Wage Posting or Wage Bargaining? A Test Using Dual Jobholders

This paper examines the behavior of dual jobholders to test a simple model of wage bargaining and wage posting. We estimate the sensitivity of wages and separation rates to wage shocks in a worker’s secondary job to assess the degree of bargaining versus wage posting in the labor market. We interpret the evidence within a model where workers facing hours constraints in their primary job may take a second, flexible-hours job for additional income. When a secondary job offers a sufficiently high wage, a worker either bargains with the primary employer for a wage increase or separates. The model provides a number of predictions that we test using matched employer-employee administrative data from Washington State. In the aggregate, wage bargaining appears to be a limited determinant of wage setting. The estimated wage response to improved outside options, which we interpret as bargaining, is precisely estimated, but qualitatively small. Wage posting appears to be more important than bargaining for wage determination overall, and especially in lower parts of the wage distribution. Observed wage bargaining takes place mainly among workers in the highest wage quartile. For this group, improved outside options translate to higher wages, but not higher separation rates. In contrast, for workers in the lowest wage quartile, wage increases in the secondary job lead to higher separation rates but no significant wage increase in the primary job, consistent with wage posting. We also find evidence in support of the hours-constraint model for dual jobholding. In particular, work hours in the primary job do not respond to wages in the secondary job, but hours and separations in the secondary job are sensitive to wages in the primary job due to income effects

Cross-verification of independent quantum devices

Quantum computers are on the brink of surpassing the capabilities of even the most powerful classical computers. This naturally raises the question of how one can trust the results of a quantum computer when they cannot be compared to classical simulation. Here we present a verification technique that exploits the principles of measurement-based quantum computation to link quantum circuits of different input size, depth, and structure. Our approach enables consistency checks of quantum computations within a device, as well as between independent devices. We showcase our protocol by applying it to five state-of-the-art quantum processors, based on four distinct physical architectures: nuclear magnetic resonance, superconducting circuits, trapped ions, and photonics, with up to 6 qubits and 200 distinct circuits

In vitro analysis of pyrogenicity and cytotoxicity profiles of flex sensors to be used to sense human joint postures

Flex sensors can be usefully adopted as mechanical-electrical transducers to measure human joint movements, since their electrical resistance varies proportionally to the angle assumed by the joint under measure. Over time, these sensors have been investigated in terms of mechanical and electrical behavior, but no reports have detailed the possibility of their adoption not just on top but under the human skin of the joint. To this aim, our work investigated in vitro the pyrogenic potential and cytotoxicity of some commercially available flex sensors as a first step toward the necessary requirements regarding their biocompatibility, to predict possible foreign body reactions when used in vivo. Results demonstrated that some specific flex sensors satisfy such requirement

AKTIP/Ft1, a new shelterin-interacting factor required for telomere maintenance

Telomeres are nucleoprotein complexes that protect the ends of linear chromosomes from incomplete replication, degradation and detection as DNA breaks. Mammalian telomeres are protected by shelterin, a multiprotein complex that binds the TTAGGG telomeric repeats and recruits a series of additional factors that are essential for telomere function. Although many shelterin-associated proteins have been so far identified, the inventory of shelterin-interacting factors required for telomere maintenance is still largely incomplete. Here, we characterize AKTIP/Ft1 (humanAKTIP and mouse Ft1 are orthologous), a novel mammalian shelterin-bound factoridentified on the basis of its homology with the Drosophila telomere protein Pendolino. AKTIP/Ft1 shares homology with the E2 variant ubiquitin-conjugating (UEV) enzymes and has been previously implicated in the control of apoptosis and in vesicle trafficking. RNAi-mediated depletion of AKTIP results in formation of telomere disfunction foci (TIFs). Consistent with these results, AKTIP interacts with telomeric DNA and binds the shelterin components TRF1 and TRF2 both in vivo and in vitro. Analysis of AKTIP- depleted human primary fibroblasts showed that they are defective in PCNA recruiting and arrest in the S phase due to the activation of the intra S checkpoint. Accordingly, AKTIP physically interacts with PCNA and the RPA70 DNA replication factor. Ft1-depleted p53-/- MEFs did not arrest in the S phase but displayed significant increases in multiple telomeric signals (MTS) and sister telomere associations (STAs), two hallmarks of defective telomere replication. In addition, we found an epistatic relation for MST formation between Ft1 and TRF1, which has been previously shown to be required for replication fork progression through telomeric DNA. Ch-IP experiments further suggested that in AKTIP-depleted cells undergoing the S phase, TRF1 is less tightly bound to telomeric DNA than in controls. Thus, our results collectively suggest that AKTIP/Ft1 works in concert with TRF1 to facilitate telomeric DNA replication

