21-cm absorption from galaxies at z ~ 0.3

We report the detection of 21-cm absorption from foreground galaxies towards quasars, specifically z_gal = 0.3120 towards SDSS J084957.97+510829.0 (z_qso = 0.584; Pair-I) and z_gal = 0.3714 towards SDSS J144304.53+021419.3 (z_qso = 1.82; Pair-II). In both the cases, the integrated 21-cm optical depth is consistent with the absorbing gas being a damped Lyman-\alpha (DLA) system. In the case of Pair-I, strong Na I and Ca II absorption are also detected at z_gal in the QSO spectrum. We identify an early-type galaxy at an impact parameter of b ~ 14 kpc whose photometric redshift is consistent with that of the detected metal and 21-cm absorption lines. This would be the first example of an early-type galaxy associated with an intervening 21-cm absorber. The gas detected in 21-cm and metal absorption lines in the outskirts of this luminous red galaxy could be associated with the reservoir of cold H I gas with a low level of star formation activity in the outer regions of the galaxy as reported in the literature for z ~ 0.1 early-type galaxies. In the case of Pair-II, the absorption is associated with a low surface brightness galaxy that, unlike most other known quasar-galaxy pairs (QGPs) i.e. QSO sight lines passing through disks/halos of foreground galaxies, is identified only via narrow optical emission lines detected on top of the QSO spectra. Using SDSS spectra we infer that the emission lines originate within ~ 5 kpc of the QSO sight line, and the gas has metallicity [12+O/H] ~ 8.4 and star formation rate ~ 0.7-0.8 M_sun per yr. The measured 21-cm optical depth can be reconciled with the N(H I) we derive from the measured extinction (A_V=0.6) if either the H I gas is warm or the extinction per hydrogen atom in this galaxy is much higher than the mean value of the Small Magellanic Cloud. (Abridged)Comment: 8 pages, 7 figures, 3 tables (A&A in press

Penormaan Pengawasan Izin Lingkungan dalam Pencegahan Pencemaran dan Kerusakan Lingkungan Hidup dalam Eksploitasi Sumber Daya Alam

In environmental protection and management, the main effort is to prevent pollution and damage on environment instead of repressing the damages occurred. Permit is one of preventive measures and becomes a principle in Administrative Law. Permits can be seen as government\u27s tool as judicial preventive and used as an administrative instrument to control people\u27s behavior. Environmental permit can be seen as preventive measure, because it always related to orders and obligations that must be obeyed by the holder. On the other hand, environmental permit also function as repressive instrument to counter environmental problems due to human activities, including mining. The norm\u27s obscurity on the supervision of environmental permits in in Law No. 32 Year 2009 on Environmental Protection and Management (hereafter will refer as UUPPLH) is an indicator for the lack of the objective results.Based on type, this research focuses the study on the Environmental permit as an absolute requirement. Normatively, the principle of environmental permit as stipulated in Environmental Law regulates that every business and/or activity which required an Environmental Impact Analysis document or UKL-UPL should also hold an environmental permit. The purpose of Environmental permits is to maintain the preservation of environmental functions while also prevent and counter environmental pollution and damage due to human activities. Based on this construction, permits plays a very important role in environmental activity. Exploitation of natural resources has a significant impact on the environment, thus based on Article 22 paragraph (1) of Environmental Law these activities requires an Environment Impact Analysis. Important impacts as detailed in Article 22 paragraph (2) at empirical level still occurs so the goal of preventing pollution and damage as the objective of environmental permits still has not been achieved

A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions

The field of machine programming (MP), the automation of the development of software, is making notable research advances. This is, in part, due to the emergence of a wide range of novel techniques in machine learning. In this paper, we apply MP to the automation of software performance regression testing. A performance regression is a software performance degradation caused by a code change. We present AutoPerf–a novel approach to automate regression testing that utilizes three core techniques:(i) zero-positive learning,(ii) autoencoders, and (iii) hardware telemetry. We demonstrate AutoPerf’s generality and efficacy against 3 types of performance regressions across 10 real performance bugs in 7 benchmark and open-source programs. On average, AutoPerf exhibits 4% profiling overhead and accurately diagnoses more performance bugs than prior state-of-the-art approaches. Thus far, AutoPerf has produced no false negatives

Learning Fitness Functions for Machine Programming

The problem of automatic software generation is known as Machine Programming. In this work, we propose a framework based on genetic algorithms to solve this problem. Although genetic algorithms have been used successfully for many problems, one criticism is that hand-crafting its fitness function, the test that aims to effectively guide its evolution, can be notably challenging. Our framework presents a novel approach to learn the fitness function using neural networks to predict values of ideal fitness functions. We also augment the evolutionary process with a minimally intrusive search heuristic. This heuristic improves the framework's ability to discover correct programs from ones that are approximately correct and does so with negligible computational overhead. We compare our approach with several state-of-the-art program synthesis methods and demonstrate that it finds more correct programs with fewer candidate program generations

An HST/COS legacy survey of high-velocity ultraviolet absorption in the Milky Way's circumgalactic medium and the Local Group

