Hasil untuk "History of Austria. Liechtenstein. Hungary. Czechoslovakia"

Akash Gupta, Ivaxi Sheth, Vyas Raina et al.

With the recent emergence of powerful instruction-tuned large language models (LLMs), various helpful conversational Artificial Intelligence (AI) systems have been deployed across many applications. When prompted by users, these AI systems successfully perform a wide range of tasks as part of a conversation. To provide some sort of memory and context, such approaches typically condition their output on the entire conversational history. Although this sensitivity to the conversational history can often lead to improved performance on subsequent tasks, we find that performance can in fact also be negatively impacted, if there is a task-switch. To the best of our knowledge, our work makes the first attempt to formalize the study of such vulnerabilities and interference of tasks in conversational LLMs caused by task-switches in the conversational history. Our experiments across 5 datasets with 15 task switches using popular LLMs reveal that many of the task-switches can lead to significant performance degradation.

Xiang Dai, Sarvnaz Karimi, Nathan O'Callaghan

Electronic health records include information on patients' status and medical history, which could cover the history of diseases and disorders that could be hereditary. One important use of family history information is in precision health, where the goal is to keep the population healthy with preventative measures. Natural Language Processing (NLP) and machine learning techniques can assist with identifying information that could assist health professionals in identifying health risks before a condition is developed in their later years, saving lives and reducing healthcare costs. We survey the literature on the techniques from the NLP field that have been developed to utilise digital health records to identify risks of familial diseases. We highlight that rule-based methods are heavily investigated and are still actively used for family history extraction. Still, more recent efforts have been put into building neural models based on large-scale pre-trained language models. In addition to the areas where NLP has successfully been utilised, we also identify the areas where more research is needed to unlock the value of patients' records regarding data collection, task formulation and downstream applications.

Guy Tennenholtz, Nadav Merlis, Lior Shani et al.

We introduce Dynamic Contextual Markov Decision Processes (DCMDPs), a novel reinforcement learning framework for history-dependent environments that generalizes the contextual MDP framework to handle non-Markov environments, where contexts change over time. We consider special cases of the model, with a focus on logistic DCMDPs, which break the exponential dependence on history length by leveraging aggregation functions to determine context transitions. This special structure allows us to derive an upper-confidence-bound style algorithm for which we establish regret bounds. Motivated by our theoretical results, we introduce a practical model-based algorithm for logistic DCMDPs that plans in a latent space and uses optimism over history-dependent features. We demonstrate the efficacy of our approach on a recommendation task (using MovieLens data) where user behavior dynamics evolve in response to recommendations.

Charles A. Kang, Mark W. McClure, Somasekhar Reddy

This document describes the functioning of the automated history matching and optimization workflow implemented with the ResFrac hydraulic fracturing and reservoir simulator. The purpose of the automated history matching and optimization workflow is enable automated calibration of simulator models to observed data and application to forward optimization. The automated workflow works by solving a formal mathematical optimization problem to minimize misfit with observations from any point in the lifecycle of a hydraulically fractured reservoir, or to maximize a quantity of interest associated such a reservoir, such as net present value. Objective function evaluations in the optimization problem consist of runs of the ResFrac simulator, which is a physics-based model of hydraulic fracturing and reservoir phenomena. The workflow employs a proxy model to improve computational speed and applies experimental design and Bayesian sampling techniques to generate points with which to train the proxy model. This document also provides an overview of the software infrastructure developed to support the automated workflow.

David L. Nidever, Knut Olsen, Yumi Choi et al.

The Large and Small Magellanic Clouds (LMC and SMC) are the largest satellite galaxies of the Milky Way and close enough to allow for a detailed exploration of their structure and formation history. The Survey of the MAgellanic Stellar History (SMASH) is a community Dark Energy Camera (DECam) survey of the Magellanic Clouds using $\sim$50 nights to sample over $\sim$2400 deg$^2$ centered on the Clouds at $\sim$20% filling factor (but with contiguous coverage in the central regions) and to depths of $\sim$24th mag in $ugriz$. The primary goals of SMASH are to map out the extended stellar peripheries of the Clouds and uncover their complicated interaction and accretion history as well as to derive spatially-resolved star formation histories of the central regions and create a "movie" of their past star formation. Here we announce the second SMASH public data release (DR2), which contains all 197 fully-calibrated DECam fields including the main body fields in the central regions. The DR2 data are available through the Astro Data Lab hosted by the NSF's National Optical-Infrared Astronomy Research Laboratory. We highlight three science cases that make use of the SMASH DR2 data and will be published in the future: (1) preliminary star formation histories of the LMC; (2) the search for Magellanic star clusters using citizen scientists; and, (3) photometric metallicities of Magellanic Cloud stars using the DECam $u$-band.

Saeed Soori, Bugra Can, Mert Gurbuzbalaba et al.

