An updated maximum likelihood approach to open cluster distance determination

An improved method for estimating distances to open clusters is presented and applied to Hipparcos data for the Pleiades and the Hyades. The method is applied in the context of the historic Pleiades distance problem, with a discussion of previous criticisms of Hipparcos parallaxes. This is followed by an outlook for Gaia, where the improved method could be especially useful. Based on maximum likelihood estimation, the method combines parallax, position, apparent magnitude, colour, proper motion, and radial velocity information to estimate the parameters describing an open cluster precisely and without bias. We find the distance to the Pleiades to be $120.3 \pm 1.5$ pc, in accordance with previously published work using the same dataset. We find that error correlations cannot be responsible for the still present discrepancy between Hipparcos and photometric methods. Additionally, the three-dimensional space velocity and physical structure of Pleiades is parametrised, where we find strong evidence of mass segregation. The distance to the Hyades is found to be $46.35\pm 0.35$ pc, also in accordance with previous results. Through the use of simulations, we confirm that the method is unbiased, so will be useful for accurate open cluster parameter estimation with Gaia at distances up to several thousand parsec.Comment: 14 pages, 8 figures, 6 tables, 3 appendices. Accepted in A&

Pelaksanaan Tugas Kecamatan Guna Memberdayakan Pemerintah sebagai Pusat Pelayanan Masyarakat

Kurangnya pelayanan kepada masyarakat diperkirakan karena kurangnya pemberdayaan pemerintah Kecamatan Jatinangor sebagai salah satu Organisasi Perangkat Daerah terdepan yang melaksanakan tugas umum pemerintahan bidang koordinasi, pembinaan dan bidang pelayanan kepada masyarakat. Desain penelitian yang digunakan adalah desain penelitian deskriptif pendekatan kualitatif. Tujuan penelitian ini untuk menggambarkan pelaksanaan tugas umum Pemerintah Kecamatan Jatinangor bidang koordinasi, pembinaan dan bidang pelayanan kepada masyarakat dan menggambarkan cara memberdayakan Pemerintah Kecamatan Jatinangor sebagai pusat pelayanan masyarakat. Hasil penelitian menunjukkan bahwa pelaksanaan tugas umum pemerintahan Pemerintah Kecamatan Jatinangor bidang koordinasi, pembinaan dan bidang pelayanan kepada masyarakat belum optimal sesuai harapan, karena kurang optimalnya implementasi kewenangan kecamatan dan kurang tersedianya unsur 3P (Personalia, Pembiayaan dan Prasarana dan Sarana)

Deep Expander Networks: Efficient Deep Networks from Graph Theory

Efficient CNN designs like ResNets and DenseNet were proposed to improve accuracy vs efficiency trade-offs. They essentially increased the connectivity, allowing efficient information flow across layers. Inspired by these techniques, we propose to model connections between filters of a CNN using graphs which are simultaneously sparse and well connected. Sparsity results in efficiency while well connectedness can preserve the expressive power of the CNNs. We use a well-studied class of graphs from theoretical computer science that satisfies these properties known as Expander graphs. Expander graphs are used to model connections between filters in CNNs to design networks called X-Nets. We present two guarantees on the connectivity of X-Nets: Each node influences every node in a layer in logarithmic steps, and the number of paths between two sets of nodes is proportional to the product of their sizes. We also propose efficient training and inference algorithms, making it possible to train deeper and wider X-Nets effectively. Expander based models give a 4% improvement in accuracy on MobileNet over grouped convolutions, a popular technique, which has the same sparsity but worse connectivity. X-Nets give better performance trade-offs than the original ResNet and DenseNet-BC architectures. We achieve model sizes comparable to state-of-the-art pruning techniques using our simple architecture design, without any pruning. We hope that this work motivates other approaches to utilize results from graph theory to develop efficient network architectures.Comment: ECCV'1

