bluuebunny/crossref_metadata_2025 huggingface dataset:
Low-noise octave spanning mid-infrared supercontinuum generation in a large core chalcogenide fiber
Authors: Eslami Zahra , Ryczkowski Piotr , Salmela Lauri , Genty Goery
Published: September 2020

We report the generation of a low noise, octave-spanning supercontinuum in the normal dispersion regime of a multimode chalcogenide fiber with 100 μm core size. The noise performances are characterized in different output wavelength bands.
Athermal Operation of Multi-Section Surface Grating Lasers for Applications including Burst-Mode for TWDM-PONs
Authors: Mickus Dovydas , Jain Gaurav , Naimi Sepideh , McKenna Robert , Murphy Caolän , Donegan John F
Published: September 2020

A multi-section laser with high order surface grating is operated athermally with variation in wavelength of 0.003 nm (±375 MHz) from 10-100 °C. In pulsed operation emulating burst mode, the maximum variation is 0.05 nm.
Sub-Doppler Double-Resonance Spectroscopy of Methane Using a Frequency Comb Probe
Authors: Foltynowicz Aleksandra , Rutkowski Lucile , Silander Isak , Johansson Alexandra C. , de Oliveira Vinicius Silva , Axner Ove , Soboń Grzegorz , Martynkien Tadeusz , Mergo Pawel , Lehmann Kevin K.
Published: September 2020

We use a 3.3 μm continuous wave optical parametric oscillator as a pump and a 1.67 μm frequency comb as a probe to record 36 sub-Doppler double-resonance transitions in the 3v3 band of methane (including 26 previously unreported) with ~1.5 MHz center frequency accuracy
Optical Frequency Comb Calibrated Near Infrared Solar Heterodyne Spectroscopy
Authors: Fredrick Connor , Olsen Freja , Terrien Ryan , Mahadevan Suvrath , Quinlan Franklyn , Diddams Scott
Published: September 2020

We perform heterodyne spectroscopy at 1.56 μm with a tunable laser and thermal radiation from the Sun. The laser tuning is calibrated with a frequency comb, providing a simple spectrometer with resolving power of 2,000,000.
Spatiotemporal Characterization of Optical Vortex Light-wave using Hyperspectral Dual-comb Imaging
Authors: Asahara Akifumi , Adachi Takuto , Akiyama Seishiro , Minoshima Kaoru
Published: September 2020

We demonstrate detailed assessment of optical vortex using single-pixel-imaging dual-comb spectroscopy. The method realizes full-characterization of vortex light-waves and is expected as a versatile measurement tool for chiral optical properties, material studies, and optical communications.
Pico-second single-pulse three-dimensional imaging with an optical frequency comb
Authors: Kato Takashi , Ishii Hirotaka , Terada Kazuhiro , Moritoh Tamaki , Minoshima Kaoru
Published: September 2020

Using a 15-ps chirped pulse of an optical frequency comb, fully single pulse, 3D surface profile imaging is demonstrated, at 100-square-pixel resolution and μm-level uncertainty. Electric- field-induced picosecond transient 3D imaging is also demonstrated.
High power cryogenic Yb:YLF regenerative amplifier
Authors: Pergament Mikhail , Demirbas Umit , Cankaya Huseyin , Hua Yi , Thesinga Jelto , Kärtner Franz X.
Published: September 2020

We present a cryogenic Yb:YLF regenerative amplifier which can reach 70 W average power and 20 mJ output energy at 3.5 kHz operation. The output beam quality factor is better than 1.05 in both axes.
Compact high-power Ytterbium thin-disk laser based on kaleidoscopic reflections of pump radiation
Authors: Ewers Benjamin , Lorbeer Raoul-Amadeus , Fischer Alexander , Speiser Jochen , Dekorsy Thomas
Published: September 2020

We present a new pump geometry for compact thin disk lasers. Multi-pass pumping without external pump optics is realized with a uniquely coated wedged laser medium. CW output powers of 700 W are achieved.
Broadband Ultraviolet Generation with 50% Conversion Efficiency in Hollow Capillary Fibers
Authors: Belli Federico , Lekosiotis Athanasios , Travers John. C.
Published: September 2020

We demonstrate broadband wavelength up-conversion (240-320 nm) based on a seeded four-wave mixing scheme in gas-filled stretched hollow-capillary fibers with 50% conversion efficiency. Our technique is scalable in energy from the nJ to mJ level.
Role of Van Hove singularities on high harmonic generation in solids by high power mid-IR pulses
Authors: Kanai Tsuneto , Lee Yeon , Kim Dong Eon
Published: September 2020

We generate high harmonics in polycrystalline and monocrystalline ZnSe with a high-power mid-IR optical parametric amplifier. By using its high photon flux and wave-length tunability, novel paths based on Van Hove singularities were observed.
Generation of a dissipative Kerr-microresonator soliton comb pumped by a MHz linewidth DFB laser
Authors: Nishimoto K. , Minoshima K. , Yasui T. , Kuse N.
Published: September 2020

We generate a dissipative Kerr-microresonator soliton comb pumped by a MHz linewidth distributed feedback laser. The phase noise of a soliton comb mode and the timing jitter of the soliton comb are also measured.
Engineered generation of a specfic Stokes order in stimulated Raman scattering process
Authors: Liu W. , Ohae C. , Zheng J. , Tahara S. , Suzuki M. , Minoshima K. , Katsuragawa M.
Published: September 2020

