Efficiency of Thin and Thick Markets

In this paper, we propose a matching model to study the efficiency of thin and thick markets. Our model shows that the probabilities of matches in a thin market are significantly lower than those in a thick market. When applying our results to a job search model, it implies that, if the ratio of job candidates to job openings remains (roughly) a constant, the probability that a person can find a job is higher in a thick market than in a thin market. We apply our matching model to the U.S. academic market for new PhD economists. Consistent with the prediction of our model, a field of specialization with more job openings and more candidates has a higher probability of matching.

Initial Wage, Human Capital and Post Wage Differentials

Insufficiency in information with which firms judge the productivity of a worker for the first time in the market creates more randomness in initial wages than in later wages. This paper examines whether the initial randomness in wages may have a persistent effecton post wages. We set up a human capital accumulation in which an individual may respond to the positive error in initial wage by adjusting hours worked thereafter in her career, and consequently may receive higher future wages than those who draw a negative error in initial wages but otherwise are equivalent. The model predicts that the initial wage, in particular, its random component, is a persistently important factor having positive effecton future wages. Using data from the National Longitudinal Survey of Youth 79, we find empirical evidence that this effect is indeed positive and persists even after 20 years since the initial entry to labor market. The decomposition of initial wages by both parametric and nonparametric IV methods further shows that this effectis derived by the random component, nott he observable component, of the initial wage. It implies that the observed cross-sectional wage variation within group can be accounted for the initial randomness in wages.Human Capital Accumulation, Learning, Initial Wage, Wage Differentials

A kinematic wave theory of capacity drop

Capacity drop at active bottlenecks is one of the most puzzling traffic phenomena, but a thorough understanding is practically important for designing variable speed limit and ramp metering strategies. In this study, we attempt to develop a simple model of capacity drop within the framework of kinematic wave theory based on the observation that capacity drop occurs when an upstream queue forms at an active bottleneck. In addition, we assume that the fundamental diagrams are continuous in steady states. This assumption is consistent with observations and can avoid unrealistic infinite characteristic wave speeds in discontinuous fundamental diagrams. A core component of the new model is an entropy condition defined by a discontinuous boundary flux function. For a lane-drop area, we demonstrate that the model is well-defined, and its Riemann problem can be uniquely solved. We theoretically discuss traffic stability with this model subject to perturbations in density, upstream demand, and downstream supply. We clarify that discontinuous flow-density relations, or so-called "discontinuous" fundamental diagrams, are caused by incomplete observations of traffic states. Theoretical results are consistent with observations in the literature and are verified by numerical simulations and empirical observations. We finally discuss potential applications and future studies.Comment: 29 pages, 10 figure

Combined Field Integral Equation Based Theory of Characteristic Mode

Conventional electric field integral equation based theory is susceptible to the spurious internal resonance problem when the characteristic modes of closed perfectly conducting objects are computed iteratively. In this paper, we present a combined field integral equation based theory to remove the difficulty of internal resonances in characteristic mode analysis. The electric and magnetic field integral operators are shown to share a common set of non-trivial characteristic pairs (values and modes), leading to a generalized eigenvalue problem which is immune to the internal resonance corruption. Numerical results are presented to validate the proposed formulation. This work may offer efficient solutions to characteristic mode analysis which involves electrically large closed surfaces

(Triphenyl­phosphine-κP)[1,1,1-tris­(diphenyl­phosphinometh­yl)ethane-κ3 P,P′,P′′]copper(I) tetra­fluoridoborate

In the title mononuclear CuI complex, [Cu(C18H15P)(C41H39P3)]BF4, the cation has a basic rigid core structure reminiscent of the framework of diamond. The metal atom is coordinated by four P atoms in a distorted tetra­hedral geometry, the distortion arising from the steric hindrance of the phenyl groups. The anion is disordered over two positions, with an occupancy ratio of 0.524 (17):0.476 (17). The cations and anions are closely packed in the crystal and are in h.c.p. arrangements

