Browsing by Author "Chen, J."

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Open Access
Demonstration of a cavity-enhanced optical parametric chirped-pulse amplification system
(Optical Society of America, 2011-03-28) Siddiqui, A.; Hong, K. H.; Moses, J.; Chen, J.; Ilday, F. O.; Kartner, F. X.
The use of a low finesse enhancement cavity resonant with a low average power (< 1W) and a high repetition rate (78MHz) pump source is shown to achieve 55% conversion efficiency into signal and idler from the coupled pump in an optical parametric process, whereas an equivalent amount of pump power in a single-pass configuration leads to negligible conversion. Careful comparison of the intracavity conversion process to the single-pass case is performed to assess the underlying impedance matching that yields the high conversion results.
Open Access
Demonstration of cavity-enhanced optical parametric chirped-pulse amplification system at high repetition rate
(OSA, 2010) Siddiqui, A.; Hong, K. H.; Moses, J.; Chen, J.; İlday, Fatih Ömer; Kartner, F. X.
First experimental demonstration of cavity-enhanced OPCPA at 78 MHz with <1 W of pump power is presented. For comparison, we demonstrated saturated gain in a single-pass experiment from 6-W Yb-fiber pump and Er-fiber signal sources.
Open Access
Exploring the role of loss functions in multiclass classification
(IEEE, 2020-05) Demirkaya, Ahmet; Chen, J.; Oymak, Samet
Cross-entropy is the de-facto loss function in modern classification tasks that involve distinguishing hundreds or even thousands of classes. To design better loss functions for new machine learning tasks, it is critical to understand what makes a loss function suitable for a problem. For instance, what makes the cross entropy better than other alternatives such as quadratic loss? In this work, we discuss the role of loss functions in learning tasks with a large number of classes. We hypothesize that different loss functions can have large variability in the difficulty of optimization and that simplicity of training is a key catalyst for better test-time performance. Our intuition draws from the success of over-parameterization in deep learning: As a model has more parameters, it trains faster and achieves higher test accuracy. We argue that, effectively, cross-entropy loss results in a much more over-parameterized problem compared to the quadratic loss, thanks to its emphasis on the correct class (associated with the label). Such over-parameterization drastically simplifies the training process and ends up boosting the test performance. For separable mixture models, we provide a separation result where cross-entropy loss can always achieve small training loss, whereas quadratic loss has diminishing benefit as the number of classes and class correlations increase. Numerical experiments with CIFAR 100 corroborate our results. We show that the accuracy with quadratic loss disproportionately degrades with a growing number of classes; however, encouraging quadratic loss to focus on the correct class results in a drastically improved performance.
Open Access
A global reference for human genetic variation