We present how to engineer nonlinear optical process with a wide freedom, by arbitrarily manipulating relative phases among relevant electromagnetic fields. We report engineered generation of 1st order stimulated Raman scattering with near 50% quantum efficiency.
Integrated Opto-Electronic Circulator for Radio-over-Fiber Links
Authors: Pinna Sergio , Gambini Fabrizio , Klamkin Jonathan
Published: September 2020

A silicon photonics integrated opto-electronic circulator for radio over fiber links is proposed and experimentally demonstrated. The device shows an isolation between optical ports above 20 dB over the 0-20 GHz range.
On-Chip Dispersive Phase Filters for Photonic Signal Processing
Authors: Kaushal Saket , Azana José
Published: September 2020

We propose a dispersive phase filter design suitable for compact integration using waveguide Bragg gratings in silicon. On-chip 2x pulse repetition-rate multiplication of a 10-GHz pulse train by Talbot effect is experimentally demonstrated using this design.
Silicon Photonic Weights for Microwave Photonic Canceller
Authors: Blow Eric C. , Huang Chaoran , Liu Zheng , Markoff Samuel J. , Prucnal Paul R.
Published: September 2020

Demonstration of silicon integrated linear filter, comprised of tunable weights, implemented as the matched filter of a microwave photonic canceller. Achieving 34 dB of weighting and 20 dB of cancellation over 6 GHz operating range.
Photonic Assisted Microwave Near-field Imager
Authors: Ashtiani Farshid , Aflatouni Firooz
Published: September 2020

The first nanophotonic near-field imager is reported that optically processes the received microwave signals to form a 121-pixel image. It achieves 4.8° spatial resolution and is significantly smaller and more power efficient than the all-electrical counterparts.
Passively Q-switched Er3+-doped ZBLAN fiber laser at ~3.5 μm based on a semiconductor saturable absorber mirror
Authors: Fang Zhiqiang , Deng Zhixiang , Liu Jun , Chen Yu , Fan Dianyuan
Published: September 2020

By adopting a GaAs based semiconductor saturable absorber mirror (SESAM), we have demonstrated a ~3.5 μm passively Q-switched Er3+:ZBLAN fiber laser. The recorded highest repetition rate is 58.71 kHz with a pulse width of 2.4 μs and pulse energy of ~1 pJ. The results prove that the GaAs based SESAM has the capacity to extend the operation wavelength to midinfrared region beyond 3.0 μm.
High Power Er Fiber Comb Beyond Soliton Compression
Authors: Lee Kevin F. , Zhou Gengji , Schunemann Peter G. , Jiang Jie , Fermann M. E.
Published: September 2020

We demonstrate a 5 W, 65 fs single-mode Er fiber comb based on efficient pulse compression of gain-narrowed pulses from a chirped pulse amplification system. We generate two octaves of midinfrared with these pulses.
38μJ Short-Wavelength Q-Switched Thulium Fiber Laser
Authors: Pidishety Shankar , Barua Pranabesh , Rojas Hernândez Pablo G. , Belal Mohammad , Velâzquez M. Nunez- , Sahu Jayanta K. , Nilsson Johan
Published: September 2020

38 μI pulse energy with peak power of 1.3 kW and 0.3 nm spectral width is obtained at a short-wavelength of 1851 nm by actively Q-switching a thulium fiber laser cladding-pumped at 792 nm.
High power optical breakdown and design to combat dirt and airborne particles
Authors: Talghader Joseph J.
Published: September 2020

Traditional design involves the spectrum of an optic, but mechanical and materials properties must be included for high CW power. High bandgaps and low stress maximize damage thresholds in the presence of dirt and particles.
Large Scale Fabrication of Graphene Nanostructures
Authors: Siegel Joel , Dwyer Jonathan H. , Suresh Anjali , Fortman Margaret , Gopalan Padma , Brar Victor W.
Published: September 2020

Patterning graphene into nanostructures enables the coupling of free space radiation to plasmons in graphene. Here, we demonstrate block copolymer based fabrication can create sub 20 nm nanostructures in a scalable, efficient, and repeatable manner.
Towards active photonic dispersion control using graphene-induced non-radiative loss
Authors: Lhuillier Jerémy , Demongodin Pierre , Wood Thomas , Kemiche Malik , Vilquin Bertrand , Grenet Genevieve , Cueff Sebastien , Rojo-Romeo Pedro , Letartre Xavier , Monat Christelle
Published: September 2020

We show that the photonic dispersion of a two coupled-mode system can be actively tuned using graphene-induced non-radiative loss. Our implementation exploits the spatial modulation of graphene’s absorption via patterned oxide substrates.
Observation and Theory of Magneto-Electric Field-induced Second Harmonic Generation
Authors: Smail Gregory , Trinh M.Tuan , Makhal Krishnandu , Yang Da S. , Kim Jinsang , Rand Stephen C.
Published: September 2020

We report induced second harmonic generation mediated by a new rectification nonlinearity in polycrystalline pentacene. Results with tilted pulse fronts confirm its magnetoelectric origin, ultrafast character and capability of supporting all-optical switching at right angles.
Observation of the Quantum Nature of Laser-Driven Particle Acceleration
Authors: Adiv Yuval , Wang Kangpeng , Dahan Raphael , Broaddus Payton , Miao Yu , Black Dylan , Leedle Kenneth , Solgaard Olav , England Joel , Kaminer Ido
Published: September 2020