Robust and language-independent acoustic features in Parkinson's disease

Introduction: The analysis of vocal samples from patients with Parkinson's disease (PDP) can be relevant in supporting early diagnosis and disease monitoring. Intriguingly, speech analysis embeds several complexities influenced by speaker characteristics (e.g., gender and language) and recording conditions (e.g., professional microphones or smartphones, supervised, or non-supervised data collection). Moreover, the set of vocal tasks performed, such as sustained phonation, reading text, or monologue, strongly affects the speech dimension investigated, the feature extracted, and, as a consequence, the performance of the overall algorithm. Methods: We employed six datasets, including a cohort of 176 Healthy Control (HC) participants and 178 PDP from different nationalities (i.e., Italian, Spanish, Czech), recorded in variable scenarios through various devices (i.e., professional microphones and smartphones), and performing several speech exercises (i.e., vowel phonation, sentence repetition). Aiming to identify the effectiveness of different vocal tasks and the trustworthiness of features independent of external co-factors such as language, gender, and data collection modality, we performed several intra- and inter-corpora statistical analyses. In addition, we compared the performance of different feature selection and classification models to evaluate the most robust and performing pipeline. Results: According to our results, the combined use of sustained phonation and sentence repetition should be preferred over a single exercise. As for the set of features, the Mel Frequency Cepstral Coefficients demonstrated to be among the most effective parameters in discriminating between HC and PDP, also in the presence of heterogeneous languages and acquisition techniques. Conclusion: Even though preliminary, the results of this work can be exploited to define a speech protocol that can effectively capture vocal alterations while minimizing the effort required to the patient. Moreover, the statistical analysis identified a set of features minimally dependent on gender, language, and recording modalities. This discloses the feasibility of extensive cross-corpora tests to develop robust and reliable tools for disease monitoring and staging and PDP follow-up

Comparison of two different classifiers for mental tasks-based Brain-Computer Interface: MLP Neural Networks vs. Fuzzy Logic

This study is devoted to the classification of fourclass mental tasks data for a Brain-Computer Interface protocol. In such view we adopted Multi Layer Perceptron Neural Network (MLP) and Fuzzy C-means analysis for classifying: left and right hand movement imagination, mental subtraction operation and mental recitation of a nursery rhyme. Five subjects participated to the experiment in two sessions recorded in distinct days. Different parameters were considered for the evaluation of the performances of the two classifiers: accuracy, that is, percentage of correct classifications, training time and size of the training dataset. The results show that even if the accuracies of the two classifiers are quite similar, the MLP classifier needs a smaller training set to reach them with respect to the Fuzzy one. This leads to the preference of MLP for the classification of mental tasks in Brain Computer Interface protocols

Genuine Counterfactual Communication with a Nanophotonic Processor

In standard communication information is carried by particles or waves. Counterintuitively, in counterfactual communication particles and information can travel in opposite directions. The quantum Zeno effect allows Bob to transmit a message to Alice by encoding information in particles he never interacts with. The first suggested protocol not only required thousands of ideal optical components, but also resulted in a so-called "weak trace" of the particles having travelled from Bob to Alice, calling the scalability and counterfactuality of previous proposals and experiments into question. Here we overcome these challenges, implementing a new protocol in a programmable nanophotonic processor, based on reconfigurable silicon-on-insulator waveguides that operate at telecom wavelengths. This, together with our telecom single-photon source and highly-efficient superconducting nanowire single-photon detectors, provides a versatile and stable platform for a high-fidelity implementation of genuinely trace-free counterfactual communication, allowing us to actively tune the number of steps in the Zeno measurement, and achieve a bit error probability below 1%, with neither post-selection nor a weak trace. Our demonstration shows how our programmable nanophotonic processor could be applied to more complex counterfactual tasks and quantum information protocols.Comment: 6 pages, 4 figure