(Nature Publishing Group, 2015) Auton, A.; Abecasis, G. R.; Altshuler, D. M.; Durbin, R. M.; Bentley, D. R.; Chakravarti, A.; Clark, A. G.; Donnelly, P.; Eichler, E. E.; Flicek, P.; Gabriel, S. B.; Gibbs, R. A.; Green, E. D.; Hurles, M. E.; Knoppers, B. M.; Korbel, J. O.; Lander, E. S.; Lee, C.; Lehrach, H.; Mardis, E. R.; Marth, G. T.; McVean, G. A.; Nickerson, D. A.; Schmidt, J. P.; Sherry, S. T.; Wang, J.; Wilson, R. K.; Boerwinkle, E.; Doddapaneni, H.; Han, Y.; Korchina, V.; Kovar, C.; Lee, S.; Muzny, D.; Reid, J. G.; Zhu, Y.; Chang, Y.; Feng, Q.; Fang, X.; Guo, X.; Jian, M.; Jiang, H.; Jin, X.; Lan, T.; Li, G.; Li, J.; Li, Y.; Liu, S.; Liu, X.; Lu, Y.; Ma, X.; Tang, M.; Wang, B.; Wang, G.; Wu, H.; Wu, R.; Xu, X.; Yin, Y.; Zhang, D.; Zhang, W.; Zhao, J.; Zhao, M.; Zheng, X.; Gupta, N.; Gharani, N.; Toji, L. H.; Gerry, N. P.; Resch, A. M.; Barker, J.; Clarke, L.; Gil, L.; Hunt, S. E.; Kelman, G.; Kulesha, E.; Leinonen, R.; McLaren, W. M.; Radhakrishnan, R.; Roa, A.; Smirnov, D.; Smith, R. E.; Streeter, I.; Thormann, A.; Toneva, I.; Vaughan, B.; Zheng-Bradley, X.; Grocock, R.; Humphray, S.; James, T.; Kingsbury, Z.; Sudbrak, R.; Albrecht, M. W.; Amstislavskiy, V. S.; Borodina, T. A.; Lienhard, M.; Mertes, F.; Sultan, M.; Timmermann, B.; Yaspo, Marie-Laure; Fulton, L.; Ananiev, V.; Belaia, Z.; Beloslyudtsev, D.; Bouk, N.; Chen, C.; Church, D.; Cohen, R.; Cook, C.; Garner, J.; Hefferon, T.; Kimelman, M.; Liu, C.; Lopez, J.; Meric, P.; O'Sullivan, C.; Ostapchuk, Y.; Phan, L.; Ponomarov, S.; Schneider, V.; Shekhtman, E.; Sirotkin, K.; Slotta, D.; Zhang, H.; Balasubramaniam, S.; Burton, J.; Danecek, P.; Keane, T. M.; Kolb-Kokocinski, A.; McCarthy, S.; Stalker, J.; Quail, M.; Davies, C. J.; Gollub, J.; Webster, T.; Wong, B.; Zhan, Y.; Campbell, C. L.; Kong, Y.; Marcketta, A.; Yu, F.; Antunes, L.; Bainbridge, M.; Sabo, A.; Huang, Z.; Coin, L. J. M.; Fang, L.; Li, Q.; Li, Z.; Lin, H.; Liu, B.; Luo, R.; Shao, H.; Xie, Y.; Ye, C.; Yu, C.; Zhang, F.; Zheng, H.; Zhu, H.; Alkan, C.; Dal, E.; Kahveci, F.; Garrison, E. P.; Kural, D.; Lee, W. P.; Leong, W. F.; Stromberg, M.; Ward, A. N.; Wu, J.; Zhang, M.; Daly, M. J.; DePristo, M. A.; Handsaker, R. E.; Banks, E.; Bhatia, G.; Del Angel, G.; Genovese, G.; Li, H.; Kashin, S.; McCarroll, S. A.; Nemesh, J. C.; Poplin, R. E.; Yoon, S. C.; Lihm, J.; Makarov, V.; Gottipati, S.; Keinan, A.; Rodriguez-Flores, J. L.; Rausch, T.; Fritz, M. H.; Stütz, A. M.; Beal, K.; Datta, A.; Herrero, J.; Ritchie, G. R. S.; Zerbino, D.; Sabeti, P. C.; Shlyakhter, I.; Schaffner, S. F.; Vitti, J.; Cooper, D. N.; Ball, E. V.; Stenson, P. D.; Barnes, B.; Bauer, M.; Cheetham, R. K.; Cox, A.; Eberle, M.; Kahn, S.; Murray, L.; Peden, J.; Shaw, R.; Kenny, E. E.; Batzer, M. A.; Konkel, M. K.; Walker, J. A.; MacArthur, D. G.; Lek, M.; Herwig, R.; Ding, L.; Koboldt, D. C.; Larson, D.; Ye, K.; Gravel, S.; Swaroop, A.; Chew, E.; Lappalainen, T.; Erlich, Y.; Gymrek, M.; Willems, T. F.; Simpson, J. T.; Shriver, M. D.; Rosenfeld, J. A.; Bustamante, C. D.; Montgomery, S. B.; De La