11 research outputs found
Distributed Service Broker Policy Algorithm for Logistics over Cloud
Logistics information system focuses on flow of information with storage and services of goods supply from the origin point to consumption point of organization. Logistics information system makes this flow more efficient with the help of cloud. Cloud computing manages the logistics information system centrally. The centralized data center keeps the track of information distribution which creates network congestion and overloading on data center when various requests of users from different regions occur at same time. So, the data center needs to be maintained effectively for better performance. This paper presents the distributed service broker policy to implement centralized data center and proposes distributed data center for logistics information system over cloud. This paper also presents the result of distributed service broker policy algorithm to reduce network congestion, higher latency and cost due to large number of demand of particular service in distributed data center for logistics
Improving the Reproductive Health of Married and Unmarried Youth in India
Provides insights and lessons learned from a ten-year multi-partner research program to improve youth reproductive and sexual health in India. Includes recommendations to strengthen community and government efforts
MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks
Recently, there has been a rapid advancement in research on Large Language
Models (LLMs), resulting in significant progress in several Natural Language
Processing (NLP) tasks. Consequently, there has been a surge in LLM evaluation
research to comprehend the models' capabilities and limitations. However, much
of this research has been confined to the English language, leaving LLM
building and evaluation for non-English languages relatively unexplored. There
has been an introduction of several new LLMs, necessitating their evaluation on
non-English languages. This study aims to expand our MEGA benchmarking suite by
including six new datasets to form the MEGAVERSE benchmark. The benchmark
comprises 22 datasets covering 81 languages, including low-resource African
languages. We evaluate several state-of-the-art LLMs like GPT-3.5-Turbo, GPT4,
PaLM2, and Llama2 on the MEGAVERSE datasets. Additionally, we include two
multimodal datasets in the benchmark and assess the performance of the
LLaVa-v1.5 model. Our experiments suggest that GPT4 and PaLM2 outperform the
Llama models on various tasks, notably on low-resource languages, with GPT4
outperforming PaLM2 on more datasets than vice versa. However, issues such as
data contamination must be addressed to obtain an accurate assessment of LLM
performance on non-English languages.Comment: 23 pages, 30 figures and 1 tabl
MEGA: Multilingual Evaluation of Generative AI
Generative AI models have shown impressive performance on many Natural
Language Processing tasks such as language understanding, reasoning, and
language generation. An important question being asked by the AI community
today is about the capabilities and limits of these models, and it is clear
that evaluating generative AI is very challenging. Most studies on generative
LLMs have been restricted to English and it is unclear how capable these models
are at understanding and generating text in other languages. We present the
first comprehensive benchmarking of generative LLMs - MEGA, which evaluates
models on standard NLP benchmarks, covering 16 NLP datasets across 70
typologically diverse languages. We compare the performance of generative LLMs
including Chat-GPT and GPT-4 to State of the Art (SOTA) non-autoregressive
models on these tasks to determine how well generative models perform compared
to the previous generation of LLMs. We present a thorough analysis of the
performance of models across languages and tasks and discuss challenges in
improving the performance of generative LLMs on low-resource languages. We
create a framework for evaluating generative LLMs in the multilingual setting
and provide directions for future progress in the field.Comment: EMNLP 202
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DES Y1 results: splitting growth and geometry to test ?cDM
We analyze Dark Energy Survey (DES) data to constrain a cosmological model where a subset of parameters - focusing on ?m - are split into versions associated with structure growth (e.g., ?mgrow) and expansion history (e.g., ?mgeo). Once the parameters have been specified for the ?CDM cosmological model, which includes general relativity as a theory of gravity, it uniquely predicts the evolution of both geometry (distances) and the growth of structure over cosmic time. Any inconsistency between measurements of geometry and growth could therefore indicate a breakdown of that model. Our growth-geometry split approach therefore serves both as a (largely) model-independent test for beyond-?CDM physics, and as a means to characterize how DES observables provide cosmological information. We analyze the same multiprobe DES data as [Phys. Rev. Lett. 122, 171301 (2019)PRLTAO0031-900710.1103/PhysRevLett.122.171301]: DES Year 1 (Y1) galaxy clustering and weak lensing, which are sensitive to both growth and geometry, as well as Y1 BAO and Y3 supernovae, which probe geometry. We additionally include external geometric information from BOSS DR12 BAO and a compressed Planck 2015 likelihood, and external growth information from BOSS DR12 RSD. We find no significant disagreement with ?mgrow=?mgeo. When DES and external data are analyzed separately, degeneracies with neutrino mass and intrinsic alignments limit our ability to measure ?mgrow, but combining DES with external data allows us to constrain both growth and geometric quantities. We also consider a parametrization where we split both ?m and w, but find that even our most constraining data combination is unable to separately constrain ?mgrow and wgrow. Relative to ?CDM, splitting growth and geometry weakens bounds on s8 but does not alter constraints on h
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Probing Galaxy Evolution in Massive Clusters Using ACT and DES: Splashback as a Cosmic Clock
We measure the projected number density profiles of galaxies and the splashback feature in clusters selected by the Sunyaev–Zel’dovich effect from the Advanced Atacama Cosmology Telescope (AdvACT) survey using galaxies observed by the Dark Energy Survey (DES). The splashback radius is consistent with CDM-only simulations and is located at -
+ -h2.4 Mpc0.4 0.3 1. We split the galaxies on color and find significant differences in their profile shapes.
Red and green-valley galaxies show a splashback-like minimum in their slope profile consistent with theory, while the bluest galaxies show a weak feature at a smaller radius. We develop a mapping of galaxies to subhalos in simulations and assign colors based on infall time onto their hosts. We find that the shift in location of the steepest slope and different profile shapes can be mapped to the average time of infall of galaxies of different colors. The steepest slope traces a discontinuity in the phase space of dark matter halos. By relating spatial profiles to infall time, we can use splashback as a clock to understand galaxy quenching. We find that red galaxies have on average been in clusters over 3.2 Gyr, green galaxies about 2.2 Gyr, while blue galaxies have been accreted most recently and have not reached apocenter. Using the full radial profiles, we fit a simple quenching model and find that the onset of galaxy quenching occurs after a delay of about a gigayear and that galaxies quench rapidly thereafter with
an exponential timescale of 0.6 Gyr
Dark Energy Survey Year 3 results: Curved-sky weak lensing mass map reconstruction
ABSTRACT
We present reconstructed convergence maps, mass maps, from the Dark Energy Survey (DES) third year (Y3) weak gravitational lensing data set. The mass maps are weighted projections of the density field (primarily dark matter) in the foreground of the observed galaxies. We use four reconstruction methods, each is a maximum a posteriori estimate with a different model for the prior probability of the map: Kaiser–Squires, null B-mode prior, Gaussian prior, and a sparsity prior. All methods are implemented on the celestial sphere to accommodate the large sky coverage of the DES Y3 data. We compare the methods using realistic ΛCDM simulations with mock data that are closely matched to the DES Y3 data. We quantify the performance of the methods at the map level and then apply the reconstruction methods to the DES Y3 data, performing tests for systematic error effects. The maps are compared with optical foreground cosmic-web structures and are used to evaluate the lensing signal from cosmic-void profiles. The recovered dark matter map covers the largest sky fraction of any galaxy weak lensing map to date