Search for: All records

Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency. However, hundreds of antennas require large volumes of pilot overhead to guarantee reliable channel estimation in FDD massive MIMO system. Compressive sensing (CS) has been applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel but suffer from high complexity. To overcome this challenge, this paper develops a hybrid channel estimation scheme by integrating the model-driven CS and data-driven deep unrolling technique. The proposed scheme consists of a coarse estimation part and a fine correction part to respectively exploit the inter- and intraframe sparsities of channels to greatly reduce the pilot overhead. Theoretical result is provided to indicate the convergence of the fine correction and coarse estimation net. Simulation results are provided to verify that our scheme can estimate MIMO channels with low pilot overhead while guaranteeing estimation accuracy with relatively low complexity. 

The field of spintronics has seen a surge of interest in altermagnetism due to novel predictions and many possible applications. MnTe is a leading altermagnetic candidate that is of significant interest across spintronics due to its layered antiferromagnetic structure, high Neel temperature (T_N ≈ 310 K) and semiconducting properties. The results on molecular beam epitaxy (MBE) grown MnTe/InP(111) films are presented. Here, it is found that the electronic and magnetic properties are driven by the natural stoichiometry of MnTe. Electronic transport and in situ angle‐resolved photoemission spectroscopy show the films are natively metallic with the Fermi level in the valence band and the band structure is in good agreement with first‐principles calculations for altermagnetic spin‐splitting. Neutron diffraction confirms that the film is antiferromagnetic with planar anisotropy and polarized neutron reflectometry indicates weak ferromagnetism, which is linked to a slight Mn‐richness that is intrinsic to the MBE‐grown samples. When combined with the anomalous Hall effect, this work shows that the electronic response is strongly affected by the ferromagnetic moment. Altogether, this highlights potential mechanisms for controlling altermagnetic ordering for diverse spintronic applications.

 

Testing database-backed web applications is chal- lenging because their behaviors (e.g., control flow) are highly dependent on data returned from SQL queries. Without a database containing sufficient and realistic data, it is challenging to reach potentially vulnerable code snippets, limiting various existing dynamic-based security testing approaches. However, obtaining such a database for testing is difficult in practice as it often contains sensitive information. Sharing it can lead to data leaks and privacy issues. In this paper, we present SYNTHDB, a program analysis- based database generation technique for database-backed PHP applications. SYNTHDB leverages a concolic execution engine to identify interactions between PHP codebase and the SQL queries. It then collects and solves various constraints to reconstruct a database that can enable exploring uncovered program paths without violating database integrity. Our evaluation results show that the database generated by SYNTHDB outperforms state-of- the-arts database generation techniques in terms of code and query coverage in 17 real-world PHP applications. Specifically, SYNTHDB generated databases achieve 62.9% code and 77.1% query coverages, which are 14.0% and 24.2% more in code and query coverages than the state-of-the-art techniques. Fur- thermore, our security analysis results show that SYNTHDB effectively aids existing security testing tools: Burp Suite, Wfuzz, and webFuzz. Burp Suite aided by SYNTHDB detects 76.8% of vulnerabilities while other existing techniques cover 55.7% or fewer. Impressively, with SYNTHDB, Burp Suite discovers 33 pre- viously unknown vulnerabilities from 5 real-world applications. 

Structural Analysis is an introductory course for structural engineering, which is taught in every undergraduate civil engineering program at about 300 institutions in the U.S., and also in most architectural and construction programs, as a core and required course. Despite its critical role in the curriculum, most novice learners in this course do not appear to have a sound understanding of fundamental concepts, such as load effects and load path; and in general, they lack the ability to visualize the deformed shape of simple structures, a necessary skill to conceptualize structural behavior beyond theoretical formulas and methods. In this paper, we aim to identify the design characteristics of an effective pedagogy involving AR to teach structural analysis. Adopting a design-based research approach, the paper describes the iterative research process that does not just evaluate the pedagogical applications involving AR, but systematically attempts to refine this intervention; and produce design principles that can guide similar research and development efforts. The cycle of research includes (a) analysis of practical problems by researchers and practitioners in collaboration; (b) development of solutions informed by existing design principles and technological innovations; (c) iterative cycles of testing and refinement of solutions in practice; and (d) reflection to produce design principles and enhance solution implementation. Findings from the evaluation and testing of the AR environment are included in the conclusions. 

In this paper, we show that the process of continually learning new tasks and memorizing previous tasks introduces unknown privacy risks and challenges to bound the privacy loss. Based upon this, we introduce a formal definition of Lifelong DP, in which the participation of any data tuples in the training set of any tasks is protected, under a consistently bounded DP protection, given a growing stream of tasks. A consistently bounded DP means having only one fixed value of the DP privacy budget, regardless of the number of tasks. To preserve Lifelong DP, we propose a scalable and heterogeneous algorithm, called L2DP-ML with a streaming batch training, to efficiently train and continue releasing new versions of an L2M model, given the heterogeneity in terms of data sizes and the training order of tasks, without affecting DP protection of the private training set. An end-to-end theoretical analysis and thorough evaluations show that our mechanism is significantly better than baseline approaches in preserving Lifelong DP. The implementation of L2DP-ML is available at: https://github.com/haiphanNJIT/PrivateDeepLearning.