Vega, F. M.; Byrnes, J. K.; Carroll, A. W.; DeGorter, M. K.; Lacroute, P.; Maples, B. K.; Martin, A. R.; Moreno-Estrada, A.; Shringarpure, S. S.; Zakharia, F.; Halperin, E.; Baran, Y.; Cerveira, E.; Hwang, J.; Malhotra, A.; Plewczynski, D.; Radew, K.; Romanovitch, M.; Zhang, C.; Hyland, F. C. L.; Craig, D. W.; Christoforides, A.; Homer, N.; Izatt, T.; Kurdoglu, A. A.; Sinari, S. A.; Squire, K.; Xiao, C.; Sebat, J.; Antaki, D.; Gujral, M.; Noor, A.; Ye, K.; Burchard, E. G.; Hernandez, R. D.; Gignoux, C. R.; Haussler, D.; Katzman, S. J.; Kent, W. J.; Howie, B.; Ruiz-Linares, A.; Dermitzakis, E. T.; Devine, S. E.; Kang, H. M.; Kidd, J. M.; Blackwell, T.; Caron, S.; Chen, W.; Emery, S.; Fritsche, L.; Fuchsberger, C.; Jun, G.; Li, B.; Lyons, R.; Scheller, C.; Sidore, C.; Song, S.; Sliwerska, E.; Taliun, D.; Tan, A.; Welch, R.; Wing, M. K.; Zhan, X.; Awadalla, P.; Hodgkinson, A.; Li, Y.; Shi, X.; Quitadamo, A.; Lunter, G.; Marchini, J. L.; Myers, S.; Churchhouse, C.; Delaneau, O.; Gupta-Hinch, A.; Kretzschmar, W.; Iqbal, Z.; Mathieson, I.; Menelaou, A.; Rimmer, A.; Xifara, D. K.; Oleksyk, T. K.; Fu, Y.; Liu, X.; Xiong, M.; Jorde, L.; Witherspoon, D.; Xing, J.; Browning, B. L.; Browning, S. R.; Hormozdiari, F.; Sudmant, P. H.; Khurana, E.; Tyler-Smith, C.; Albers, C. A.; Ayub, Q.; Chen, Y.; Colonna, V.; Jostins, L.; Walter, K.; Xue, Y.; Gerstein, M. B.; Abyzov, A.; Balasubramanian, S.; Chen, J.; Clarke, D.; Fu, Y.; Harmanci, A. O.; Jin, M.; Lee, D.; Liu, J.; Mu, X. J.; Zhang, J.; Zhang, Y.; Hartl, C.; Shakir, K.; Degenhardt, J.; Meiers, S.; Raeder, B.; Casale, F. P.; Stegle, O.; Lameijer, E. W.; Hall, I.; Bafna, V.; Michaelson, J.; Gardner, E. J.; Mills, R. E.; Dayama, G.; Chen, K.; Fan, X.; Chong, Z.; Chen, T.; Chaisson, M. J.; Huddleston, J.; Malig, M.; Nelson, B. J.; Parrish, N. F.; Blackburne, B.; Lindsay, S. J.; Ning, Z.; Zhang, Y.; Lam, H.; Sisu, C.; Challis, D.; Evani, U. S.; Lu, J.; Nagaswamy, U.; Yu, J.; Li, W.; Habegger, L.; Yu, H.; Cunningham, F.; Dunham, I.; Lage, K.; Jespersen, J. B.; Horn, H.; Kim, D.; Desalle, R.; Narechania, A.; Sayres, M. A. W.; Mendez, F. L.; Poznik, G. D.; Underhill, P. A.; Mittelman, D.; Banerjee, R.; Cerezo, M.; Fitzgerald, T. W.; Louzada, S.; Massaia, A.; Yang, F.; Kalra, D.; Hale, W.; Dan, X.; Barnes, K. C.; Beiswanger, C.; Cai, H.; Cao, H.; Henn, B.; Jones, D.; Kaye, J. S.; Kent, A.; Kerasidou, A.; Mathias, R.; Ossorio, P. N.; Parker, M.; Rotimi, C. N.; Royal, C. D.; Sandoval, K.; Su, Y.; Tian, Z.; Tishkoff, S.; Via, M.; Wang, Y.; Yang, H.; Yang, L.; Zhu, J.; Bodmer, W.; Bedoya, G.; Cai, Z.; Gao, Y.; Chu, J.; Peltonen, L.; Garcia-Montero, A.; Orfao, A.; Dutil, J.; Martinez-Cruzado, J. C.; Mathias, R. A.; Hennis, A.; Watson, H.; McKenzie, C.; Qadri, F.; LaRocque, R.; Deng, X.; Asogun, D.; Folarin, O.; Happi, C.; Omoniwa, O.; Stremlau, M.; Tariyal, R.; Jallow, M.; Joof, F. S.; Corrah, T.; Rockett, K.; Kwiatkowski, D.; Kooner, J.; Hien, T. T.; Dunstan, S. J.; ThuyHang, N.; Fonnie, R.; Garry, R.; Kanneh, L.; Moses, L.; Schieffelin, J.; Grant, D. S.; Gallo, C.; Poletti, G.; Saleheen, D.; Rasheed, A.; Brooks, L. D.; Felsenfeld, A. L.; McEwen, J. E.; Vaydylevich, Y.; Duncanson, A.; Dunn, M.; Schloss, J. A.
The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies. © 2015 Macmillan Publishers Limited. All rights reserved.
