ANDES, the high resolution spectrograph for the ELT: science goals, project overview, and future developments
Alessandro Marconi
Artur R. Abreu
Vardan Adibekyan
Valentina Alberti
Simon Albrecht
Jailson Alcaniz
Matteo Aliverti
Carlos Allende Prieto
Julian Alvarado-Gomez
Catarina Alves
Pedro J. Amado
Manuel Amate
Michael Andersen
Simone Antoniucci
E. Artigau
Christophe Bailet
Clark E. Baker
Veronica Baldini
Andrea Balestra
S.A. Barnes … (see 271 more)
Frédérique Baron
Susana Barros
Svend-Marian Bauer
Mathilde Beaulieu
Olga Bellido-Tirado
Björn Benneke
Thomas Bensby
Edwin Bergin
P. Berio
Katia Biazzo
Laurent Bigot
Arjan Bik
Jayne L. Birkby
Nicolas Blind
Olivier Boebion
Isabelle Boisse
Emeline Bolmont
J. S. Bolton
Marco Bonaglia
Xavier Bonfils
Lea Bonhomme
Francesco Borsa
Jean-Claude Bouret
Alexis Brandeker
Wolfgang Brandner
Christopher H. Broeg
Matteo Brogi
Denis Brousseau
Anna Brucalassi
Joar G. Brynnel
Lars A. Buchhave
David F. Buscher
Lorenzo Cabona
A. Cabral
Alexandre Cabral
Giorgio Calderone
Rocío Calvo-Ortega
Faustine Cantalloube
Bruno L. Canto Martins
Luca Carbonaro
Yan Caujolle
Gaël Chauvin
Bruno Chazelas
Anne-Laure L. Cheffot
Yuk Shan Cheng
Andrea Chiavassa
Lise B. Christensen
Roberto Cirami
Michele Cirasuolo
Neil J. Cook
Ryan Cooke
Igor Coretti
Stefano Covino
Nicolas B. Cowan
Giovanni Cresci
Stefano Cristiani
Vanderlei Cunha Parro
Guido Cupani
Valentina D'Odorico
Kamalesh Dadi
Izan C. de Castro Leão
Annalisa De Cia
Jose R. De Medeiros
Florian Debras
Michael Debus
Alain Delorme
Olivier Demangeon
Frederic Derie
M. Dessauges-Zavadsky
Paolo Di Marcantonio
Simona Di Stefano
Frank Dionies
Armando Domiciano de Souza
René Doyon
Jennifer S. Dunn
Sébastien E. Egner
David Ehrenreich
Joao P. Faria
Debora Ferruzzi
Chiara Feruglio
Martin Fisher
Adriano Fontana
B S. Frank
C. Fuesslein
M. Fumagalli
Thierry Fusco
Johan P. U. Fynbo
O. Gabella
W. Gaessler
E. Gallo
X. Gao
L. Genolet
M. Genoni
P. Giacobbe
E. Giro
R. S. Gonçalves
O. A. Gonzalez
J. I. González-Hernández
C. Gouvret
F. Gracia Témich
M. G. Haehnelt
C. Haniff
A. Hatzes
R. Helled
H. J. Hoeijmakers
I. Hughes
Philipp Huke
Y. Ivanisenko
A. S. Järvinen
S. P. Järvinen
A. Kaminski
J. Kern
J. Knoche
A. Kordt
H. Korhonen
A. Korn
D. Kouach
G. Kowzan
L. Kreidberg
M. Landoni
A. A. Lanotte
A. Lavail
B. Lavie
D. Lee
M. Lehmitz
Jian Li
Wei Li
J. Liske
C. Lovis
S. Lucatello
D. Lunney
M. J. MacIntosh
N. Madhusudhan
L. Magrini
R. Maiolino
J. Maldonado
L. Malo
A. W. S. Man
T. Marquart
C. M. J. Marques
E. L. Marques
P. Martinez
A. M. Martins
C. J. A. P. Martins
J. H. C. Martins
P. Maslowski
C. Mason
E. Mason
R. A. McCracken
M. A. F. Melo e Sousa
P. Mergo
G. Micela
D. Milaković
P. Mollière
M. A. Monteiro
D. Montgomery
C. Mordasini
J. Morin
A. Mucciarelli
M. T. Murphy
M. N'Diaye
N. Nardetto
B. Neichel
N. Neri
A. T. Niedzielski
E. Niemczura
B. Nisini
L. Nortmann
P. Noterdaeme
N. J. Nunes
L. Oggioni
F. Olchewsky
E. Oliva
H. Önel
L. Origlia
G. Östlin
N. N.-Q. Ouellette
Enric Pallé
P. Papaderos
G. Pariani
L. Pasquini
J. Peñate Castro
F. Pepe
C. Peroux
L. Perreault Levasseur
Sandrine Perruchot
P. Petit
Oliver Pfuhl
L. Pino
Javier Piqueras
N. Piskunov
A. Pollo
K. Poppenhaeger
M. Porru
J. Puschnig
A. Quirrenbach