Domain-adaptive deep network compression

Deep Neural Networks trained on large datasets can be easily transferred to new domains with far fewer labeled examples by a process called fine-tuning. This has the advantage that representations learned in the large source domain can be exploited on smaller target domains. However, networks designed to be optimal for the source task are often prohibitively large for the target task. In this work we address the compression of networks after domain transfer. We focus on compression algorithms based on low-rank matrix decomposition. Existing methods base compression solely on learned network weights and ignore the statistics of network activations. We show that domain transfer leads to large shifts in network activations and that it is desirable to take this into account when compressing. We demonstrate that considering activation statistics when compressing weights leads to a rank-constrained regression problem with a closed-form solution. Because our method takes into account the target domain, it can more optimally remove the redundancy in the weights. Experiments show that our Domain Adaptive Low Rank (DALR) method significantly outperforms existing low-rank compression techniques. With our approach, the fc6 layer of VGG19 can be compressed more than 4x more than using truncated SVD alone -- with only a minor or no loss in accuracy. When applied to domain-transferred networks it allows for compression down to only 5-20% of the original number of parameters with only a minor drop in performance.Comment: Accepted at ICCV 201

Tectónica activa de la Falla de Alhama de Murcia, Cordillera Bética, España

We present an overview of the knowledge of the structure and the seismic behavior of the Alhama de Murcia Fault (AMF). We utilize a fault traces map created from a LIDAR DEM combined with the geodynamic setting, the analysis of the morphology, the distribution of seismicity, the geological information from E 1:50000 geological maps and the available paleoseismic data to describe the recent activity of the AMF. We discuss the importance of uncertainties regarding the structure and kinematics of the AMF applied to the interpretation and spatial correlation of the paleoseismic data. In particular, we discuss the nature of the faults dipping to the SE (antithetic to the main faults of the AMF) in several segments that have been studied in the previous paleoseismic works. A special chapter is dedicated to the analysis of the tectonic source of the Lorca 2011 earthquake that took place in between two large segments of the fault.En este estudio se presenta una revisión del conocimiento que hasta la actualidad se tiene de la estructura y comportamiento sismogenético de la Falla de Alhama de Murcia (AMF). Se utiliza un nuevo mapa de la traza de la AMF realizado a partir de un modelo digital del terreno de alta resolución a partir de datos LIDAR, combinado con el análisis del marco geodinámico, la geomorforlogía, la distribución espaciotemporal de la sismicidad, la información geológica de trabajos previos y los datos paleosísmicos existentes, para describir la actividad reciente de la AMF. Se discute la importancia de las incertidumbres que se mantienen en relación con la estructura y la cinemática de la AMF para la correcta interpretación y correlación espacio-temporal de los datos paleosísmicos obtenidos hasta ahora. En particular, se discute la naturaleza de las fallas con buzamiento SE en superficie (antitéticas con las fallas principales de la AMF que bordean las sierras) en varios segmentos que han sido estudiados en análisis paleosismológicos previos. Se dedica un capítulo especial al análisis de la fuente geológica del terremoto de Lorca de 2011 que tuvo lugar en la zona de intersegmento que separa dos de los segmentos de mayor longitud de la AMF

Application of a Neural Network classifier for the generation of clean Small Magellanic Cloud stellar samples

Context. Previous attempts to separate Small Magellanic Cloud (SMC) stars from the Milky Way (MW) foreground stars are based only on the proper motions of the stars. Aims. In this paper we develop a statistical classification technique to effectively separate the SMC stars from the MW stars using a wider set of Gaia data. We aim to reduce the possible contamination from MW stars compared to previous strategies. Methods. The new strategy is based on neural network classifier, applied to the bulk of the Gaia DR3 data. We produce three samples of stars flagged as SMC members, with varying levels of completeness and purity, obtained by application of this classifier. Using different test samples we validate these classification results and we compare them with the results of the selection technique employed in the Gaia Collaboration papers, which was based solely on the proper motions. Results. The contamination of MW in each of the three SMC samples is estimated to be in the 10-40%; the "best case" in this range is obtained for bright stars (G > 16), which belong to the Vlos sub-samples, and the "worst case" for the full SMC sample determined by using very stringent criteria based on StarHorse distances. A further check based on the comparison with a nearby area with uniform sky density indicates that the global contamination in our samples is probably close to the low end of the range, around 10%. Conclusions. We provide three selections of SMC star samples with different degrees of purity and completeness, for which we estimate a low contamination level and have successfully validated using SMC RR Lyrae, SMC Cepheids and SMC/MW StarHorse samples.Comment: arXiv admin note: substantial text overlap with arXiv:2210.0172