Open Access
High-precision laser master oscillators for optical timing distribution systems in future light sources
(European Physical Society Accelerator Group (EPS-AG), 2006) Winter, A.; Schmüser, P.; Ludwig, F.; Schlarb, H.; Chen, J.; Kärtner, F. X.; ilday, F. ÖMER
An ultra-stable timing and synchronization system for linac-driven FELs has been designed providing 10 fs precision over distances of several kilometers. Mode-locked fiber lasers serve as master oscillators. The optical pulse train is distributed through length-stabilized fiber links. The layout of the optical synchronization system and its phase noise properties are described. A prototype system has been tested in an accelerator environment and has achieved the required stability.
Open Access
Inborn errors of OAS–RNase L in SARS-CoV-2–related multisystem inflammatory syndrome in children
(American Association for the Advancement of Science (AAAS), 2022-12-20) Lee, D.; Pen, J. L.; Yatim, A.; Dong, B.; Aquino, Y.; Ogishi, M.; Pescarmona, R.; Talouarn, E.; Rinchai, D.; Zhang, P.; Perret, M.; Liu, Z.; Jordan, L.; Bozdemir, S. E.; Bayhan, G. I.; Beaufils, C.; Bizien, L.; Bisiaux, A.; Lei, W.; Hasan, M.; Chen, J.; Gaughan, C.; Asthana, A.; Libri, V.; Luna, Joseph M.; Jaffré, Fabrice; Hoffmann, H.; Michailidis, E.; Moreews, M.; Seeleuthner, Y.; Bilguvar, K.; Mane, S.; Flores, C.; Zhang, Y.; Arias, A. A.; Bailey, R.; Schlüter, A.; Milisavljevic, B.; Bigio, B.; Voyer, T. L.; Materna, M.; Gervais, A.; Moncada-Velez, M.; Pala, F.; Lazarov, T.; Levy, R.; Neehus, A.; Rosain, J.; Peel, J.; Chan, Y.; Morin, M.; Pino-Ramirez, R. M.; Belkaya, Serkan; Lorenzo, L.; Anton, J.; Delafontaine, S.; Toubiana, J.; Bajolle, F.; Fumadó, V.; DeDiego, M. L.; Fidouh, N.; Rozenberg, F.; Pérez-Tur, J.; Chen, S.; Evans, T.; Geissmann, F.; Lebon, P.; Weiss, S. R.; Bonnet, D.; Duval, X.; Cohort§, C.; Effort, C.; Pan-Hammarström, Q.; Planas, A. M.; Meyts, I.; Haerynck, F.; Pujol, A.; Sancho-Shimizu, V.; Dalgard, C.; Bustamante, J.; Puel, A.; Boisson-Dupuis, S.; Boisson, B.; Maniatis, T.; Zhang, Q.; Bastard, P.; Notarangelo, L.; Béziat, V.; Diego, R.; Rodriguez-Gallego, C.; Su, H. C.; Lifton, R. P.; Jouanguy, E.; Cobat, A.; Alsina, L.; Keles, S.; Haddad, E.; Abel, L.; Belot, A.; Quintana-Murci, L.; Rice, C. M.; Silverman, R. H.; Zhang, S.; Casanova, J.
Multisystem inflammatory syndrome in children (MIS-C) is a rare and severe condition that follows benign COVID-19. We report autosomal recessive deficiencies of OAS1, OAS2, or RNASEL in five unrelated children with MIS-C. The cytosolic double-stranded RNA (dsRNA)-sensing OAS1 and OAS2 generate 2'-5'-linked oligoadenylates (2-5A) that activate the single-stranded RNA-degrading ribonuclease L (RNase L). Monocytic cell lines and primary myeloid cells with OAS1, OAS2, or RNase L deficiencies produce excessive amounts of inflammatory cytokines upon dsRNA or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stimulation. Exogenous 2-5A suppresses cytokine production in OAS1-deficient but not RNase L-deficient cells. Cytokine production in RNase L-deficient cells is impaired by MDA5 or RIG-I deficiency and abolished by mitochondrial antiviral-signaling protein (MAVS) deficiency. Recessive OAS-RNase L deficiencies in these patients unleash the production of SARS-CoV-2-triggered, MAVS-mediated inflammatory cytokines by mononuclear phagocytes, thereby underlying MIS-C.
Open Access
Inborn errors of type I IFN immunity in patients with life-threatening COVID-19
(American Association for the Advancement of Science, 2020) Zhang, Q.; Liu, Z.; Moncada-Velez, M.; Chen, J.; Ogishi, M.; Bigio, B.; Yang, R.; Arias, A. A.; Zhou, Q.; Han, J. E.; Özçelik, Tayfun; Uğurbil, A. C.; Zhang, P.; Rapaport, F.; Li, J.; Spaan, A. N.