We reveal quantum features in laser acceleration arising from the underlying wave nature of the accelerated electron, and we present the first accelerator on-chip operated inside a transmission electron microscope.
Advances in spatial frequency modulation imaging techniques for applications from advanced manufacturing to the neurosciences
Authors: Squier Jeff , Field Jeffrey J. , Bartels Randy
Published: September 2020

Spatial frequency modulation imaging has a broad range of attributes: enhanced resolution in linear and nonlinear imaging modalities, phase sensitivity, and random access capability. Advanced manufacturing to the neurosciences can benefit from this unique optical metrology.
A real-time radio frequency spectrum analyzer with 1.8 THz bandwidth
Authors: Wang Ruolan , Chen Liao , Hu Hao , Zhao Yanjing , Zhang Chi , Zhang Xinliang
Published: September 2020

An optimized real-time radio frequency spectrum analyzer is experimentally demonstrated. By eliminating third-order dispersion, the bandwidth is increased by 2.25 times up to 1.8-THz and 1-GHz resolution is achieved with 20-MHz frame rate.
2 GHz Regeneratively Mode-locked Laser at 2 Micron
Authors: Qin Jiarong , Jiang Weiqi , Zhu Shiyu , Dai Ruihong , Meng Yafei , Li Yao , Wang Fengqiu
Published: September 2020

We have for the first time demonstrated a 2 GHz regeneratively mode-locked 2 gm fiber laser with PLL operation, which shows long-term stability. Such a source is well suited for soliton communications and signal processing.
Tunable terahertz radiation generation using the beating of two super-Gaussian laser beams in the collisional nanocluster plasma
Authors: Nemati Siahmazgi R. , Jafari S.
Published: September 2020

In this paper, generation of terahertz (THz) radiation by the beating of two super-Gaussian laser beams in a nanocluster plasma is investigated, theoretically. The electric field of laser beams interacts with the nanocluster, leads to the ionization of the cluster atoms, and produces nanoplasma. Interaction of laser beams with the electronic clouds of nanoplasma generates ponderomotive force that leads to the creation of a macroscopic electron current at the beat frequency, which can generate THz radiation. The THz wave equations and THz efficiency in the nanoplasma medium were analytically derived and then numerically discussed. The effects of the density and radius of nanoclusters, the laser beam width, and intensity of super-Gaussian lasers on the THz radiation efficiency have been investigated in the nanoplasma-based THz emitter. The results indicated when the beat-wave frequency approaches the effective plasmon frequency of the nanoplasma, the maximum value of the field amplitude of THz radiation is acquired. In addition, it was found that the best situation that can be obtained is the THz radiation occurring at frequency

ω
=

ω
p

/

3

.
Inversion probability enhancement of all-fiber CDWL by noise modeling and robust fitting
Authors: Wei Tianwen , Xia Haiyun , Wu Yunbin , Yuan Jinlong , Wang Chong , Dou Xiankang
Published: September 2020

Accurate power spectrum analysis of weak backscattered signals are the primary constraint in long-distance coherent Doppler wind lidar (CDWL) applications. To study the atmospheric boundary layer, an all-fiber CDWL with 300µJ pulse energy is developed. In principle, the coherent detection method can approach the quantum limit sensitivity if the noise in the photodetector output is dominated by the shot noise of the local oscillator. In practice, however, abnormal power spectra occur randomly, resulting in error estimation and low inversion probability. This phenomenon is theoretically analyzed and shown to be due to the leakage of a time-varying DC noise of the balanced detector. Thus, a correction algorithm with accurate noise modeling is proposed and demonstrated. The accuracy of radial velocity, carrier-to-noise ratio (CNR), and spectral width are improved. In wind profiling process, a robust sine-wave fitting algorithm with data quality control is adopted in the velocity-azimuth display (VAD) scanning detection. Finally, in 5-day continuous wind detection, the inversion probability is tremendously enhanced. As an example, it is increased from 8.6% to 52.1% at the height of 4 km.
Nitride light-emitting diodes for cryogenic temperatures
Authors: Chlipala Mikolaj , Turski Henryk , Siekacz Marcin , Pieniak Katarzyna , Nowakowski-Szkudlarek Krzesimir , Suski Tadeusz , Skierbiszewski Czeslaw
Published: September 2020

A novel approach to fabricate efficient nitride light-emitting diodes (LEDs) grown on gallium polar surface operating at cryogenic temperatures is presented. We investigate and compare LEDs with standard construction with structures where p-n junction field is inverted through the use of bottom tunnel junction (BTJ). BTJ LEDs show improved turn on voltage, reduced parasitic recombination and increased quantum efficiency at cryogenic temperatures. This is achieved by moving to low resistivity n-type contacts and nitrogen polar-like built-in field with respect to current flow. It inhibits the electron overflow past quantum wells and improves hole injection even at T=12K. Therefore, as cryogenic light sources, BTJ LEDs offer significantly enhanced performance over standard LEDs.
Demonstration of a multi-color diffractive lens with adjustable focal length
Authors: Bregenzer N. , Öttl T. , Zobernig M. , Bawart M. , Bernet S. , Ritsch-Marte M.
Published: September 2020

