Theoretical results on a weightless neural classifier and application to computational linguistics

WiSARD é um classificador n-upla, historicamente usado em tarefas de reconhecimento de padrões em imagens em preto e branco. Infelizmente, não era comum que este fosse usado em outras tarefas, devido á sua incapacidade de arcar com grandes volumes de dados por ser sensível ao conteúdo aprendido. Recentemente, a técnica de bleaching foi concebida como uma melhoria à arquitetura do classificador n-upla, como um meio de coibir a sensibilidade da WiSARD. Desde então, houve um aumento na gama de aplicações construídas com este sistema de aprendizado. Pelo uso frequente de corpora bastante grandes, a etiquetação gramatical multilíngue encaixa-se neste grupo de aplicações. Esta tese aprimora o mWANN-Tagger, um etiquetador gramatical sem peso proposto em 2012. Este texto mostra que a pesquisa em etiquetação multilíngue com WiSARD foi intensificada através do uso de linguística quantitativa e que uma configuração de parâmetros universal foi encontrada para o mWANN-Tagger. Análises e experimentos com as bases da Universal Dependencies (UD) mostram que o mWANN-Tagger tem potencial para superar os etiquetadores do estado da arte dada uma melhor representação de palavra. Esta tese também almeja avaliar as vantagens do bleaching em relação ao modelo tradicional através do arcabouço teórico da teoria VC. As dimensões VC destes foram calculadas, atestando-se que um classificador n-upla, seja WiSARD ou com bleaching, que possua N memórias endereçadas por n-uplas binárias tem uma dimensão VC de exatamente N (2n − 1) + 1. Um paralelo foi então estabelecido entre ambos os modelos, onde deduziu-se que a técnica de bleaching é uma melhoria ao método n-upla que não causa prejuízos à sua capacidade de aprendizado.WiSARD é um classificador n-upla, historicamente usado em tarefas de reconhecimento de padrões em imagens em preto e branco. Infelizmente, não era comum que este fosse usado em outras tarefas, devido á sua incapacidade de arcar com grandes volumes de dados por ser sensível ao conteúdo aprendido. Recentemente, a técnica de bleaching foi concebida como uma melhoria à arquitetura do classificador n-upla, como um meio de coibir a sensibilidade da WiSARD. Desde então, houve um aumento na gama de aplicações construídas com este sistema de aprendizado. Pelo uso frequente de corpora bastante grandes, a etiquetação gramatical multilíngue encaixa-se neste grupo de aplicações. Esta tese aprimora o mWANN-Tagger, um etiquetador gramatical sem peso proposto em 2012. Este texto mostra que a pesquisa em etiquetação multilíngue com WiSARD foi intensificada através do uso de linguística quantitativa e que uma configuração de parâmetros universal foi encontrada para o mWANN-Tagger. Análises e experimentos com as bases da Universal Dependencies (UD) mostram que o mWANN-Tagger tem potencial para superar os etiquetadores do estado da arte dada uma melhor representação de palavra. Esta tese também almeja avaliar as vantagens do bleaching em relação ao modelo tradicional através do arcabouço teórico da teoria VC. As dimensões VC destes foram calculadas, atestando-se que um classificador n-upla, seja WiSARD ou com bleaching, que possua N memórias endereçadas por n-uplas binárias tem uma dimensão VC de exatamente N (2n − 1) + 1. Um paralelo foi então estabelecido entre ambos os modelos, onde deduziu-se que a técnica de bleaching é uma melhoria ao método n-upla que não causa prejuízos à sua capacidade de aprendizado

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Neotropical freshwater fisheries : A dataset of occurrence and abundance of freshwater fishes in the Neotropics

The Neotropical region hosts 4225 freshwater fish species, ranking first among the world's most diverse regions for freshwater fishes. Our NEOTROPICAL FRESHWATER FISHES data set is the first to produce a large-scale Neotropical freshwater fish inventory, covering the entire Neotropical region from Mexico and the Caribbean in the north to the southern limits in Argentina, Paraguay, Chile, and Uruguay. We compiled 185,787 distribution records, with unique georeferenced coordinates, for the 4225 species, represented by occurrence and abundance data. The number of species for the most numerous orders are as follows: Characiformes (1289), Siluriformes (1384), Cichliformes (354), Cyprinodontiformes (245), and Gymnotiformes (135). The most recorded species was the characid Astyanax fasciatus (4696 records). We registered 116,802 distribution records for native species, compared to 1802 distribution records for nonnative species. The main aim of the NEOTROPICAL FRESHWATER FISHES data set was to make these occurrence and abundance data accessible for international researchers to develop ecological and macroecological studies, from local to regional scales, with focal fish species, families, or orders. We anticipate that the NEOTROPICAL FRESHWATER FISHES data set will be valuable for studies on a wide range of ecological processes, such as trophic cascades, fishery pressure, the effects of habitat loss and fragmentation, and the impacts of species invasion and climate change. There are no copyright restrictions on the data, and please cite this data paper when using the data in publications

