Neural-network selection of high-redshift radio quasars, and the luminosity function at z~4

We obtain a sample of 87 radio-loud QSOs in the redshift range 3.6<z<4.4 by cross-correlating sources in the FIRST radio survey S{1.4GHz} > 1 mJy with star-like objects having r <20.2 in SDSS Data Release 7. Of these 87 QSOs, 80 are spectroscopically classified in previous work (mainly SDSS), and form the training set for a search for additional such sources. We apply our selection to 2,916 FIRST-DR7 pairs and find 15 likely candidates. Seven of these are confirmed as high-redshift quasars, bringing the total to 87. The candidates were selected using a neural-network, which yields 97% completeness (fraction of actual high-z QSOs selected as such) and an efficiency (fraction of candidates which are high-z QSOs) in the range of 47 to 60%. We use this sample to estimate the binned optical luminosity function of radio-loud QSOs at $z\sim 4$, and also the LF of the total QSO population and its comoving density. Our results suggest that the radio-loud fraction (RLF) at high z is similar to that at low-z and that other authors may be underestimating the fraction at high-z. Finally, we determine the slope of the optical luminosity function and obtain results consistent with previous studies of radio-loud QSOs and of the whole population of QSOs. The evolution of the luminosity function with redshift was for many years interpreted as a flattening of the bright end slope, but has recently been re-interpreted as strong evolution of the break luminosity for high-z QSOs, and our results, for the radio-loud population, are consistent with this.Comment: 20 pages. Accepted for publication in MNRAS on 3 March 201

A primacy code for odor identity

Humans can identify visual objects independently of view angle and lighting, words independently of volume and pitch, and smells independently of concentration. The computational principles underlying invariant object recognition remain mostly unknown. Here we propose that, in olfaction, a small and relatively stable set comprised of the earliest activated receptors forms a code for concentration-invariant odor identity. One prediction of this "primacy coding" scheme is that decisions based on odor identity can be made solely using early odor-evoked neural activity. Using an optogenetic masking paradigm, we define the sensory integration time necessary for odor identification and demonstrate that animals can use information occurring <100 ms after inhalation onset to identify odors. Using multi-electrode array recordings of odor responses in the olfactory bulb, we find that concentration-invariant units respond earliest and at latencies that are within this behaviorally-defined time window. We propose a computational model demonstrating how such a code can be read by neural circuits of the olfactory system

Proposal to study Bs→DˉsJB_s \to \bar D_{sJ} transitions

It is proposed to clear some of the puzzles of B decay to the broad $\bar{D}^{\ast\ast}$ states by studying the corresponding decay with strange states $B_s^0 \to D_{s0}^{\ast -} \pi^+$ at LHCb. Interpretation of the results should be easier due to the narrowness of the $D_{s0}^{\ast -}$ state.Comment: 21 page

Insect Diversity of the Lower Montane Evergreen Forest of the Western Andes Mountain Range: Cascada Chilicay and Suncamal

Biological research in the low montane evergreen forests of Ecuador focuses on ecological and botanical aspects, while knowledge of the entomofauna of these areas is almost nil. In February 2022, sampling was carried out during the dry season for 15 days, using direct and indirect capture methods (tapping, sieving, and light traps) in two waterfalls of the low montane evergreen forest of the western Andean Cordillera: Chilicay and Suncamal waterfalls, with the objective of identifying the composition of the terrestrial insect fauna at the family level. Two orders and 21 families were recorded, among which the families Carabidae and Noctuidae represented the highest percentage of the total abundance. Although preliminary, this work constitutes the first contribution to the knowledge of the entomofauna of this ecosystem. Keywords: biodiversity, conservation, entomofauna, insects. Resumen Las investigaciones biológicas en los bosques siempreverdes montanos bajos de Ecuador, se centran en aspectos ecológicos y botánicos, mientras que el conocimiento de la entomofauna de estas zonas es escaso. En febrero de 2022, en la época seca y durante 15 días, utilizando métodos de captura directa e indirecta (golpeteo, tamizado y trampas de luz), se realizaron muestreos en dos cascadas del Bosque siempreverde montano bajo de la cordillera occidental de los Andes: Cascada Chilicay y Suncamal, con el objetivo de identificar la composición de la fauna de insectos terrestres a nivel de familia. Se registraron dos órdenes y 21 familias, entre las cuales, las familias Carabidae y Noctuidae representaron el mayor porcentaje de la abundancia total. Aunque en forma preliminar, este trabajo constituye el primer aporte al conocimiento de la entomofauna de este ecosistema. Palabras Clave: Biodiversidad, conservación, entomofauna, insectos

Employment Expectations and Gross Flows by Type of Work Contract

There is growing interest in understanding firms’ temporary and permanent employment practices and how institutional changes shape them. Using data on Spanish establishments, we examine: (a) how employers adjust temporary and permanent job and worker flows to prior employment expectations, and (b) how the 1994 and 1997 labour reforms promoting permanent employment affected establishments’ employment practices. Generally, establishments’ prior employment expectations are realized through changes in all job and worker flows. However, establishments uniquely rely on temporary hires as a buffer to confront diminishing long-run employment expectations. None of the reforms significantly affected establishments’ net temporary or permanent employment flows.http://deepblue.lib.umich.edu/bitstream/2027.42/40032/3/wp646.pd

Electron-phonon renormalization of the absorption edge of the cuprous halides

Compared to most tetrahedral semiconductors, the temperature dependence of the absorption edges of the cuprous halides (CuCl, CuBr, CuI) is very small. CuCl and CuBr show a small increase of the gap $E_0$ with increasing temperature, with a change in the slope of $E_0$ vs. $T$ at around 150 K: above this temperature, the variation of $E_0$ with $T$ becomes even smaller. This unusual behavior has been clarified for CuCl by measurements of the low temperature gap vs. the isotopic masses of both constituents, yielding an anomalous negative shift with increasing copper mass. Here we report the isotope effects of Cu and Br on the gap of CuBr, and that of Cu on the gap of CuI. The measured isotope effects allow us to understand the corresponding temperature dependences, which we also report, to our knowledge for the first time, in the case of CuI. These results enable us to develop a more quantitative understanding of the phenomena mentioned for the three halides, and to interpret other anomalies reported for the temperature dependence of the absorption gap in copper and silver chalcogenides; similarities to the behavior observed for the copper chalcopyrites are also pointed out.Comment: 14 pages, 5 figures, submitted to Phys. Rev.

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

The age of data-driven proteomics : how machine learning enables novel workflows

A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. In this viewpoint we therefore point out highly promising recent machine learning developments in proteomics, alongside some of the remaining challenges