A pair of combined diffractive optical elements (DOEs) realizes a so-called moiré lens, with an optical power which can be tuned by a mutual rotation of the two DOEs around their central optical axis. Earlier demonstrated moiré lenses still suffered from chromatic aberrations. Here we experimentally investigate a multi-color version of such a lens, realized by a pair of multi-order DOEs. These DOEs have a deeper surface structure which modulates the phase of the transmitted light wave by several multiples of 2π. The corresponding multi-order moiré lenses all have the same focal length at a fixed set of harmonic wavelengths within the white light spectrum. The experiments demonstrate that multi-order moiré lenses have significantly reduced chromatic aberrations. We investigate the performance of the lens for narrow band and white light imaging applications.
Electrically generated optical waveguide in a lithium-niobate thin film
Authors: Chen Qingming , Zhu Yujiao , Wu Di , Li Tenghao , Li Zhaohui , Lu Chao , Chiang Kin Seng , Zhang Xuming
Published: September 2020

This paper reports an electrically generated optical waveguide for the transverse-magnetic wave. The waveguide is formed in a z-cut single-crystal lithium-niobate (LN) thin film by the electro-optic effect, where the extraordinary refractive index (RI) of the LN film is increased by a voltage applied to patterned electrodes that define the waveguide geometry. Such a waveguide can be made to exist or disappear by turning on or off the applied voltage. A straight waveguide and an S-bend waveguide with an RI contrast of ∼0.004 are generated at a voltage of 200 V. The propagation loss of the generated waveguide measured at the wavelength 532 nm is 1.8 dB/cm. Electrically generated optical waveguides could fulfill useful functions in photonic integrated circuits, such as reconfigurable cross connect and switching that require wavelength-independent and mode-independent operation.
Color discrimination metric based on the neutrality of lighting and hue transposition quantification
Authors: Liu Qiang , Liu Ying , Pointer Michael R. , Huang Zheng , Wu Xinwei , Chen Zhiyu , Luo Ming Ronnier
Published: September 2020

In this Letter, we propose a combined metric for quantifying the color discrimination capability of white light sources. This metric is based on considerations of human visual adaptation to daylight chromaticities, as well as on the concerns of the huge spectral diversity of modern light sources. Two existing metrics,

S

n
e
u
t
r
a
l

(degree of neutrality) by Smet et al. [Opt. Express 22, 25830 (2014)OPEXFF1094-408710.1364/OE.22.025830] and

R

d

(hue transposition among color samples of the Farnsworth–Munsell 100 Hue color vision test) by Esposito and Houser [Lighting Res. Technol. 51, 5 (2019)LRTEA90024-342610.1177/1477153517729200] were adopted, and their weights were determined by a meta-analysis of five groups of psychophysical data on color discrimination. The superiority of the newly proposed metric was demonstrated by 16 groups of psychophysical data from eight color discrimination studies, as well as by a comparison with 29 typical color quality metrics and their linear combinations.
Fully reconfigurable coherent optical vector–matrix multiplication
Authors: Spall James , Guo Xianxin , Barrett Thomas D. , Lvovsky A. I.
Published: September 2020

Optics is a promising platform in which to help realize the next generation of fast, parallel, and energy-efficient computation. We demonstrate a reconfigurable free-space optical multiplier that is capable of over 3000 computations in parallel, using spatial light modulators with a pixel resolution of only

340
×
340

. This enables vector–matrix multiplication and parallel vector–vector multiplication with vector size of up to 56. Our design is, to the best of our knowledge, the first to simultaneously support optical implementation of reconfigurable, large-sized, and real-valued linear algebraic operations. Such an optical multiplier can serve as a building block of special-purpose optical processors such as optical neural networks and optical Ising machines.
Handheld swept-source optical coherence tomography guided by smartphone-enabled wide-field autofluorescence photography for imaging facial sebaceous glands
Authors: He Qinghua , Liu Teng , Wang Ruikang K.
Published: September 2020

We report a method to image facial sebaceous glands (SGs) using smartphone-enabled wide-field autofluorescence photography (AFP) and handheld swept-source optical coherence tomography (SS-OCT). Smartphone-enabled AFP provides a 2D wide-field fluorescence image that is used both as a functional mapping of the sebum and a positioning guidance for OCT imaging of the SG. Following the guidance, handheld SS-OCT conducts the volume scan to investigate depth-resolved conditions of the SG in the selected regions of interest. We show the results from smartphone-enabled AFP and handheld SS-OCT to demonstrate the ability of our method to image facial SGs, potentially useful for the assessment of skin conditions in dermatology and cosmetology.
Prediction of Air Flight Cancellation during COVID-19 using Deep Learning Methods
Authors: Bandyopadhyay Samir Kumar , Goyel Vishal , DUTTA SHAWNI
Published: September 2020

Air traffic is vulnerable to external factors, such as oil crises, natural disasters, economic recessions and disease outbreaks due to COVID-19. This reason seems to have a more severe and more rapid impact on air traffic numbers as sudden increases in flight cancellations, aircraft groundings and travel bans. Various Airways loose revenues and it is difficult for them to sustain for a long period. This problem as been facing the entire world. The reductions in passenger numbers are significant. It is due to flights being cancelled or planes flying empty between airports. It is in turn massively reducing revenues for airlines and forced many airlines to lay off employees or declare bankruptcy. Airways also have to attempt refunding cancelled trips in order to diminish their losses. The airliner manufacturers and airport operators have also laid off employees. According to some commentators, this crisis is the worst ever encountered in the history of the aviation industry.