Genotypic Characterization of Non-O157 Shiga Toxin–Producing Escherichia coli in Beef Abattoirs of Argentina

The non-O157 Shiga toxin-producing Escherichia coli (STEC) contamination in carcasses and feces of 811 bovines in nine beef abattoirs from Argentina was analyzed during a period of 17 months. The feces of 181 (22.3%) bovines were positive for non-O157 STEC, while 73 (9.0%) of the carcasses showed non-O157 STEC contamination. Non-O157 STEC strains isolated from feces (227) and carcasses (80) were characterized. The main serotypes identified were O178:H19, O8:H19, O130:H11, and O113:H21, all of which have produced sporadic cases of hemolytic-uremic syndrome in Argentina and worldwide. Twenty-two (7.2%) strains carried a fully virulent stx/eae/ehxA genotype. Among them, strains of serotypes O103:[H2], O145:NM, and O111:NM represented 4.8% of the isolates. XbaI pulsed-field gel electrophoresis pattern analysis showed 234 different patterns, with 76 strains grouped in 30 clusters. Nine of the clusters grouped strains isolated from feces and from carcasses of the same or different bovines in a lot, while three clusters were comprised of strains distributed in more than one abattoir. Patterns AREXSX01.0157, AREXBX01.0015, and AREXPX01.0013 were identified as 100% compatible with the patterns of one strain isolated from a hemolytic-uremic syndrome case and two strains previously isolated from beef medallions, included in the Argentine PulseNet Database. In this survey, 4.8% (39 of 811) of the bovine carcasses appeared to be contaminated with non- O157 STEC strains potentially capable of producing sporadic human disease, and a lower proportion (0.25%) with strains able to produce outbreaks of severe disease.Fil: Masana, Marcelo. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Agroindustria. Instituto de Tecnología de Alimentos; ArgentinaFil: D´Astek, B. A.. Dirección Nacional de Instituto de Investigación. Administración Nacional de Laboratorio e Instituto de Salud “Dr. C. G. Malbrán”; ArgentinaFil: Palladino, Pablo Martín. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Agroindustria. Instituto de Tecnología de Alimentos; ArgentinaFil: Galli, Lucía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; Argentina. Dirección Nacional de Institutos de Investigación. Administración Nacional de Laboratorios e Institutos de Salud. Instituto Nacional de Enfermedades Infecciosas; ArgentinaFil: del Castillo, Lourdes Leonor. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Agroindustria. Instituto de Tecnología de Alimentos; ArgentinaFil: Carbonari, Claudia Carolina. Dirección Nacional de Institutos de Investigación. Administración Nacional de Laboratorios e Institutos de Salud. Instituto Nacional de Enfermedades Infecciosas; ArgentinaFil: Leotta, Gerardo Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; Argentina. Dirección Nacional de Institutos de Investigación. Administración Nacional de Laboratorios e Institutos de Salud. Instituto Nacional de Enfermedades Infecciosas; ArgentinaFil: Vilacoba, Elisabet. Dirección Nacional de Institutos de Investigación. Administración Nacional de Laboratorios e Institutos de Salud. Instituto Nacional de Enfermedades Infecciosas; ArgentinaFil: Irino, K.. Instituto Adolfo Lutz. Seção de Bacteriologia; BrasilFil: Rivas, M.. Dirección Nacional de Institutos de Investigación. Administración Nacional de Laboratorios e Institutos de Salud. Instituto Nacional de Enfermedades Infecciosas; Argentin