Search for the rare hadronic decay Bs0→ppˉB_s^0\to p \bar{p}

A search for the rare hadronic decay Bs0→pp¯ is performed using proton-proton collision data recorded by the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 6  fb-1. No evidence of the decay is found and an upper limit on its branching fraction is set at B(Bs0→pp¯)&lt;4.4(5.1)×10-9 at 90% (95%) confidence level; this is currently the world’s best upper limit. The decay mode B0→pp¯ is measured with very large significance, confirming the first observation by the LHCb experiment in 2017. The branching fraction is determined to be B(B0→pp¯)=(1.27±0.15±0.05±0.04)×10-8, where the first uncertainty is statistical, the second is systematic and the third is due to the external branching fraction of the normalization channel B0→K+π-. The combination of the two LHCb measurements of the B0→pp¯ branching fraction yields B(B0→pp¯)=(1.27±0.13±0.05±0.03)×10-8.A search for the rare hadronic decay $B_s^0\to p \bar{p}$ is performed using proton-proton collision data recorded by the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 6 fb$^{-1}$. No evidence of the decay is found and an upper limit on its branching fraction is set at ${\cal B}(B_s^0\to p \bar{p}) < 4.4~(5.1) \times 10^{-9}$ at 90% (95%) confidence level; this is currently the world's best upper limit. The decay mode $B^0\to p \bar{p}$ is measured with very large significance, confirming the first observation by the LHCb experiment in 2017. The branching fraction is determined to be ${\cal B}(B^0\to p \bar{p}) = \rm (1.27 \pm 0.15 \pm 0.05 \pm 0.04) \times 10^{-8}$, where the first uncertainty is statistical, the second is systematic and the third is due to the external branching fraction of the normalization channel $B^0\to K^+\pi^-$. The combination of the two LHCb measurements of the $B^0\to p \bar{p}$ branching fraction yields ${\cal B}(B^0\to p \bar{p}) = \rm (1.27 \pm 0.13 \pm 0.05 \pm 0.03) \times 10^{-8}$

Measurement of the prompt D0D^0 nuclear modification factor in ppPb collisions at sNN=8.16\sqrt{s_\mathrm{NN}} = 8.16 TeV

International audienceThe production of prompt $D^0$ mesons in proton-lead collisions in the forward and backward configurations at a center-of-mass energy per nucleon pair of $\sqrt{s_\mathrm{NN}} = 8.16~\mathrm{TeV}$ is measured by the LHCb experiment. The nuclear modification factor of prompt $D^0$ mesons is determined as a function of the transverse momentum $p_\mathrm{T}$, and rapidity in the nucleon-nucleon center-of-mass frame $y^*$. In the forward rapidity region, significantly suppressed production with respect to $pp$ collisions is measured, which provides significant constraints of nuclear parton distributions and hadron production down to the very low Bjorken-$x$ region of $\sim 10^{-5}$. In the backward rapidity region, a suppression with a significance of 2.0 - 3.8 standard deviations compared to nPDF expectations is found in the kinematic region of $p_\mathrm{T}>6~\mathrm{GeV}/c$ and $-3.25<y^*<-2.5$, corresponding to $x\sim 0.01$

Helium identification with LHCb

International audienceThe identification of helium nuclei at LHCb is achieved using a method based on measurements of ionisation losses in the silicon sensors and timing measurements in the Outer Tracker drift tubes. The background from photon conversions is reduced using the RICH detectors and an isolation requirement. The method is developed using $pp$ collision data at $\sqrt{s}=13\,{\rm TeV}$ recorded by the LHCb experiment in the years 2016 to 2018, corresponding to an integrated luminosity of $5.5\,{\rm fb}^{-1}$. A total of around $10^5$ helium and antihelium candidates are identified with negligible background contamination. The helium identification efficiency is estimated to be approximately $50\%$ with a corresponding background rejection rate of up to $\mathcal O(10^{12})$. These results demonstrate the feasibility of a rich programme of measurements of QCD and astrophysics interest involving light nuclei