Emily Rauscher
R. Rebolo
E. M. A. Redaelli
S. Reffert
D. T. Reid
A. Reiners
P. Richter
M. Riva
S. Rivoire
C. Rodríguez-López
I. U. Roederer
D. Romano
M. Roth
S. Rousseau
J. Rowe
A. Saccardi
S. Salvadori
N. Sanna
N. C. Santos
P. Santos Diaz
Jorge Sanz-Forcada
M. Sarajlic
J.-F. Sauvage
D. Savio
A. Scaudo
S. Schäfer
R. P. Schiavon
T. M. Schmidt
C. Selmi
R. Simoes
A. Simonnin
S. Sivanandam
M. Sordet
R. Sordo
F. Sortino
D. Sosnowska
S. G. Sousa
A. Spang
R. Spiga
E. Stempels
J. R. Y. Stevenson
Klaus G. Strassmeier
A. Suárez Mascareño
A. Sulich
X. Sun
N. R. Tanvir
F. Tenegi-Sanginés
S. Thibault
S. J. Thompson
P. Tisserand
A. Tozzi
M. Turbet
J.-P. Véran
Julien Veran
P. Vallée
I. Vanni
R. Varas
A. Vega-Moreno
K. A. Venn
A. Verma
J. Vernet
M. Viel
G. Wade
C. Waring
M. Weber
J. Weder
B. Wehbé
J. Weingrill
M. Woche
M. Xompero
E. Zackrisson
A. Zanutta
M. R. Zapatero Osorio
M. Zechmeister
J. Zimara
ANDES, the high resolution spectrograph for the ELT: science goals, project overview, and future developments
Alessandro Marconi
Artur R. Abreu
Vardan Adibekyan
Valentina Alberti
Simon Albrecht
Jailson Alcaniz
Matteo Aliverti
Carlos Allende Prieto
Julian Alvarado-Gomez
Catarina Alves
Pedro J. Amado
Manuel Amate
Michael Andersen
Simone Antoniucci
E. Artigau
Christophe Bailet
Clark E. Baker
Veronica Baldini
Andrea Balestra
S.A. Barnes … (see 269 more)
Frédérique Baron
Susana Barros
Svend-Marian Bauer
Mathilde Beaulieu
Olga Bellido-Tirado
Björn Benneke
Thomas Bensby
Edwin Bergin
P. Berio
Katia Biazzo
Laurent Bigot
Arjan Bik
Jayne L. Birkby
Nicolas Blind
Olivier Boebion
Isabelle Boisse
Emeline Bolmont
J. S. Bolton
Marco Bonaglia
Xavier Bonfils
Lea Bonhomme
Francesco Borsa
Jean-Claude Bouret
Alexis Brandeker
Wolfgang Brandner
Christopher H. Broeg
Matteo Brogi
Denis Brousseau
Anna Brucalassi
Joar G. Brynnel
Lars A. Buchhave
David F. Buscher
Lorenzo Cabona
A. Cabral
Giorgio Calderone
Rocío Calvo-Ortega
Faustine Cantalloube
Bruno L. Canto Martins
Luca Carbonaro
Yan Caujolle
Gaël Chauvin
Bruno Chazelas
Anne-Laure L. Cheffot
Yuk Shan Cheng
Andrea Chiavassa
Lise B. Christensen
Roberto Cirami
Michele Cirasuolo
Neil J. Cook
Ryan Cooke
Igor Coretti
Stefano Covino
Nicolas B. Cowan
Giovanni Cresci
Stefano Cristiani
Vanderlei Cunha Parro
Guido Cupani
Valentina D'Odorico
Kamalesh Dadi
Izan C. de Castro Leão
Annalisa De Cia
Jose R. De Medeiros
Florian Debras
Michael Debus
Alain Delorme
Olivier Demangeon
Frederic Derie
M. Dessauges-Zavadsky
Paolo Di Marcantonio
Simona Di Stefano
Frank Dionies
Armando Domiciano de Souza
René Doyon
Jennifer S. Dunn
Sébastien E. Egner
David Ehrenreich
Joao P. Faria
Debora Ferruzzi
Chiara Feruglio
Martin Fisher
Adriano Fontana
B S. Frank
C. Fuesslein
M. Fumagalli
Thierry Fusco
Johan P. U. Fynbo
O. Gabella
W. Gaessler
E. Gallo
X. Gao
L. Genolet
M. Genoni
P. Giacobbe
E. Giro
R. S. Gonçalves
O. A. Gonzalez
J. I. González-Hernández
C. Gouvret
F. Gracia Témich
M. G. Haehnelt
C. Haniff
A. Hatzes
R. Helled
H. J. Hoeijmakers
I. Hughes
Philipp Huke
Y. Ivanisenko
A. S. Järvinen
S. P. Järvinen
A. Kaminski
J. Kern
J. Knoche
A. Kordt
H. Korhonen
A. Korn
D. Kouach
G. Kowzan
L. Kreidberg
M. Landoni
A. A. Lanotte
A. Lavail
B. Lavie
D. Lee
M. Lehmitz
J. Li
W. Li