ASYNC is a framework that supports the implementation of asynchrony and history for optimization methods on distributed computing platforms. The popularity of asynchronous optimization methods has increased in distributed machine learning. However, their applicability and practical experimentation on distributed systems are limited because current bulk-processing cloud engines do not provide a robust support for asynchrony and history. With introducing three main modules and bookkeeping system-specific and application parameters, ASYNC provides practitioners with a framework to implement asynchronous machine learning methods. To demonstrate ease-of-implementation in ASYNC, the synchronous and asynchronous variants of two well-known optimization methods, stochastic gradient descent and SAGA, are demonstrated in ASYNC.

Jian Yuan, Guozhong Xiu, Bao Shi et al.

Hereditary effects of exponentially damped oscillators with past histories are considered in this paper. Nonviscously damped oscillators involve hereditary damping forces which depend on time-histories of vibrating motions via convolution integrals over exponentially decaying functions. As a result, this kind of oscillators are said to have memory. In this work, initialization for nonviscously damped oscillators is firstly proposed. Unlike the classical viscously damped ones, information of the past history of response velocity is necessary to fully determine the dynamic behaviors of nonviscously damped oscillators. Then, initialization response of exponentially damped oscillators is obtained to characterize the hereditary effects on the dynamic response. At last, stability of initialization response is proved and the hereditary effects are shown to gradually recede with increasing of time.

David Steinsaltz, Shripad Tuljapurkar

We follow up on a companion work that considered growth rates of populations growing at different sites, with different randomly varying growth rates at each site, in the limit as migration between sites goes to 0. We extend this work here to the special case where the maximum average log growth rate is achieved at two different sites. The primary motivation is to cover the case where `sites' are understood as age classes for the same individuals. The theory then calculates the effect on growth rate of introducing a rare delay in development, a diapause, into an otherwise fixed-length semelparous life history. Whereas the increase in stochastic growth rate due to rare migrations was found to grow as a power of the migration rate, we show that under quite general conditions that in the diapause model --- or in the migration model with two or more sites having equal individual stochastic growth rates --- the increase in stochastic growth rate due to diapause at rate $ε$ behaves like $(\log ε^{-1})^{-1}$ as $ε\downarrow 0$. In particular, this implies that a small random disruption to the deterministic life history will always be favored by natural selection, in the sense that it will increase the stochastic growth rate relative to the zero-delay deterministic life history.

Luyan Xu, Zeon Trevor Fernando, Xuan Zhou et al.

In this demo paper, we introduce LogCanvas, a platform for user search history visualisation. Different from the existing visualisation tools, LogCanvas focuses on helping users re-construct the semantic relationship among their search activities. LogCanvas segments a user's search history into different sessions and generates a knowledge graph to represent the information exploration process in each session. A knowledge graph is composed of the most important concepts or entities discovered by each search query as well as their relationships. It thus captures the semantic relationship among the queries. LogCanvas offers a session timeline viewer and a snippets viewer to enable users to re-find their previous search results efficiently. LogCanvas also provides a collaborative perspective to support a group of users in sharing search results and experience.

Dan Maoz, Or Graur

Recent versions of the observed cosmic star-formation history (SFH) have resolved an inconsistency with the stellar mass density history. We show that the revised SFH also scales up the delay-time distribution (DTD) of Type Ia supernovae (SNe Ia), as determined from the observed volumetric SN Ia rate history, aligning it with other field-galaxy SN Ia DTD measurements. The revised-SFH-based DTD has a $t^{-1.1 \pm 0.1}$ form and a Hubble-time-integrated production efficiency of $N/M_\star=1.3\pm0.1$ SNe Ia per $1000~{\rm M_\odot}$ of formed stellar mass. Using these revised histories and updated empirical iron yields of the various SN types, we re-derive the cosmic iron accumulation history. Core-collapse SNe and SNe Ia have contributed about equally to the total mass of iron in the Universe today. We find the track of the average cosmic gas element in the [$α$/Fe] vs. [Fe/H] abundance-ratio plane. The track is broadly similar to the observed main locus of Galactic stars in this plane, indicating a Milky Way (MW) SFH similar in form to the cosmic one. We easily find a simple MW SFH that makes the track closely match this stellar locus. Galaxy clusters appear to have a higher-normalization DTD. This cluster DTD, combined with a short-burst MW SFH peaked at $z=3$, produces a track that matches remarkably well the observed "high-$α$" locus of MW stars, suggesting the halo/thick-disk population has had a galaxy-cluster-like formation mode. Thus, a simple two-component SFH, combined with empirical DTDs and SN iron yields, suffices to closely reproduce the MW's stellar abundance patterns.

Natalia V. Bhattacharjee, Pritam Ranjan, Abhyuday Mandal et al.

