Separability in Asymmetric Phase-Covariant Cloning

Here, asymmetric phase-covariant quantum cloning machines are defined and trade-off between qualities of their outputs and its impact on entanglement properties of the outputs are studies. In addition, optimal families among these cloners are introduced and also their entanglement properties are investigated. An explicit proof of optimality is presented for the case of qubits, which is based on the no-signaling condition. Our optimality proof can also be used to derive an upper bound on trade-off relations for a more general class of optimal cloners which clone states on a specific orbit of the Bloch sphere. It is shown that the optimal cloners of the equatorial states, as in the case of symmetric phase-covariant cloning, give rise to two separable clones, and in this sense these states are unique. For these cloners it is shown that total output is of GHZ-type

AfriMTE and AfriCOMET : Empowering COMET to Embrace Under-resourced African Languages

Despite the progress we have recorded in scaling multilingual machine translation (MT) models and evaluation data to several under-resourced African languages, it is difficult to measure accurately the progress we have made on these languages because evaluation is often performed on n-gram matching metrics like BLEU that often have worse correlation with human judgments. Embedding-based metrics such as COMET correlate better; however, lack of evaluation data with human ratings for under-resourced languages, complexity of annotation guidelines like Multidimensional Quality Metrics (MQM), and limited language coverage of multilingual encoders have hampered their applicability to African languages. In this paper, we address these challenges by creating high-quality human evaluation data with a simplified MQM guideline for error-span annotation and direct assessment (DA) scoring for 13 typologically diverse African languages. Furthermore, we develop AfriCOMET, a COMET evaluation metric for African languages by leveraging DA training data from high-resource languages and African-centric multilingual encoder (AfroXLM-Roberta) to create the state-of-the-art evaluation metric for African languages MT with respect to Spearman-rank correlation with human judgments (+0.406)

Perception of facial and vocal affect by people with schizophrenia in early and late stages of illness

BACKGROUND: Emotion recognition impairments have been demonstrated in schizophrenia, but few studies have examined whether these reflect generalised or specific perceptual deficits or are associated with illness course. AIMS: To examine the nature of emotion recognition abnormalities in patients with schizophrenia at different stages of illness. METHOD: We examined the performance of 50 in-patients with early-stage schizophrenia, 50 with chronic schizophrenia and 50 healthy controls on the Benton Facial Recognition Test, Facial Emotion Recognition Test and Voice Emotion Recognition Test. RESULTS: Patients with chronic schizophrenia were significantly more impaired than other groups on the emotional tasks, even after controlling for impairments in non-emotional stimuli. Individual emotion recognition accuracy for the two sensory modalities was not significantly positively correlated for either group with schizophrenia. CONCLUSIONS: Emotion recognition deficits in schizophrenia are trait features of the disorder and increase with illness duration

Motives and profiles of ICO investors

Research on initial coin offerings (ICOs) is nascent and assesses ICOs from the perspectives of ventures and regulators. Little is known about the equally important group of investors who provide their capital to ventures in ICOs. Using a primary dataset of 517 ICO investors, we identify and categorize the motivations to invest in ICOs using factor analysis. We find that investors are driven by ideological, technological, and financial motives. Regarding the relative importance of the motives, we find that technological motives are the most important motives to ICO investors, followed by financial and ideological motives. To further profile investors, we conduct a regression analysis to distinguish investors across different motives. For example, we show significant differences across motives with regard to investors' risk perception, sources of information, and demand for strict regulation. The implications of this study for both theory and practice are considerable