J. Liske
C. Lovis
S. Lucatello
D. Lunney
M. J. MacIntosh
N. Madhusudhan
L. Magrini
R. Maiolino
J. Maldonado
L. Malo
A. W. S. Man
T. Marquart
C. M. J. Marques
E. L. Marques
P. Martinez
A. M. Martins
C. J. A. P. Martins
J. H. C. Martins
P. Maslowski
C. Mason
E. Mason
R. A. McCracken
M. A. F. Melo e Sousa
P. Mergo
G. Micela
D. Milaković
P. Mollière
M. A. Monteiro
D. Montgomery
C. Mordasini
J. Morin
A. Mucciarelli
M. T. Murphy
M. N'Diaye
N. Nardetto
B. Neichel
N. Neri
A. T. Niedzielski
E. Niemczura
B. Nisini
L. Nortmann
P. Noterdaeme
N. J. Nunes
L. Oggioni
F. Olchewsky
E. Oliva
H. Önel
L. Origlia
G. Östlin
N. N.-Q. Ouellette
Enric Pallé
P. Papaderos
G. Pariani
L. Pasquini
J. Peñate Castro
F. Pepe
C. Peroux
L. Perreault Levasseur
Sandrine Perruchot
P. Petit
Oliver Pfuhl
L. Pino
Javier Piqueras
N. Piskunov
A. Pollo
K. Poppenhaeger
M. Porru
J. Puschnig
A. Quirrenbach
Emily Rauscher
R. Rebolo
E. M. A. Redaelli
S. Reffert
D. T. Reid
A. Reiners
P. Richter
M. Riva
S. Rivoire
C. Rodríguez-López
I. U. Roederer
D. Romano
M. Roth
S. Rousseau
J. Rowe
A. Saccardi
S. Salvadori
N. Sanna
N. C. Santos
P. Santos Diaz
Jorge Sanz-Forcada
M. Sarajlic
J.-F. Sauvage
D. Savio
A. Scaudo
S. Schäfer
R. P. Schiavon
T. M. Schmidt
C. Selmi
R. Simoes
A. Simonnin
S. Sivanandam
M. Sordet
R. Sordo
F. Sortino
D. Sosnowska
S. G. Sousa
A. Spang
R. Spiga
E. Stempels
J. R. Y. Stevenson
Klaus G. Strassmeier
A. Suárez Mascareño
A. Sulich
X. Sun
N. R. Tanvir
F. Tenegi-Sanginés
S. Thibault
S. J. Thompson
P. Tisserand
A. Tozzi
M. Turbet
Julien Veran
P. Vallée
I. Vanni
R. Varas
A. Vega-Moreno
K. A. Venn
A. Verma
J. Vernet
M. Viel
G. Wade
C. Waring
M. Weber
J. Weder
B. Wehbé
J. Weingrill
M. Woche
M. Xompero
E. Zackrisson
A. Zanutta
M. R. Zapatero Osorio
M. Zechmeister
J. Zimara
The first generation of ELT instruments includes an optical-infrared high-resolution spectrograph, indicated as ELT-HIRES and recently chris… (see more)tened ANDES (ArmazoNes high Dispersion Echelle Spectrograph). ANDES consists of three fibre-fed spectrographs ([U]BV, RIZ, YJH) providing a spectral resolution of
Distinct Social Behavior and Inter-Brain Connectivity in Dyads with autistic individuals
Quentin Moreau
Florence Brun
Anaël Ayrolles
Jacqueline Nadel
Myelin basic protein mRNA levels affect myelin sheath dimensions, architecture, plasticity, and density of resident glial cells
Hooman Bagheri
Hana Friedman
Amanda Hadwen
Celia Jarweh
Ellis Cooper
Lawrence Oprea
Claire Guerrier
Anmar Khadra
Armand Collin
Amanda Young
Gerardo Mendez Victoriano
Matthew Swire
Andrew Jarjour
Marie E. Bechler
Rachel S. Pryce
Pierre Chaurand
Lise Cougnaud
Dajana Vuckovic
Elliott Wilion … (see 11 more)
Owen Greene
Akiko Nishiyama
Anouk Benmamar‐Badel
Trevor Owens
Vladimir Grouza
Marius Tuznik
Hanwen Liu
David A. Rudko
Jinyi Zhang
Katherine A. Siminovitch
Alan C. Peterson