Calibration of hydrological time-series models is a challenging task since these models give a wide spectrum of output series and calibration procedures require significant amount of time. From a statistical standpoint, this model parameter estimation problem simplifies to finding an inverse solution of a computer model that generates pre-specified time-series output (i.e., realistic output series). In this paper, we propose a modified history matching approach for calibrating the time-series rainfall-runoff models with respect to the real data collected from the state of Georgia, USA. We present the methodology and illustrate the application of the algorithm by carrying a simulation study and the two case studies. Several goodness-of-fit statistics were calculated to assess the model performance. The results showed that the proposed history matching algorithm led to a significant improvement, of 30% and 14% (in terms of root mean squared error) and 26% and 118% (in terms of peak percent threshold statistics), for the two case-studies with Matlab-Simulink and SWAT models, respectively.

John Hawks

Human populations have a complex history of introgression and of changing population size. Human genetic variation has been affected by both these processes, so that inference of past population size depends upon the pattern of gene flow and introgression among past populations. One remarkable aspect of human population history as inferred from genetics is a consistent "wave" of larger effective population size, prior to the bottlenecks and expansions of the last 100,000 years. Here I carry out a series of simulations to investigate how introgression and gene flow from genetically divergent ancestral populations affect the inference of ancestral effective population size. Both introgression and gene flow from an extinct, genetically divergent population consistently produce a wave in the history of inferred effective population size. The time and amplitude of the wave reflect the time of origin of the genetically divergent ancestral populations and the strength of introgression or gene flow. These results demonstrate that even small fractions of introgression or gene flow from ancient populations may have large effects on the inference of effective population size.

Alessandro Savino, Maurizio Salaris, Eline Tolstoy

We present a detailed analysis of the Horizontal Branch of the Carina Dwarf Spheroidal Galaxy by means of synthetic modelling techniques, taking consistently into account the star formation history and metallicity evolution as determined from main sequence and red giant branch spectroscopic observations. We found that a range of integrated red giant branch mass loss values of 0.1-0.14 M, increasing with metallicity, is able to reproduce the colour extension of the old Horizontal Branch. However, leaving the mass loss as the only free parameter is not enough to match the detailed morphology of Carina Horizontal Branch. We explored the role played by the star formation history on the discrepancies between synthetic and observed Horizontal Branches. We derived a toy bursty star formation history that reproduces the horizontal branch star counts, and also matches qualitatively the red giant and the turn off regions. This star formation history is made of a subset of age and [M/H] components of the star formation history based on turn off and red giants only, and entails four separate bursts of star formation of different strenghts, centred at 2, 5, 8.6 and 11.5 Gyr, with mean [M/H] decreasing from \sim -1.7 to \sim -2.2 for increasing ages, and a Gaussian spread of 0.1 dex. The comparison between the metallicity distribution function of our star formation history and the one measured from the infrared CaT feature using a CaT-[Fe/H] calibration shows a qualitative agreement, once taken into account the range of [Ca/Fe] abundances measured in a sample of Carina stars, that biases the derived [Fe/H] distribution toward too low values. In conclusion, we have shown how the information contained within the horizontal branch of Carina (and dwarf galaxies in general) can be extracted and interpreted to refine the star formation history derived from red giants and turn off stars only. Abridged

M. Filser, F. Eggers

As noted by numerous studies entrepreneurial orientation (EO) is assumed to have a positive effect on firm performance. However, there is an ongoing debate concerning the importance of each of the constructs’ dimensions namelyinnovativeness, proactiveness and risk-taking and the respective impact of environmental factors. Therefore, the objective of this study is to investigate the influence of the EO dimensions on the performance of small and medium-sizedenterprises (SMEs) in different but neighboring countries. The focus is on the Rhine Valley, a region that covers parts of Austria, Liechtenstein and Switzerland. Based on a telephone survey responses from 304 business owners and CEOs in the Rhine Valley were collected. Multiple regression analysis shows that firm performance is affected by innovativeness and risk-taking and surprisingly not by proactiveness. The findings reveal that firms in different countries show different configurations of EO dimensions. Therefore, our results suggest that firm performance depends on each EO dimension with regard to environmental aspects. Practical as well as theoretical implications are discussed and recommendations for future research are proposed.

V. K. Shchigolev

In the present paper, we study a toy cosmological model derived from the specific behavior of the Hubble parameter and the scale factor in a spatially-flat Friedmann-Robertson-Walker (FRW) space-time. We demonstrate that our model could match in some approximation the complete history of cosmic expansion. To establish the appropriate values of the the model parameters, that is to fit the real universe, we apply some theoretical and observational tests.

Natália Krajčovičová

Mark Cornwall

Agris Gailitis, Gunter Gerbeth, Thomas Gundrum et al.

On 11 November 1999, a self-exciting magnetic eigenfield was detected for the first time in the Riga liquid sodium dynamo experiment. We report on the long history leading to this event, and on the subsequent experimental campaigns which provided a wealth of data on the kinematic and the saturated regime of this dynamo. The present state of the theoretical understanding of both regimes is delineated, and some comparisons with other laboratory dynamo experiments are made.

Kurt Peball