Clinical outcomes of human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection range from silent infection to lethal coronavirus disease 2019 (COVID-19). Epidemiological studies have identified three risk factors for severe disease: being male, being elderly, and having other medical conditions. However, interindividual clinical variability remains huge in each demographic category. Discovering the root cause and detailed molecular, cellular, and tissue- and body-levelmechanismsunderlying life-threatening COVID-19 is of the utmost biological and medical importance.
Open Access
An integrated femtosecond timing distribution system for XFELS
(Massachusetts Institute of Technology, 2006) Kim, J.; Burnham, J.; Chen, J.; Kartner, F. X.; İlday, Fatih Ömer; Ludwig, F.; Schlarb, H.; Winter, A.; Ferianis, M.; Cheever, D.
Tightly synchronized lasers and RF-systems with timing jitter in the few femtoseconds range are necessary sub-systems for future X-ray free electron laser facilities. In this paper, we present an optical-microwave phase detector that is capable of extracting an RF-signal from an optical pulse stream without amplitude-to-phase conversion. Extraction of a microwave signal with 3 fs timing jitter (from 1 Hz to 10 MHz) from an optical pulse stream is demonstrated. Scaling of this component to subfemtosecond resolution is discussed. Together with low noise mode-locked lasers, timing-stabilized optical fiber links and compact optical cross-correlators, a flexible femtosecond timing distribution system with potentially sub-10 fs precision over distances of a few kilometers can be constructed. Experimental results on both synchronized RF and laser sources will be presented.
Open Access
An integrated map of structural variation in 2,504 human genomes
(Nature Publishing Group, 2015) Sudmant, P. H.; Rausch, T.; Gardner, E. J.; Handsaker, R. E.; Abyzov, A.; Huddleston, J.; Zhang, Y.; Ye, K.; Jun, G.; Fritz, M. Hsi-Yang; Konkel, M. K.; Malhotra, A.; Stütz, A. M.; Shi, X.; Casale, F. P.; Chen, J.; Hormozdiari, F.; Dayama, G.; Chen, K.; Malig, M.; Chaisson, M. J. P.; Walter, K.; Meiers, S.; Kashin, S.; Garrison, E.; Auton, A.; Lam, H. Y. K.; Mu, X. J.; Alkan, C.; Antaki, D.; Bae, T.; Cerveira, E.; Chines, P.; Chong, Z.; Clarke, L.; Dal, E.; Ding, L.; Emery, S.; Fan, X.; Gujral, M.; Kahveci, F.; Kidd, J. M.; Kong, Y.; Lameijer, Eric-Wubbo; McCarthy, S.; Flicek, P.; Gibbs, R. A.; Marth, G.; Mason, C. E.; Menelaou, A.; Muzny, D. M.; Nelson, B. J.; Noor, A.; Parrish, N. F.; Pendleton, M.; Quitadamo, A.; Raeder, B.; Schadt, E. E.; Romanovitch, M.; Schlattl, A.; Sebra, R.; Shabalin, A. A.; Untergasser, A.; Walker J. A.; Wang, M.; Yu, F.; Zhang, C.; Zhang, J.; Zheng-Bradley, X.; Zhou, W.; Zichner, T.; Sebat, J.; Batzer, M. A.; McCarroll, S. A.; Mills, R. E.; Gerstein, M. B.; Bashir, A.; Stegle, O.; Devine, S. E.; Lee, C.; Eichler, E. E.; Korbel, J. O.
Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.
Open Access
Oncogenic kinase-induced PKM2 tyrosine 105 phosphorylation converts nononcogenic PKM2 to a tumor promoter and induces cancer stem-like cells
(American Association for Cancer Research, 2018) Zhou, Z.; Li, M.; Zhang, L.; Zhao, H.; Şahin, Özgür; Chen, J.; Zhao, J. J.; Songyang, Z.; Yu, D.