Interaction in Metaverse: A Survey

Human-computer interaction (HCI) emerged with the birth of the computer and has been upgraded through decades of development. Metaverse has attracted a lot of interest with its immersive experience, and HCI is the entrance to the Metaverse for people. It is predictable that HCI will determine the immersion of the Metaverse. However, the technologies of HCI in Metaverse are not mature enough. There are many issues that we should address for HCI in the Metaverse. To this end, the purpose of this paper is to provide a systematic literature review on the key technologies and applications of HCI in the Metaverse. This paper is a comprehensive survey of HCI for the Metaverse, focusing on current technology, future directions, and challenges. First, we provide a brief overview of HCI in the Metaverse and their mutually exclusive relationships. Then, we summarize the evolution of HCI and its future characteristics in the Metaverse. Next, we envision and present the key technologies involved in HCI in the Metaverse. We also review recent case studies of HCI in the Metaverse. Finally, we highlight several challenges and future issues in this promising area.Comment: Preprint. 3 figures, 3 table

Beyond Probability Partitions: Calibrating Neural Networks with Semantic Aware Grouping

Research has shown that deep networks tend to be overly optimistic about their predictions, leading to an underestimation of prediction errors. Due to the limited nature of data, existing studies have proposed various methods based on model prediction probabilities to bin the data and evaluate calibration error. We propose a more generalized definition of calibration error called Partitioned Calibration Error (PCE), revealing that the key difference among these calibration error metrics lies in how the data space is partitioned. We put forth an intuitive proposition that an accurate model should be calibrated across any partition, suggesting that the input space partitioning can extend beyond just the partitioning of prediction probabilities, and include partitions directly related to the input. Through semantic-related partitioning functions, we demonstrate that the relationship between model accuracy and calibration lies in the granularity of the partitioning function. This highlights the importance of partitioning criteria for training a calibrated and accurate model. To validate the aforementioned analysis, we propose a method that involves jointly learning a semantic aware grouping function based on deep model features and logits to partition the data space into subsets. Subsequently, a separate calibration function is learned for each subset. Experimental results demonstrate that our approach achieves significant performance improvements across multiple datasets and network architectures, thus highlighting the importance of the partitioning function for calibration

A Trigeminoreticular Pathway: Implications in Pain

Neurons in the caudalmost ventrolateral medulla (cmVLM) respond to noxious stimulation. We previously have shown most efferent projections from this locus project to areas implicated either in the processing or modulation of pain. Here we show the cmVLM of the rat receives projections from superficial laminae of the medullary dorsal horn (MDH) and has neurons activated with capsaicin injections into the temporalis muscle. Injections of either biotinylated dextran amine (BDA) into the MDH or fluorogold (FG)/fluorescent microbeads into the cmVLM showed projections from lamina I and II of the MDH to the cmVLM. Morphometric analysis showed the retrogradely-labeled neurons were small (area 88.7 µm2±3.4) and mostly fusiform in shape. Injections (20–50 µl) of 0.5% capsaicin into the temporalis muscle and subsequent immunohistochemistry for c-Fos showed nuclei labeled in the dorsomedial trigeminocervical complex (TCC), the cmVLM, the lateral medulla, and the internal lateral subnucleus of the parabrachial complex (PBil). Additional labeling with c-Fos was seen in the subnucleus interpolaris of the spinal trigeminal nucleus, the rostral ventrolateral medulla, the superior salivatory nucleus, the rostral ventromedial medulla, and the A1, A5, A7 and subcoeruleus catecholamine areas. Injections of FG into the PBil produced robust label in the lateral medulla and cmVLM while injections of BDA into the lateral medulla showed projections to the PBil. Immunohistochemical experiments to antibodies against substance P, the substance P receptor (NK1), calcitonin gene regulating peptide, leucine enkephalin, VRL1 (TPRV2) receptors and neuropeptide Y showed that these peptides/receptors densely stained the cmVLM. We suggest the MDH- cmVLM projection is important for pain from head and neck areas. We offer a potential new pathway for regulating deep pain via the neurons of the TCC, the cmVLM, the lateral medulla, and the PBil and propose these areas compose a trigeminoreticular pathway, possibly the trigeminal homologue of the spinoreticulothalamic pathway