Abstract Myelin Basic Protein (MBP) is essential for both elaboration and maintenance of CNS myelin, and its reduced accumulation results in… (see more) hypomyelination. How different Mbp mRNA levels affect myelin dimensions across the lifespan and how resident glial cells may respond to such changes are unknown. Here, to investigate these questions, we used enhancer‐edited mouse lines that accumulate Mbp mRNA levels ranging from 8% to 160% of wild type. In young mice, reduced Mbp mRNA levels resulted in corresponding decreases in Mbp protein accumulation and myelin sheath thickness, confirming the previously demonstrated rate‐limiting role of Mbp transcription in the control of initial myelin synthesis. However, despite maintaining lower line specific Mbp mRNA levels into old age, both MBP protein levels and myelin thickness improved or fully normalized at rates defined by the relative Mbp mRNA level. Sheath length, in contrast, was affected only when mRNA levels were very low, demonstrating that sheath thickness and length are not equally coupled to Mbp mRNA level. Striking abnormalities in sheath structure also emerged with reduced mRNA levels. Unexpectedly, an increase in the density of all glial cell types arose in response to reduced Mbp mRNA levels. This investigation extends understanding of the role MBP plays in myelin sheath elaboration, architecture, and plasticity across the mouse lifespan and illuminates a novel axis of glial cell crosstalk.
Myelin basic protein mRNA levels affect myelin sheath dimensions, architecture, plasticity, and density of resident glial cells
Hooman Bagheri
Hana Friedman
Amanda Hadwen
Celia Jarweh
Ellis Cooper
Lawrence Oprea
Claire Guerrier
Anmar Khadra
Armand Collin
Amanda Young
Gerardo Mendez Victoriano
Matthew Swire
Andrew Jarjour
Marie E. Bechler
Rachel S. Pryce
Pierre Chaurand
Lise Cougnaud
Dajana Vuckovic
Elliott Wilion … (see 11 more)
Owen Greene
Akiko Nishiyama
Anouk Benmamar‐Badel
Trevor Owens
Vladimir Grouza
Marius Tuznik
Hanwen Liu
David A. Rudko
Jinyi Zhang
Katherine A. Siminovitch
Alan C. Peterson
Abstract Myelin Basic Protein (MBP) is essential for both elaboration and maintenance of CNS myelin, and its reduced accumulation results in… (see more) hypomyelination. How different Mbp mRNA levels affect myelin dimensions across the lifespan and how resident glial cells may respond to such changes are unknown. Here, to investigate these questions, we used enhancer‐edited mouse lines that accumulate Mbp mRNA levels ranging from 8% to 160% of wild type. In young mice, reduced Mbp mRNA levels resulted in corresponding decreases in Mbp protein accumulation and myelin sheath thickness, confirming the previously demonstrated rate‐limiting role of Mbp transcription in the control of initial myelin synthesis. However, despite maintaining lower line specific Mbp mRNA levels into old age, both MBP protein levels and myelin thickness improved or fully normalized at rates defined by the relative Mbp mRNA level. Sheath length, in contrast, was affected only when mRNA levels were very low, demonstrating that sheath thickness and length are not equally coupled to Mbp mRNA level. Striking abnormalities in sheath structure also emerged with reduced mRNA levels. Unexpectedly, an increase in the density of all glial cell types arose in response to reduced Mbp mRNA levels. This investigation extends understanding of the role MBP plays in myelin sheath elaboration, architecture, and plasticity across the mouse lifespan and illuminates a novel axis of glial cell crosstalk.