The role of pyruvate kinase M2 isoform (PKM2) in tumor progression has been controversial. Previous studies showed that PKM2 promoted tumor growth in xenograft models; however, depletion of PKM2 in the Brca1-loss–driven mammary tumor mouse model accelerates tumor formation. Because oncogenic kinases are frequently activated in tumors and PKM2 phosphorylation promotes tumor growth, we hypothesized that phosphorylation of PKM2 by activated kinases in tumor cells confers PKM2 oncogenic function, whereas nonphosphorylated PKM2 is nononcogenic. Indeed, PKM2 was phosphorylated at tyrosine 105 (Y105) and formed oncogenic dimers in MDA-MB-231 breast cancer cells, whereas PKM2 was largely unphosphorylated and formed nontumorigenic tetramers in nontransformed MCF10A cells. PKM2 knockdown did not affect MCF10A cell growth but significantly decreased proliferation of MDA-MB-231 breast cancer cells with tyrosine kinase activation. Multiple kinases that are frequently activated in different cancer types were identified to phosphorylate PKM2-Y105 in our tyrosine kinase screening. Introduction of the PKM2-Y105D phosphomimetic mutant into MCF10A cells induced colony formation and the CD44hi/ CD24neg cancer stem–like cell population by increasing Yesassociated protein (YAP) nuclear localization. ErbB2, a strong inducer of PKM2-Y105 phosphorylation, boosted nuclear localization of YAP and enhanced the cancer stem–like cell population. Treatment with the ErbB2 kinase inhibitor lapatinib decreased PKM2-Y105 phosphorylation and cancer stem–like cells, impeding PKM2 tumor-promoting function. Taken together, phosphorylation of PKM2-Y105 by activated kinases exerts oncogenic functions in part via activation of YAP downstream signaling to increase cancer stem–like cell properties. Significance: These findings reveal PKM2 promotes tumorigenesis by inducing cancer stem-like cell properties and clarify the paradox of PKM2's dichotomous functions in tumor progression.
Open Access
Rank reduction for the local consistency problem
(A I P Publishing, 2012) Chen, J.; Ji, Z.; Klyachko, A.; Kribs, D. W.; Zeng, B.
We address the problem of how simple a solution can be for a given quantum local consistency instance. More specifically, we investigate how small the rank of the global density operator can be if the local constraints are known to be compatible. We prove that any compatible local density operators can be satisfied by a low rank global density operator. Then we study both fermionic and bosonic versions of the N-representability problem as applications. After applying the channel-state duality, we prove that any compatible local channels can be obtained through a global quantum channel with small Kraus rank. © 2012 American Institute of Physics.
Open Access
Resonant cavity based compact efficient antireflection structures for photonic crystals
(Institute of Physics Publishing Ltd., 2007) Li, Z.; Özbay, Ekmel; Chen, H.; Chen, J.; Yang, F.; Zheng, H.
We propose an effective admittance (EA) method to design antireflection structures for two-dimensional photonic crystals (PCs). We demonstrate that a compact and efficient antireflection structure, which is difficult to obtain by the conventional admittance matching method, can be readily designed by the EA method. The antireflection structure consists of an air slot resonant cavity that is constructed only with the materials that constitute the PC. Compared with a bare PC, the reflection from a PC with an antireflection structure is reduced by two orders of magnitude over a wide bandwidth. To confirm the presented EA method, finite-difference time-domain (FDTD) simulations are performed, and the results from the FDTD and the EA method are in good agreement.
Open Access
Ultra-low timing-jitter passively mode-locked fiber lasers for long-distance timing synchronization
(SPIE, 2006) İlday, F. Ömer; Winter, A.; Kim J.-W.; Chen, J.; Schmüser, P.; Schlarb, H.; Kärtner, F. X.
One of the key challenges for the next-generation light sources such as X-FELs is to implement a timing stabilization and distribution system to enable ∼ 10 fs synchronization of the different RF and laser sources distributed in such facilities with distances up to a few kilometers. These requirements appear to be beyond the capability of traditional RF distribution systems based on temperature-stabilized coaxial cables. A promising alternative is to use an optical transmission system: A train of pulses generated from a laser with low timing jitter is distributed over length-stabilized fiber links to remote locations. The repetition frequency of the pulse train and its higher harmonics contain the synchronization information. At the remote locations, RF signals are extracted simply by using a photodiode and a suitable bandpass filter to pick the desired harmonic of the laser repetition rate. Passively mode-locked Er-doped fiber lasers provide excellent long-term stability. The laser must have extremely low timing jitter, particularly at high frequencies (>1 kHz). Ultimately, the timing jitter is limited by quantum fluctuations in the number of photons making up the pulse and the incoherent photons added in the cavity due to spontaneous emission. The amplitude and phase noise of a home-built laser, generating 100-fs, 1-nJ pulses, was characterized. The measured phase noise (timing jitter) is sub-10 fs. from 1 kHz to Nyquist frequency. In addition to synchronization of accelerators, the ultra-low timing jitter pulse source can find applications in next-generation telecommunication systems.