Aircraft cancellation prediction is accomplished by utilising deep learning framework. In this framework, two dissimilar recurrent neural networks are assembled as a single entity while inferring the prediction results. Long-short term memory (LSTM) and Gated Recurrent Unit (GRU) are employed to design the proposed predictive model. This predictive model is compared against traditional neural network based Multi-layer perceptron model. Experimental results indicated an accuracy of 98.7% by the proposed model.
EXPERIMENTAL INVESTIGATION OF THE POZZOLANIC POTENTIALS OF MILLET AND RICE HUSK ASH AS MINERAL ADDITIVE IN SELF-COMPACTING CONCRETE
Authors: Wasiu John , Oluwatosin Fashina Ayoola
Published: September 2020

Self-compacting concrete (SCC) is a new concept of concrete mix which flows in a formwork and consolidates itself without the need for compaction. Effectively compacting concrete can be very difficult especially in areas with a high number of reinforcement. Millet Husk Ash (MHA) and Rice Husk Ash (RHA) are agricultural waste materials obtained from farm and burnt to ashes to discard them since they are environmental waste. This research is focused on finding the pozzolanic potentials of MHA and RHA as a mineral additive in SCC to see if it will improve its properties rather than discarding them as environmental waste. Laboratory investigations were carried out on normally vibrated concrete (NVC) and SCC using MHA and RHA as an additive at a 10 % replacement with cement. Workability tests were carried out following the BS specifications. ASTM 293 C was used for the Flexural Capacity test on the beam specimen. The Results of the workability tests using MHA and RHA as mineral additive are within the specified standard values. The compressive strength test also revealed that the SCC using MHA is about 12.8 % higher than the RHA and NVC at 28 days with densities of 2487.5, 2516.5 and 2437.5kg/m3 respectively. The Modulus of Rupture (MoR) and Split Tensile strength for MHA is 0-19.2 % and 17.2-22.2 % higher than the RHA and NVC respectively. It was concluded that the improvement in the pozzolanic properties of MHA and RHA may be due to the content of Lime (CaO), Silica ((SiO2)), Alumina (Al2O3), Iron oxide (Fe2O3) being greater than 70 % and in an accordance with BS 618 code. It is concluded that the MHA and RHA can find suitable applications in the SCC as a mineral additive rather than discarding them as environmental waste.
Space sound absorbers with next-generation materials: additional sound absorption for post-pandemic challenges in indoor acoustic environments
Authors: Sakagami Kimihiro , Okuzono Takeshi
Published: September 2020

In this study, we first point out the possible acoustic problems associated with the post-pandemic operation of built environments. Particularly, we focus on the problem of acoustic deficiency due to the lack of absorption. This deficiency, which is likely to be encountered in most enclosed spaces in a range of establishments, is due to the reduced number of audience members or users of the space as a result of social distancing. As one of the promising solutions to this problem, we introduce a sound absorption technique using three-dimensional space sound absorbers developed through our recent research projects. Significantly, the type of sound absorber propose herein is made of materials that are especially suited to hygiene considerations. The materials are microperforated panels (MPPs) and permeable membranes (PMs), both of which are easily washable and sanitised. Furthermore, we point out that three-dimensional MPP or PM space absorbers possess the additional value of aesthetic designability.
ASPECTOS EPIDEMIOLÓGICOS DE ACIDENTES OFÍDICOS REGISTRADOS NO ESTADO DO ACRE, BRASIL, ENTRE 2013-2017: UM ESTUDO ECOLÓGICO
Authors: Melo Priscila Araujo , Maqui Oscar Nestor Condo
Published: September 2020

O presente estudo objetiva analisar os acidentes ofídicos ocorridos e notificados no Estado do Acre, Brasil, entre 2013 e 2017, a fim de colaborar com o planejamento estratégico das ações de saúde. Métodos: estudo epidemiológico, observacional, seccional, descritivo e retrospectivo. Foram utilizados dados obtidos nos registros de notificação do Sistema de informação de Agravos de Notificação – SINAN. Foram incluídos no estudo todos os casos de acidente ofídico notificados ao SINAN no Estado do Acre entre 2013 e 2017. Os dados coletados foram processados pelo Microsoft Excel 2016. Resultados: foram notificados 2.456 casos no período, com prevalência média de 491 casos/ano. A maior frequência foi em vítimas de cor parda (81,15%), do sexo masculino (72,35%). Os acidentes classificados como leves totalizaram 52,19%; os que evoluíram com cura totalizaram 88,52%. A maioria dos acidentes foi causada por serpentes do gênero Bothrops (63,19%). Em 21,95% dos casos o tempo decorrido entre o acidente e o atendimento foi de 0 a 1 hora. A frequência de acidentes foi maior nos meses do verão e outono. Considerações finais: Ainda existem falhas no fluxo de informação do SINAN. Houve equívoco no reconhecimento do gênero da serpente por parte da equipe de saúde no momento do atendimento. Situações como essa justificam a importância de treinamentos regulares para os profissionais envolvidos na linha de frente da assistência desse tipo de acidente. Serão necessários outros estudos de caráter social, ambiental e de saúde para responder a questões que ficaram abertas após as reflexões realizadas ao fim desta pesquisa.
Em busca do desenvolvimento territorial rural do Matopiba: uma análise do Cerrado
Authors: De Jesus Andrade Widmarck Julienne
Published: September 2020

