Evaluation of the Push-pull Model on the Effect of COVID-19 on the Employment Stability in the Private and Informal Sector in parts of Lagos State, Nigeria

This study tends to examine the push-pull model on the effect of covid-19 on the employment stability in the private and informal sector in parts of Lagos state, Nigeria. Survey method was adopted and population was drawn from employees across the low income private and informal sector in some of the LGA of Lagos state, Nigeria. Questionnaire was sent through the Google form via e-mails and WhatsApp to about 245 target respondents and 229 was returned completed. A Push-Pull model was developed using stochastic statistical model to obtain the transition probabilities and the stationary distribution. The results revealed that 132, with transitional probability of 0.5788, of low-income private sector employees remain on the job and are willing to pull back to work while 92 individuals with transitional probability of 0.4057 wish to explore other industries and only 5 employees with transitional probability of 0.0165 were ready to defect to other industry

Pricing Early-Exercise and Discrete Barrier Options by Fourier-Cosine Series Expansions

We present a pricing method based on Fourier-cosine expansions for early-exercise and discretely-monitored barrier options. The method works well for exponential Levy asset price models. The error convergence is exponential for processes characterized by very smooth transitional probability density functions. The computational complexity is $O((M-1) N \log{N})$ with $N$ a (small) number of terms from the series expansion, and $M$, the number of early-exercise/monitoring dates.

Modeling Human Performance on Statistical Word Segmentation Tasks

Harnessing the orbital angular momentum (OAM) of light is an appealing approach to developing photonic technologies for future applications in optical communications and high-dimensional quantum key distribution (QKD) systems. An outstanding challenge to the widespread uptake of the OAM resource is its efficient generation. In this work we design a new device that can directly emit an OAM-carrying light beam from a low-cost semiconductor laser. By fabricating micro-scale spiral phase plates within the aperture of a vertical-cavity surface-emitting laser (VCSEL), the linearly polarized Gaussian beam emitted by the VCSEL is converted into a beam carrying specific OAM modes and their superposition states, with high efficiency and high beam quality. This new approach to OAM generation may be particularly useful in the field of OAM-based optical and quantum communications, especially for short-reach data interconnects and QKD

Isolated Words Selectively Enhance Memory for High Transitional Probability Sound Sequences

Research over the past two decades has demonstrated that infants are equipped with remarkable computational abilities that allow them to find words in continuous speech. Infants can encode information about the transitional probability (TP) between syllables to segment words from speech when tested immediately after familiarization with an artificial (e.g., Saffran, Aslin & Newport, 1996) or natural language (Pelucchi, Hay, & Saffran, 2009). However, infants’ ability to retain the sequential statistics beyond the immediate familiarization context remains unknown. In the present study, we examine infants’ memory for statistically-defined words 10- minutes following familiarization with a naturally produced Italian corpus. Eight-month-old English-learning infants were familiarized with Italian sentences that contained four embedded target words (see Pelucchi et al., 2009): two words had high internal TP (HTP, TP=1.0) and two had low TP (LTP, TP=.33) and were tested on their ability to discriminate HTP from LTP words using the Headturn Preference Procedure. When discrimination was tested following a 10-minute delay, infants listened equally to HTP and LTP words, suggesting that memory for statistical information likely decays over even short delays (Experiment 1). Experiments 2-4 were designed to test whether experience with isolated words selectively reinforces memory for statistically- defined words. When 8-month-olds were familiarized with the same corpus and then were given experience with the isolated words immediately after familiarization, they looked significantly longer to HTP words than LTP words after the 10-minute delay, suggesting that the experience with isolated words may reinforce memory for HTP words following a delay

Testing the limits of statistical learning for word segmentation

Past research has demonstrated that infants can rapidly extract syllable distribution information from an artificial language and use this knowledge to infer likely word boundaries in speech. However, artificial languages are extremely simplified with respect to natural language. In this study, we ask whether infants’ ability to track transitional probabilities between syllables in an artificial language can scale up to the challenge of natural language. We do so by testing both 5.5- and 8-month-olds’ ability to segment an artificial language containing four words of uniform length (all CVCV) or four words of varying length (two CVCV, two CVCVCV). The transitional probability cues to word boundaries were held equal across the two languages. Both age groups segmented the language containing words of uniform length, demonstrating that even 5.5-month-olds are extremely sensitive to the conditional probabilities in their environment. However, either age group succeeded in segmenting the language containing words of varying length, despite the fact that the transitional probability cues defining word boundaries were equally strong in the two languages. We conclude that infants’ statistical learning abilities may not be as robust as earlier studies have suggested