Myelin basic protein mRNA levels affect myelin sheath dimensions, architecture, plasticity, and density of resident glial cells
Hooman Bagheri
Hana Friedman
Amanda Hadwen
Celia Jarweh
Ellis Cooper
Lawrence Oprea
Claire Guerrier
Anmar Khadra
Armand Collin
Amanda Young
Gerardo Mendez Victoriano
Matthew Swire
Andrew Jarjour
Marie E. Bechler
Rachel S. Pryce
Pierre Chaurand
Lise Cougnaud
Dajana Vuckovic
Elliott Wilion … (see 11 more)
Owen Greene
Akiko Nishiyama
Anouk Benmamar‐Badel
Trevor Owens
Vladimir Grouza
Marius Tuznik
Hanwen Liu
David A. Rudko
Jinyi Zhang
Katherine A. Siminovitch
Alan C. Peterson
The Madness of Multiple Entries in March Madness
Jeff Decary
David Bergman
Carlos Henrique Cardonha
Jason Imbrogno
Andrea Lodi
This paper explores multi-entry strategies for betting pools related to single-elimination tournaments. In such betting pools, participants … (see more)select winners of games, and their respective score is a weighted sum of the number of correct selections. Most betting pools have a top-heavy payoff structure, so the paper focuses on strategies that maximize the expected score of the best-performing entry. There is no known closed-formula expression for the estimation of this metric, so the paper investigates the challenges associated with the estimation and the optimization of multi-entry solutions. We present an exact dynamic programming approach for calculating the maximum expected score of any given fixed solution, which is exponential in the number of entries. We explore the structural properties of the problem to develop several solution techniques. In particular, by extracting insights from the solutions produced by one of our algorithms, we design a simple yet effective problem-specific heuristic that was the best-performing technique in our experiments, which were based on real-world data extracted from recent March Madness tournaments. In particular, our results show that the best 100-entry solution identified by our heuristic had a 2.2% likelihood of winning a
Tree semantic segmentation from aerial image time series
Venkatesh Ramesh
Arthur Ouaknine
Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning
Ziyan Luo
Solving Constrained Horn Clauses (CHCs) is a fundamental challenge behind a wide range of verification and analysis tasks. Data-driven appro… (see more)aches show great promise in improving CHC solving without the painstaking manual effort of creating and tuning various heuristics. However, a large performance gap exists between data-driven CHC solvers and symbolic reasoning-based solvers. In this work, we develop a simple but effective framework,"Chronosymbolic Learning", which unifies symbolic information and numerical data points to solve a CHC system efficiently. We also present a simple instance of Chronosymbolic Learning with a data-driven learner and a BMC-styled reasoner. Despite its great simplicity, experimental results show the efficacy and robustness of our tool. It outperforms state-of-the-art CHC solvers on a dataset consisting of 288 benchmarks, including many instances with non-linear integer arithmetics.
Evaluating the transferability potential of deep learning models for climate downscaling
Ayush Prasad
Paula Harder
Qidong Yang
Prasanna Sattegeri
D. Szwarcman
Campbell Watson
Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding … (see more)and adapting to climate change at regional and local scales. Deep learning approaches have proven useful in tackling this problem. However, existing studies usually focus on training models for one specific task, location and variable, which are therefore limited in their generalizability and transferability. In this paper, we evaluate the efficacy of training deep learning downscaling models on multiple diverse climate datasets to learn more robust and transferable representations. We evaluate the effectiveness of architectures zero-shot transferability using CNNs, Fourier Neural Operators (FNOs), and vision Transformers (ViTs). We assess the spatial, variable, and product transferability of downscaling models experimentally, to understand the generalizability of these different architecture types.
Evaluating the transferability potential of deep learning models for climate downscaling
Ayush Prasad
Paula Harder
Qidong Yang
Prasanna Sattegeri
Daniela Szwarcman
Campbell Watson
Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding … (see more)and adapting to climate change at regional and local scales. Deep learning approaches have proven useful in tackling this problem. However, existing studies usually focus on training models for one specific task, location and variable, which are therefore limited in their generalizability and transferability. In this paper, we evaluate the efficacy of training deep learning downscaling models on multiple diverse climate datasets to learn more robust and transferable representations. We evaluate the effectiveness of architectures zero-shot transferability using CNNs, Fourier Neural Operators (FNOs), and vision Transformers (ViTs). We assess the spatial, variable, and product transferability of downscaling models experimentally, to understand the generalizability of these different architecture types.
Spectra: A Comprehensive Study of Ternary, Quantized, and FP16 Language Models
Ayush Kaushal
Tejas Pandey
Tejas Vaidhya
Aaryan Bhagat