No Brasil, o crescimento da produção agrícola no Matopiba (Maranhão, Tocantins, Piauí e Bahia), nas últimas duas décadas, reascenderam o debate em torno do desenvolvimento territorial rural. Logo, este trabalho apresenta como hipótese, a ideia de que o crescimento econômico, a partir da  produção agrícola, produz desenvolvimento socioeconômico rural. A análise em questão utiliza dados da produção agrícola, do produto interno bruto e do indicador Firjan de desenvolvimento municipal, para os períodos de 1999 a 2017. Em termos metodológicos, utilizamos duas técnicas de exame de dados: a econometria espacial e a análise de dados em painel (GMM). Os resultados encontrados revelam um transbordamento do crescimento econômico para o tecido social, com ressalvas apenas no aumento da desigualdade de renda.
Estar y producir en colectivo. Reflexiones sobre los saberes desde la experiencia de los Espacios de Formación Integral de la Universidad de la República
Authors: Cavalli Dalla Rizza Valeria
Published: September 2020

En el presente artículo compartimos algunas reflexiones epistemológicas y teóricas en torno a la integralidad como perspectiva que requiere una transformación en la concepción de la ciencia y la enseñanza universitaria y que apuesta a coproducir conocimiento desde el diálogo de saberes. Nos centramos en la experiencia de los Espacios de Formación Integral de la Universidad de la República como dispositivos pedagógicos que favorecen la relación entre los sujetos y sus saberes y disponen a producir colectivamente desde el estar y ser juntos y juntas. Asimismo, introducimos algunos abordajes metodológicos y estrategias didácticas que promueven la participación y la coproducción. Finalmente, problematizamos sobre la propia noción de diálogo de saberes, pensándolo como meta y guía de la integralidad, y planteamos que los Espacios de Formación Integral permiten producir prácticas educativas a partir del diálogo entre actores y actoras involucrados.
Sato
Authors: Zhang Dan , Hulsebos Madelon , Suhara Yoshihiko , Demiralp Çağatay , Li Jinfeng , Tan Wang-Chiew
Published: September 2020

Detecting the semantic types of data columns in relational tables is important for various data preparation and information retrieval tasks such as data cleaning, schema matching, data discovery, and semantic search. However, existing detection approaches either perform poorly with dirty data, support only a limited number of semantic types, fail to incorporate the table context of columns or rely on large sample sizes for training data. We introduce Sato, a hybrid machine learning model to automatically detect the semantic types of columns in tables, exploiting the signals from the table context as well as the column values. Sato combines a deep learning model trained on a large-scale table corpus with topic modeling and structured prediction to achieve support-weighted and macro average F1 scores of 0.925 and 0.735, respectively, exceeding the state-of-the-art performance by a significant margin. We extensively analyze the overall and per-type performance of Sato, discussing how individual modeling components, as well as feature categories, contribute to its performance.
Capturing associations in graphs
Authors: Fan Wenfei , Jin Ruochun , Liu Muyang , Lu Ping , Tian Chao , Zhou Jingren
Published: September 2020

This paper proposes a class of graph association rules, denoted by GARs, to specify regularities between entities in graphs. A GAR is a combination of a graph pattern and a dependency; it may take as predicates ML (machine learning) classifiers for link prediction. We show that GARs help us catch incomplete information in schemaless graphs, predict links in social graphs, identify potential customers in digital marketing, and extend graph functional dependencies (GFDs) to capture both missing links and inconsistencies.

We formalize association deduction with GARs in terms of the chase, and prove its Church-Rosser property. We show that the satisfiability, implication and association deduction problems for GARs are coNP-complete, NP-complete and NP-complete, respectively, retaining the same complexity bounds as their GFD counterparts, despite the increased expressive power of GARs. The incremental deduction problem is DP-complete for GARs versus coNP-complete for GFDs. In addition, we provide parallel algorithms for association deduction and incremental deduction. Using real-life and synthetic graphs, we experimentally verify the effectiveness, scalability and efficiency of the parallel algorithms.
Aria
Authors: Lu Yi , Yu Xiangyao , Cao Lei , Madden Samuel
Published: September 2020

Deterministic databases are able to efficiently run transactions across different replicas without coordination. However, existing state-of-the-art deterministic databases require that transaction read/write sets are known before execution, making such systems impractical in many OLTP applications. In this paper, we present Aria, a new distributed and deterministic OLTP database that does not have this limitation. The key idea behind Aria is that it first executes a batch of transactions against the same database snapshot in an
execution phase
, and then deterministically (without communication between replicas) chooses those that should commit to ensure serializability in a
commit phase.
We also propose a novel deterministic reordering mechanism that allows Aria to order transactions in a way that reduces the number of conflicts. Our experiments on a cluster of eight nodes show that Aria outperforms systems with conventional nondeterministic concurrency control algorithms and the state-of-the-art deterministic databases by up to a factor of two on two popular benchmarks (YCSB and TPC-C).
The computation of optimal subset repairs
Authors: Miao Dongjing , Cai Zhipeng , Li Jianzhong , Gao Xiangyu , Liu Xianmin
Published: September 2020

