An analysis of the determinants behind having an additional job by employees

Apart from having the main job, many people in Poland decide to take additional jobs. There are many potential factors which determine having a second job. These include varied needs of individuals, such as the desire to improve their material status, family situation, or the opportunities arising from human capital. In this study, apart from the aforementioned needs, the features of individuals, such as age, sex, place of residence and the features of the main workplace have been included. Unfortunately, some determinants of the studied phenomenon cannot be clearly observed or are generally unobservable. Hence, the models with unobservable heterogeneity, which were used in this study, are of particular importance in modelling this type of phenomena. The purpose of this paper was to show the demographic profile of a two-job worker. This has been done by the assessment of the impact of selected determinants on having an additional job. Furthermore, the scale of the impact of the studied determinants has been compared in the case of women and men. The study used the Bayesian logistic regression model

Socioeconomic aspects of long-term unemployment in the context of the ageing population of Europe: the case of Poland

In view of the ageing populations of Europe, an important current challenge for labour markets is to increase the professional activity of those social groups whose participation in the labour market is insufficient. This work focuses on people unemployed for 12 months or more. The main purpose of this study is designation of those social groups which have the greatest problems with getting out of long-term unemployment, and assessment of the consequences of long-term unemployment, depending on its duration, in the context of the ageing society. We have investigated this problem using the example of Poland, which suffers from particularly low fertility rates. In this study, data from the Labour Force Survey (L.F.S.) for Poland have been used to model the duration of long-term unemployment. In the analysis the accelerated failure time models (A.F.T.) in the Bayesian approach have been used. Our results show, among other things, that difficulties in getting out of long-term unemployment mainly affect women and people who have children or who looked after children directly before the start of their job search. This, in consequence, may deepen the problem of the decrease in labour supply caused by the ageing populatio

The use of self-organising maps to investigate heat demand profiles

District heating companies are responsible for delivering the heat produced in central heat plants to the consumers through a pipeline system. At the same time they are expected to keep the total heat production cost as low as possible. Therefore, there is a growing need to optimise heat production through better prediction of customers needs. The paper illustrates the way neural networks, namely self-organised maps can be used to investigate long-term demand profiles of consumers. Real-life historical sales data is used to establish a number of typical demand profiles

INFORMATIVE VERSUS NON-INFORMATIVE PRIOR DISTRIBUTIONS AND THEIR IMPACT ON THE ACCURACY OF BAYESIAN INFERENCE

In this study the benefits arising from the use of the Bayesian approach to predictive modelling will be outlined and exemplified by a linear regression model and a logistic regression model. The impact of informative and non-informative prior on model accuracy will be examined and compared. The data from the Central Statistical Office of Poland describing unemployment in individual districts in Poland will be used. Markov Chain Monte Carlo methods (MCMC) will be employed in modelling

Clinical value of serum eosinophilic cationic protein assessment in children with inflammatory bowel disease

Introduction: Eosinophils contribute to the pathogenesis of inflammatory bowel disease (IBD) in the intestine. Eosinophilic cationic protein (ECP) is one of the most important eosinophilic specific mediators released during activation. The aim of the study was to evaluate the clinical value of serum ECP determination in children with active and inactive IBD and its correlation with disease activity. Material and methods: There were 125 children with IBD (63 with Crohn’s disease - CD, 44 with ulcerative colitis - UC, 18 indeterminate colitis - IC) enrolled in the study. Among them 83 children were in the active phase of the disease, while the remaining 42 were in remission. The control group consisted of 56 healthy children. The ECP was assessed three times in children with active IBD, at baseline and after 2 and 6 weeks of treatment and once in children with inactive IBD and controls using fluoroenzymeimmunoassays. Results: We found elevated ECP at baseline in the total active IBD group when compared to the inactive IBD and control groups, decreasing during treatment. Serum ECP was also elevated in the active UC and CD groups when compared to the inactive UC and CD groups, and correlated with clinical UC and CD activity (R = 0.57 and R = 0.52, p < 0.05, respectively) and duration of the clinical manifestation in UC (R = 0.62, p < 0.05) but not with the disease location in the gastrointestinal tract, or endoscopic and histopathological activity. Conclusions: Evaluation of serum ECP in children with IBD may be useful in disease activity assessment at onset and during the treatment

A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams

Unlabelled data appear in many domains and are particularly relevant to streaming applications, where even though data is abundant, labelled data is rare. To address the learning problems associated with such data, one can ignore the unlabelled data and focus only on the labelled data (supervised learning); use the labelled data and attempt to leverage the unlabelled data (semi-supervised learning); or assume some labels will be available on request (active learning). The first approach is the simplest, yet the amount of labelled data available will limit the predictive performance. The second relies on finding and exploiting the underlying characteristics of the data distribution. The third depends on an external agent to provide the required labels in a timely fashion. This survey pays special attention to methods that leverage unlabelled data in a semi-supervised setting. We also discuss the delayed labelling issue, which impacts both fully supervised and semi-supervised methods. We propose a unified problem setting, discuss the learning guarantees and existing methods, explain the differences between related problem settings. Finally, we review the current benchmarking practices and propose adaptations to enhance them

Memory Injections: Correcting Multi-Hop Reasoning Failures during Inference in Transformer-Based Language Models

Answering multi-hop reasoning questions requires retrieving and synthesizing information from diverse sources. Large Language Models (LLMs) struggle to perform such reasoning consistently. Here we propose an approach to pinpoint and rectify multi-hop reasoning failures through targeted memory injections on LLM attention heads. First, we analyze the per-layer activations of GPT-2 models in response to single and multi-hop prompts. We then propose a mechanism that allows users to inject pertinent prompt-specific information, which we refer to as "memories," at critical LLM locations during inference. By thus enabling the LLM to incorporate additional relevant information during inference, we enhance the quality of multi-hop prompt completions. We show empirically that a simple, efficient, and targeted memory injection into a key attention layer can often increase the probability of the desired next token in multi-hop tasks, by up to 424%

Attention Lens: A Tool for Mechanistically Interpreting the Attention Head Information Retrieval Mechanism

Transformer-based Large Language Models (LLMs) are the state-of-the-art for natural language tasks. Recent work has attempted to decode, by reverse engineering the role of linear layers, the internal mechanisms by which LLMs arrive at their final predictions for text completion tasks. Yet little is known about the specific role of attention heads in producing the final token prediction. We propose Attention Lens, a tool that enables researchers to translate the outputs of attention heads into vocabulary tokens via learned attention-head-specific transformations called lenses. Preliminary findings from our trained lenses indicate that attention heads play highly specialized roles in language models. The code for Attention Lens is available at github.com/msakarvadia/AttentionLens