To characterize the absorption properties of this circumgalactic medium (CGM) and its relation to the LG we present the so-far largest survey of metal absorption in Galactic high-velocity clouds (HVCs) using archival ultraviolet (UV) spectra of extragalactic background sources. The UV data are obtained with the Cosmic Origins Spectrograph (COS) onboard the Hubble Space Telescope (HST) and are supplemented by 21 cm radio observations of neutral hydrogen. Along 270 sightlines we measure metal absorption in the lines of SiII, SiIII, CII, and CIV and associated HI 21 cm emission in HVCs in the velocity range |v_LSR|=100-500 km s^-1. With this unprecedented large HVC sample we were able to improve the statistics on HVC covering fractions, ionization conditions, small-scale structure, CGM mass, and inflow rate. For the first time, we determine robustly the angular two point correlation function of the high-velocity absorbers, systematically analyze antipodal sightlines on the celestial sphere, and compare the absorption characteristics with that of Damped Lyman alpha absorbers (DLAs) and constrained cosmological simulations of the LG. Our study demonstrates that the Milky Way CGM contains sufficient gaseous material to maintain the Galactic star-formation rate at its current level. We show that the CGM is composed of discrete gaseous structures that exhibit a large-scale kinematics together with small-scale variations in physical conditions. The Magellanic Stream clearly dominates both the cross section and mass flow of high-velocity gas in the Milky Way's CGM. The possible presence of high-velocity LG gas underlines the important role of the local cosmological environment in the large-scale gas-circulation processes in and around the Milky Way (abridged).Comment: 37 pages, 25 figures, 8 tables, accepted for publication in A&

The Relation Between Galaxy ISM and Circumgalactic OVI Gas Kinematics Derived from Observations and Λ\LambdaCDM Simulations

We present the first galaxy-OVI absorption kinematic study for 20 absorption systems (EW>0.1~{\AA}) associated with isolated galaxies (0.15$<z<$0.55) that have accurate redshifts and rotation curves obtained using Keck/ESI. Our sample is split into two azimuthal angle bins: major axis ($\Phi<25^{\circ}$) and minor axis ($\Phi>33^{\circ}$). OVI absorption along the galaxy major axis is not correlated with galaxy rotation kinematics, with only 1/10 systems that could be explained with rotation/accretion models. This is in contrast to co-rotation commonly observed for MgII absorption. OVI along the minor axis could be modeled by accelerating outflows but only for small opening angles, while the majority of the OVI is decelerating. Along both axes, stacked OVI profiles reside at the galaxy systemic velocity with the absorption kinematics spanning the entire dynamical range of their galaxies. The OVI found in AMR cosmological simulations exists within filaments and in halos of ~50 kpc surrounding galaxies. Simulations show that major axis OVI gas inflows along filaments and decelerates as it approaches the galaxy while increasing in its level of co-rotation. Minor axis outflows in the simulations are effective within 50-75 kpc beyond that they decelerate and fall back onto the galaxy. Although the simulations show clear OVI kinematic signatures they are not directly comparable to observations. When we compare kinematic signatures integrated through the entire simulated galaxy halo we find that these signatures are washed out due to full velocity distribution of OVI throughout the halo. We conclude that OVI alone does not serve as a useful kinematic indicator of gas accretion, outflows or star-formation and likely best probes the halo virial temperature.Comment: 24 pages, 21 figures, 4 tables. Accepted to ApJ on November 14, 201

A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data

The main purpose of this research is to study the effect of various types of venues on the density distribution of residents and model check-in data from a Location-Based Social Network for the city of Shanghai, China by using combination of multiple temporal, spatial and visualization techniques by classifying users’ check-ins into different venue categories. This article investigates the use of Weibo for big data analysis and its efficiency in various categories instead of manually collected datasets, by exploring the relation between time, frequency, place and category of check-in based on location characteristics and their contributions. The data used in this research was acquired from a famous Chinese microblogs called Weibo, which was preprocessed to get the most significant and relevant attributes for the current study and transformed into Geographical Information Systems format, analyzed and, finally, presented with the help of graphs, tables and heat maps. The Kernel Density Estimation was used for spatial analysis. The venue categorization was based on nature of the physical locations within the city by comparing the name of venue extracted from Weibo dataset with the function such as education for schools or shopping for malls and so on. The results of usage patterns from hours to days, venue categories and frequency distribution into these categories as well as the density of check-in within the Shanghai and contribution of each venue category in its diversity are thoroughly demonstrated, uncovering interesting spatio-temporal patterns including frequency and density of users from different venues at different time intervals, and significance of using geo-data from Weibo to study human behavior in variety of studies like education, tourism and city dynamics based on location-based social networks. Our findings uncover various aspects of activity patterns in human behavior, the significance of venue classes and its effects in Shanghai, which can be applied in pattern analysis, recommendation systems and other interactive applications for these classes.</jats:p

Large Language Models Based Automatic Synthesis of Software Specifications

Software configurations play a crucial role in determining the behavior of software systems. In order to ensure safe and error-free operation, it is necessary to identify the correct configuration, along with their valid bounds and rules, which are commonly referred to as software specifications. As software systems grow in complexity and scale, the number of configurations and associated specifications required to ensure the correct operation can become large and prohibitively difficult to manipulate manually. Due to the fast pace of software development, it is often the case that correct software specifications are not thoroughly checked or validated within the software itself. Rather, they are frequently discussed and documented in a variety of external sources, including software manuals, code comments, and online discussion forums. Therefore, it is hard for the system administrator to know the correct specifications of configurations due to the lack of clarity, organization, and a centralized unified source to look at. To address this challenge, we propose SpecSyn a framework that leverages a state-of-the-art large language model to automatically synthesize software specifications from natural language sources. Our approach formulates software specification synthesis as a sequence-to-sequence learning problem and investigates the extraction of specifications from large contextual texts. This is the first work that uses a large language model for end-to-end specification synthesis from natural language texts. Empirical results demonstrate that our system outperforms prior the state-of-the-art specification synthesis tool by 21% in terms of F1 score and can find specifications from single as well as multiple sentences

Detection of two intervening Ne viii absorbers probing warm gas at z ~ 0.6

Large scale structure and cosmolog