More

In this paper, we investigate joint antenna selection and spatial switching (SS) for quality-of-service (QoS)-constrained energy efficiency (EE) optimization in a multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system. A practical linear power model taking into account the entire transmit-receive chain is accordingly utilized. The corresponding fractional-combinatorial and non-convex EE problem, involving joint optimization of eigen-channel assignment, power allocation, and active receive antenna set selection, subject to satisfying minimum sum-rate and power transfer constraints, is extremely difficult to solve directly. Read More

Simultaneous wireless information and power transfer (SWIPT) is anticipated to have great applications in fifth-generation (5G) and beyond communication systems. In this paper, we address the energy efficiency (EE) optimization problem for SWIPT multiple-input multiple-output broadcast channel (MIMO-BC) with time-switching (TS) receiver design. Our aim is to maximize the EE of the system whilst satisfying certain constraints in terms of maximum transmit power and minimum harvested energy per user. Read More

In this paper, we provide joint subcarrier assignment and power allocation schemes for quality-of-service (QoS)-constrained energy-efficiency (EE) optimization in the downlink of an orthogonal frequency division multiple access (OFDMA)-based two-tier heterogeneous cellular network (HCN). Considering underlay transmission, where spectrum-efficiency (SE) is fully exploited, the EE solution involves tackling a complex mixed-combinatorial and non-convex optimization problem. With appropriate decomposition of the original problem and leveraging on the quasi-concavity of the EE function, we propose a dual-layer resource allocation approach and provide a complete solution using difference-of-two-concave-functions approximation, successive convex approximation, and gradient-search methods. Read More

Searching for the Neutrinoless Double Beta Decay (NLDBD) is now regarded as the topmost promising technique to explore the nature of neutrinos after the discovery of neutrino masses in oscillation experiments. PandaX-III (Particle And Astrophysical Xenon Experiment III) will search for the NLDBD of $^{136}$Xe at the China Jin Ping underground Laboratory (CJPL). In the first phase of the experiment, a high pressure gas Time Projection Chamber (TPC) will contain 200 kg, 90% $^{136}$Xe enriched gas operated at 10 bar. Read More

2016Oct
Authors: CDF Collaboration, T. Aaltonen, 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, 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

We report on a measurement of the $D^{+}$-meson production cross section as a function of transverse momentum ($p_T$) in proton-antiproton ($p\bar{p}$) collisions at 1.96 TeV center-of-mass energy, using the full data set collected by the Collider Detector at Fermilab in Tevatron Run II and corresponding to 10 fb$^{-1}$ of integrated luminosity. We use $D^{+} \to K^-\pi^+\pi^+$ decays fully reconstructed in the central rapidity region $|y|<1$ with transverse momentum down to 1. Read More

Dense cellular networks (DenseNets) are fast becoming a reality with the rapid deployment of base stations (BSs) aimed at meeting the explosive data traffic demand. In legacy systems however this comes with the penalties of higher network interference and energy consumption. In order to support network densification in a sustainable manner, the system behavior should be made 'load-proportional' thus allowing certain portions of the network to activate on-demand. Read More

The importance of timely response to natural disasters and evacuating affected people to safe areas is paramount to save lives. Emergency services are often handicapped by the amount of rescue resources at their disposal. We present a system that leverages the power of a social network forming new connections among people based on \textit{real-time location} and expands the rescue resources pool by adding private sector cars. Read More

This paper addresses the problem of adding redundancy to a collection of physical objects so that the overall system is more robust to failures. Physical redundancy can (generally) be achieved by employing copy/substitute procedures. This is fundamentally different from information redundancy, where a single parity check simultaneously protects a large number of data bits against a single erasure. Read More

Native extracellular matrices (ECMs), such as those of the human brain and other neural tissues, exhibit networks of molecular interactions between specific matrix proteins and other tissue components. Guided by these naturally self-assembling supramolecular systems, we have designed a matrix-derived protein chimera that contains a laminin globular-like (LG) domain fused to an elastin-like polypeptide (ELP). All-atom, classical molecular dynamics simulations of our designed laminin-elastin fusion protein reveal temperature-dependent conformational changes, in terms of secondary structure composition, solvent accessible surface area, hydrogen bonding, and surface hydration. Read More

We propose a randomized first order optimization algorithm Gradient Projection Iterative Sketch (GPIS) and an accelerated variant for efficiently solving large scale constrained Least Squares (LS). We provide theoretical convergence analysis for both proposed algorithms and demonstrate our methods' computational efficiency compared to classical accelerated gradient method, and the state of the art variance-reduced stochastic gradient methods through numerical experiments in various large synthetic/real data sets. Read More

We propose a direct self-assembly mechanism towards obtaining defect-free perpendicular lamellar phases of diblock copolymer (BCP) thin films. In our numerical study, a thin BCP film having a flat top surface is casted on a uni-directional corrugated solid substrate. The substrate is treated chemically and has a weak preference toward one of the two BCP components. Read More

Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible for data with millions of features. Here we introduce the first feature selection method for nonlinear learning problems that can scale up to large, ultra-high dimensional biological data. 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

Understanding human actions in wild videos is an important task with a broad range of applications. In this paper we propose a novel approach named Hierarchical Attention Network (HAN), which enables to incorporate static spatial information, short-term motion information and long-term video temporal structures for complex human action understanding. Compared to recent convolutional neural network based approaches, HAN has following advantages (1) HAN can efficiently capture video temporal structures in a longer range; (2) HAN is able to reveal temporal transitions between frame chunks with different time steps, i. Read More