Computing an optimal subset repair of an inconsistent database is becoming a standalone research problem and has a wide range of applications. However, it has not been well-studied yet. A tight inapproximability bound of the problem computing optimal subset repairs is still unknown, and there is still no existing algorithm with a constant approximation factor better than two. In this paper, we prove a new tighter inapproximability bound of the problem computing optimal subset repairs. We show that it is usually NP-hard to approximate it within a factor better than 17/16. An algorithm with an approximation ratio (2 - 1/2
σ-1
)is developed, where σ is the number of functional dependencies. It is the current best algorithm in terms of approximation ratio. The ratio can be further improved if there are a large amount of
quasi
-Turán clusters in the input database. Plenty of experiments are conducted on real data to examine the performance and the effectiveness of the proposed approximation algorithms in real-world applications.
CoopStore
Authors: Gan Edward , Bailis Peter , Charikar Moses
Published: September 2020

An emerging class of data systems partition their data and precompute approximate summaries (i.e., sketches and samples) for each segment to reduce query costs. They can then aggregate and combine the segment summaries to estimate results without scanning the raw data. However, given limited storage space each summary introduces approximation errors that affect query accuracy. For instance, systems that use existing mergeable summaries cannot reduce query error below the error of an individual precomputed summary. We introduce CoopStore, a query system that optimizes item frequency and quantile summaries for accuracy when aggregating over multiple segments. Compared to conventional mergeable summaries, CoopStore leverages additional memory available for summary construction and aggregation to derive a more precise combined result. This reduces error by up to 25 x over interval aggregations and 4.5 x over data cube aggregations on industrial datasets compared to standard summarization methods, with provable worst-case error guarantees.
SimTab
Authors: Liu Yu , Zou Lei , Ge Qian , Wei Zhewei
Published: September 2020

SimRank is a classic measure of vertex-pair similarity according to the structure of graphs. Top-
k
and thresholding SimRank queries are two important types of similarity search with numerous applications in web mining, social network analysis, spam detection, etc. However, extensive studies for SimRank most focus on single-pair and single-source queries and fail to provide any feasible solution for the top-
k
and thresholding queries, e.g., with theoretical accuracy guarantee or acceptable empirical performance. In this paper, we propose
SimTab
(SimRank queries with Tighter confidence bounds and multi-armed bandits) to answer top-
k
and thresholding queries in a unified manner. First, we integrate several techniques with random walk sampling to tighten the confidence bound of SimRank estimation, which enhances the query efficiency. Second, we answer top-
k
and thresholding queries from the perspective of the Multi-Armed Bandits (MAB) problems. The proposed algorithms significantly improve the theoretical efficiency over state of the art, whereas the algorithmic complexity closely matches the hardness of the problem. We further propose a novel sampling strategy specially tailored for node similarity queries, which improves both the theoretical and practical query efficiency of the MAB-based algorithms. Our method is the first with query accuracy guarantee for these two queries, and the sole algorithm to achieve high-quality query results on large graphs. Moreover, all proposed algorithms are index-free, and thus can be naturally applied to dynamic graphs.

Extensive experiments on several large graph datasets demonstrate that our algorithms achieve much superior effectiveness with comparable or less query time cost than all index-free and index-based state of the art. Besides, our work proposes the first thorough empirical evaluation of the existing SimRank algorithms over top-
k
and thresholding queries.
Hypergraph motifs
Authors: Lee Geon , Ko Jihoon , Shin Kijung
Published: September 2020

Hypergraphs naturally represent group interactions, which are omnipresent in many domains: collaborations of researchers, co-purchases of items, joint interactions of proteins, to name a few. In this work, we propose tools for answering the following questions in a systematic manner: (
Q1
) what are structural design principles of real-world hypergraphs? (
Q2
) how can we compare local structures of hypergraphs of different sizes? (
Q3
) how can we identify domains which hypergraphs are from? We first define
hypergraph motifs
(h-motifs), which describe the connectivity patterns of three connected hyperedges. Then, we define the significance of each h-motif in a hypergraph as its occurrences relative to those in properly randomized hypergraphs. Lastly, we define the
characteristic profile
(CP) as the vector of the normalized significance of every h-motif. Regarding Q1, we find that h-motifs' occurrences in 11 real-world hypergraphs from 5 domains are clearly distinguished from those of randomized hypergraphs. In addition, we demonstrate that CPs capture local structural patterns unique to each domain, and thus comparing CPs of hypergraphs addresses Q2 and Q3. Our algorithmic contribution is to propose MoCHy, a family of parallel algorithms for counting h-motifs' occurrences in a hypergraph. We theoretically analyze their speed and accuracy, and we show empirically that the advanced approximate version MoCHy-A
+
is up to 25X more accurate and 32X faster than the basic approximate and exact versions, respectively.
A benchmarking study of embedding-based entity alignment for knowledge graphs
Authors: Sun Zequn , Zhang Qingheng , Hu Wei , Wang Chengming , Chen Muhao , Akrami Farahnaz , Li Chengkai
Published: September 2020

Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the same real-world object. Recent advancement in KG embedding impels the advent of embedding-based entity alignment, which encodes entities in a continuous embedding space and measures entity similarities based on the learned embeddings. In this paper, we conduct a comprehensive experimental study of this emerging field. We survey 23 recent embedding-based entity alignment approaches and categorize them based on their techniques and characteristics. We also propose a new KG sampling algorithm, with which we generate a set of dedicated benchmark datasets with various heterogeneity and distributions for a realistic evaluation. We develop an open-source library including 12 representative embedding-based entity alignment approaches, and extensively evaluate these approaches, to understand their strengths and limitations. Additionally, for several directions that have not been explored in current approaches, we perform exploratory experiments and report our preliminary findings for future studies. The benchmark datasets, open-source library and experimental results are all accessible online and will be duly maintained.
Effectively learning spatial indices
Authors: Qi Jianzhong , Liu Guanli , Jensen Christian S. , Kulik Lars
Published: September 2020

