Testing for high-dimensional white noise

Testing for multi-dimensional white noise is an important subject in statistical inference. Such test in the high-dimensional case becomes an open problem waiting to be solved, especially when the dimension of a time series is comparable to or even greater than the sample size. To detect an arbitrary form of departure from high-dimensional white noise, a few tests have been developed. Some of these tests are based on max-type statistics, while others are based on sum-type ones. Despite the progress, an urgent issue awaits to be resolved: none of these tests is robust to the sparsity of the serial correlation structure. Motivated by this, we propose a Fisher's combination test by combining the max-type and the sum-type statistics, based on the established asymptotically independence between them. This combination test can achieve robustness to the sparsity of the serial correlation structure,and combine the advantages of the two types of tests. We demonstrate the advantages of the proposed test over some existing tests through extensive numerical results and an empirical analysis.Comment: 84 page

A versatile route to fabricate single atom catalysts with high chemoselectivity

Preparation of single atom catalysts (SACs) is of broad interest to materials scientists and chemists but remains a formidable challenge. Herein, we develop an efficient approach to synthesize SACs via a precursor-dilution strategy, in which metalloporphyrin (MTPP) with target metals are co-polymerized with diluents (tetraphenylporphyrin, TPP), followed by pyrolysis to N-doped porous carbon supported SACs (M1/N-C). Twenty-four different SACs, including noble metals and non-noble metals, are successfully prepared. In addition, the synthesis of a series of catalysts with different surface atom densities, bi-metallic sites, and metal aggregation states are achieved. This approach shows remarkable adjustability and generality, providing sufficient freedom to design catalysts at atomic-scale and explore the unique catalytic properties of SACs. As an example, we show that the prepared Pt1/N-C exhibits superior chemoselectivity and regioselectivity in hydrogenation. It only converts terminal alkynes to alkenes while keeping other reducible functional groups such as alkenyl, nitro group, and even internal alkyne intact

Tungsten Nanoparticles Accelerate Polysulfides Conversion: A Viable Route toward Stable Room-Temperature Sodium–Sulfur Batteries

Room-temperature sodium–sulfur (RT Na–S) batteries are arousing great interest in recent years. Their practical applications, however, are hindered by several intrinsic problems, such as the sluggish kinetic, shuttle effect, and the incomplete conversion of sodium polysulfides (NaPSs). Here a sulfur host material that is based on tungsten nanoparticles embedded in nitrogen-doped graphene is reported. The incorporation of tungsten nanoparticles significantly accelerates the polysulfides conversion (especially the reduction of Na2S4 to Na2S, which contributes to 75% of the full capacity) and completely suppresses the shuttle effect, en route to a fully reversible reaction of NaPSs. With a host weight ratio of only 9.1% (about 3–6 times lower than that in recent reports), the cathode shows unprecedented electrochemical performances even at high sulfur mass loadings. The experimental findings, which are corroborated by the first-principles calculations, highlight the so far unexplored role of tungsten nanoparticles in sulfur hosts, thus pointing to a viable route toward stable Na–S batteries at room temperatures

ATP-dependent dynamic protein aggregation regulates bacterial dormancy depth critical for antibiotic tolerance

Cell dormancy is a widespread mechanism used by bacteria to evade environmental threats including antibiotics. Here we monitored bacterial antibiotic tolerance and regrowth at the single-cell level and found that each individual survival cell shows different ‘dormancy depth’, which in return regulates the lag time for cell resuscitation after removal of antibiotic. We further established that protein aggresome - a collection of endogenous protein aggregates - is an important indicator of bacterial dormancy depth, whose formation is promoted by decreased cellular ATP level. For cells to leave the dormant state and resuscitate, clearance of protein aggresome and recovery of proteostasis are required. We revealed the ability to recruit functional DnaK-ClpB machineries, which facilitate protein disaggregation in an ATP-dependent manner, determines the lag time for bacterial regrowth. Better understanding of the key factors regulating bacterial regrowth after surviving antibiotic attack could lead to new therapeutic strategies for combating bacterial antibiotic tolerance

A one-sided refined symmetrized data aggregation approach to robust mutual fund selection

We consider the problem of identifying skilled funds among a large number of candidates under the linear factor pricing models containing both observable and latent market factors. Motivated by the existence of non-strong potential factors and diversity of error distribution types of the linear factor pricing models, we develop a distribution-free multiple testing procedure to solve this problem. The proposed procedure is established based on the statistical tool of symmetrized data aggregation, which makes it robust to the strength of potential factors and distribution type of the error terms. We then establish the asymptotic validity of the proposed procedure in terms of both the false discovery rate and true discovery proportion under some mild regularity conditions. Furthermore, we demonstrate the advantages of the proposed procedure over some existing methods through extensive Monte Carlo experiments. In an empirical application, we illustrate the practical utility of the proposed procedure in the context of selecting skilled funds, which clearly has much more satisfactory performance than its main competitors.</p

Randomly Distributed Passive Seismic Source Reconstruction Record Waveform Rectification Based on Deep Learning

In passive seismic exploration, the number and location of underground sources are very random, and there may be few passive sources or an uneven spatial distribution. The random distribution of seismic sources can cause the virtual shot recordings to produce artifacts and coherent noise. These artifacts and coherent noise interfere with the valid information in the virtual shot record, making the virtual shot record a poorer presentation of subsurface information. In this paper, we utilize the powerful learning and data processing abilities of convolutional neural networks to process virtual shot recordings of sources in undesirable situations. We add an adaptive attention mechanism to the network so that it can automatically lock the positions that need special attention and processing in the virtual shot records. After testing, the trained network can eliminate coherent noise and artifacts and restore real reflected waves. Protecting valid signals means restoring valid signals with waveform anomalies to a reasonable shape

Optical microfiber-loaded surface plasmonic TE-pass polarizer

We propose a novel optical microfiber-loaded plasmonic TE-pass polarizer consisting of an optical microfiber placed on top of a silver substrate and demonstrate its performance both numerically by using the finite element method (FEM) and experimentally. The simulation results show that the loss in the fundamental TE mode is relatively low while at the same time the fundamental TM mode suffers from a large metal dissipation loss induced by excitation of the microfiber-loaded surface plasmonic mode. The microfiber was fabricated using the standard microheater brushing–tapering technique. The measured extinction ratio over the range of the C-band wavelengths is greater than 20 dB for the polarizer with a microfiber diameter of 4 µm, which agrees well with the simulation results

ECMPride: prediction of human extracellular matrix proteins based on the ideal dataset using hybrid features with domain evidence

Extracellular matrix (ECM) proteins play an essential role in various biological processes in multicellular organisms, and their abnormal regulation can lead to many diseases. For large-scale ECM protein identification, especially through proteomic-based techniques, a theoretical reference database of ECM proteins is required. In this study, based on the experimentally verified ECM datasets and by the integration of protein domain features and a machine learning model, we developed ECMPride, a flexible and scalable tool for predicting ECM proteins. ECMPride achieved excellent performance in predicting ECM proteins, with appropriate balanced accuracy and sensitivity, and the performance of ECMPride was shown to be superior to the previously developed tool. A new theoretical dataset of human ECM components was also established by applying ECMPride to all human entries in the SwissProt database, containing a significant number of putative ECM proteins as well as the abundant biological annotations. This dataset might serve as a valuable reference resource for ECM protein identification