138 research outputs found

    Regulating Space Debris as Separate from Space Objects

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    Synthesis and Characterization of barium titanate-cobalt ferrite composite

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    Barium titanate –cobalt ferrite composite has been prepared by mixing of cobalt ferrite obtained by co-precipitation method and barium titanate synthesized by solid route. Phase formation behavior of the sample has been studied from the XRD pattern of the sintered sample. Microstructure of the sintered sample has been studied by using scanning electron microscopy. Magnetoelectric voltage co-efficient of different composition of composite has also been studie

    Dark Z' Search and (Un)Natural Supersymmetric Models

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    The Standard Model (SM) of particle physics is the most successful theory of particles and fundamental laws of nature. It has been tested numerous times for over forty years. Yet, it is considered to be incomplete. It does not incorporate gravity and does not explain dark matter or dark energy. The parameters in the SM are ad-hoc in nature and the Higgs mass is unstable to quantum corrections. The SM covers only ~ 5% of the energy-matter content of the cosmos. Dark matter (DM) has been indirectly observed via its gravitational effects on ordinary matter. Currently, there are no acceptable results from terrestrial experiments that can explain the particle properties of the DM. We consider several production models for a dark gauge boson, Z' that mediates a dark force. We find that by introducing new cuts, we can optimize dilepton resonance and MET searches at the Large Hadron Collider that can efficiently look for the Z' of mass of O(100 GeV). Supersymmetry (SUSY) is the most widely used framework for beyond the SM framework. It provides a good DM candidate and can achieve better gauge coupling unfication compared to the SM. For it to solve the hierarchy problem in a natural way, SUSY is expected to show itself at a scale of a few hundred GeVs. We present a model that combines two different SUSY breaking mechanisms allowing gaugino masses of O(TeV) and yet preserving naturalness in the theory. For this purpose, we introduce messenger fields resulting in a compressed gaugino spectrum. This more compressed spectrum is less constrained by LHC searches and allows for lighter gluinos. In addition to the model, we present gaugino pole mass equations that differ from (and correct) the original literature. We also consider the case where SUSY is not associated with the weak scale and solves the hierarchy problem by fine-tuning while retaining its other appealing features. A Mini-Split SUSY model is presented with SUSY scalars, msc in the mass range of 100 - 1000 TeV. Higgsino masses, if not at the Planck scale, should generically appear at the same scale. The gaugino mass contributions from anomaly mediation, with the heavy Higgsino threshold, generally leads to a more compressed spectrum than standard anomaly mediation, while the presence of extra vector-like matter near msc typically leads to an even more compressed spectrum. Heavy Higgsinos improve gauge coupling unification relative to the MSSM. This model achieves the experimentally observed mass of Higgs and has a DM candidate

    In Search of netUnicorn: A Data-Collection Platform to Develop Generalizable ML Models for Network Security Problems

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    The remarkable success of the use of machine learning-based solutions for network security problems has been impeded by the developed ML models' inability to maintain efficacy when used in different network environments exhibiting different network behaviors. This issue is commonly referred to as the generalizability problem of ML models. The community has recognized the critical role that training datasets play in this context and has developed various techniques to improve dataset curation to overcome this problem. Unfortunately, these methods are generally ill-suited or even counterproductive in the network security domain, where they often result in unrealistic or poor-quality datasets. To address this issue, we propose an augmented ML pipeline that leverages explainable ML tools to guide the network data collection in an iterative fashion. To ensure the data's realism and quality, we require that the new datasets should be endogenously collected in this iterative process, thus advocating for a gradual removal of data-related problems to improve model generalizability. To realize this capability, we develop a data-collection platform, netUnicorn, that takes inspiration from the classic "hourglass" model and is implemented as its "thin waist" to simplify data collection for different learning problems from diverse network environments. The proposed system decouples data-collection intents from the deployment mechanisms and disaggregates these high-level intents into smaller reusable, self-contained tasks. We demonstrate how netUnicorn simplifies collecting data for different learning problems from multiple network environments and how the proposed iterative data collection improves a model's generalizability

    Health and Financial Fragility: Evidence from Car Crashes and Consumer Bankruptcy

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    This paper assesses the importance of adverse health shocks as triggers of bankruptcy filings. We view car crashes as a proxy for health shocks and draw on a large sample of police crash reports linked to hospital admission records and bankruptcy case files. We report two findings: (i) there is a strong positive correlation between an individual\u27s pre-shock financial condition and his or her likelihood of suffering a health shock, an example of behavioral consistency; and (ii) after accounting for this simultaneity, we are unable to identify a causal effect of health shocks on bankruptcy filing rates. These findings emphasize the importance of risk heterogeneity in determining financial fragility, raise questions about prior studies of medical bankruptcy, and point to important challenges in identifying the triggers of consumer bankruptcy. JEL Codes: D12, D14, K35
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