Machine learning, especially deep learning, is used increasingly to enable better solutions for data management tasks previously solved by other means, including database indexing. A recent study shows that a neural network can not only learn to predict the disk address of the data value associated with a one-dimensional search key but also outperform B-tree-based indexing, thus promises to speed up a broad range of database queries that rely on B-trees for efficient data access. We consider the problem of learning an index for two-dimensional spatial data. A direct application of a neural network is unattractive because there is no obvious ordering of spatial point data. Instead, we introduce a rank space based ordering technique to establish an ordering of point data and group the points into blocks for index learning. To enable scalability, we propose a recursive strategy that partitions a large point set and learns indices for each partition. Experiments on real and synthetic data sets with more than 100 million points show that our learned indices are highly effective and efficient. Query processing using our indices is more than an order of magnitude faster than the use of R-trees or a recently proposed learned index.
MorphStore
Authors: Damme Patrick , Ungethüm Annett , Pietrzyk Johannes , Krause Alexander , Habich Dirk , Lehner Wolfgang
Published: September 2020

In this paper, we present
MorphStore
, an open-source in-memory columnar analytical query engine with a novel
holistic compression-enabled
processing model. Basically, compression using lightweight integer compression algorithms already plays an important role in existing in-memory column-store database systems, but mainly for base data. In particular, during query processing, these systems only keep the data compressed until an operator cannot process the compressed data directly, whereupon the data is decompressed, but not recompressed. Thus, the full potential of compression during query processing is not exploited. To overcome that, we developed a novel compression-enabled processing model as presented in this paper. As we are going to show, the continuous usage of compression for all base data and all intermediates is very beneficial to reduce the overall memory footprint as well as to improve the query performance.
Deep or simple models for semantic tagging?
Authors: Li Jinfeng , Li Yuliang , Wang Xiaolan , Tan Wang-Chiew
Published: September 2020

Semantic tagging, which has extensive applications in text mining, predicts whether a given piece of text conveys the meaning of a given semantic tag. The problem of semantic tagging is largely solved with supervised learning and today, deep learning models are widely perceived to be better for semantic tagging. However, there is no comprehensive study supporting the popular belief. Practitioners often have to train different types of models for each semantic tagging task to identify the best model. This process is both expensive and inefficient.

We embark on a systematic study to investigate the following question: Are deep models the best performing model for all semantic tagging tasks? To answer this question, we compare deep models against "simple models" over datasets with varying characteristics. Specifically, we select three prevalent deep models (i.e. CNN, LSTM, and BERT) and two simple models (i.e. LR and SVM), and compare their performance on the semantic tagging task over 21 datasets. Results show that the size, the label ratio, and the label cleanliness of a dataset significantly impact the quality of semantic tagging. Simple models achieve similar tagging quality to deep models on large datasets, but the runtime of simple models is much shorter. Moreover, simple models can achieve better tagging quality than deep models when targeting datasets show worse label cleanliness and/or more severe imbalance. Based on these findings, our study can systematically guide practitioners in selecting the right learning model for their semantic tagging task.
Finding large diverse communities on networks
Authors: Zhou Alexander , Wang Yue , Chen Lei
Published: September 2020

In this work we examine the problem of finding large, diverse communities on graphs where the users are separated into distinct groups. More specifically, this work considers diversity to be the inclusion of users from multiple groups as opposed to homogeneous communities in which the majority of users are from one group. We design and propose the
k
*
-Partite Clique (and the
edge-maximum k
*
-Partite Clique Problem) which modifies the
k
-Partite Clique structure as a means to capture these large, diverse communities in a way that does not currently exist. We then design a non-trivial baseline enumeration algorithm, which is further improved via heuristics to significantly reduce the running time whilst avoiding excessive memory requirements. Moreover, we propose a core as well as a truss structure for the
k
-Partite environment aimed at finding the
edge-maximum k
*
-Partite Clique structure on the network. Comprehensive experiments on real-world datasets verify both the effectiveness of the
k
*
-Partite Clique at finding diverse communities as well as the efficiency of the proposed heuristics to our algorithms compared to reasonable baselines.
Mosaic
Authors: Vogel Lukas , Leis Viktor , van Renen Alexander , Neumann Thomas , Imamura Satoshi , Kemper Alfons
Published: September 2020

Relational database systems are purpose-built for a specific storage device class (e.g., HDD, SSD, or DRAM). They do not cope well with the multitude of storage devices that are competitive at their price `sweet spots'. To make use of different storage device classes, users have to resort to workarounds, such as storing data in different tablespaces. A lot of research has been done on heterogeneous storage frameworks for distributed big data query engines. These engines scale well for big data sets but are often CPU- or network-bound. Both approaches only maximize performance for previously purchased storage devices.

We present Mosaic, a storage engine for scan-heavy workloads on RDBMS that manages devices in a tierless pool and provides device purchase recommendations for a specified workload and budget. In contrast to existing systems, Mosaic generates a performance/budget curve that is Pareto-optimal, along which the user can choose. Our approach uses device models and linear optimization to find a data placement solution that maximizes I/O throughput for the workload. Our evaluation shows that Mosaic provides a higher throughput at the same budget or a similar throughput at a lower budget than the state-of-the-art approaches of big data query engines and RDBMS.