257 research outputs found
Robust Multiple Testing under High-dimensional Dynamic Factor Model
Large-scale multiple testing under static factor models is commonly used to
select skilled funds in financial market. However, static factor models are
arguably too stringent as it ignores the serial correlation, which severely
distorts error rate control in large-scale inference. In this manuscript, we
propose a new multiple testing procedure under dynamic factor models that is
robust against both heavy-tailed distributions and the serial dependence. The
idea is to integrate a new sample-splitting strategy based on chronological
order and a two-pass Fama-Macbeth regression to form a series of statistics
with marginal symmetry properties and then to utilize the symmetry properties
to obtain a data-driven threshold. We show that our procedure is able to
control the false discovery rate (FDR) asymptotically under high-dimensional
dynamic factor models. As a byproduct that is of independent interest, we
establish a new exponential-type deviation inequality for the sum of random
variables on a variety of functionals of linear and non-linear processes.
Numerical results including a case study on hedge fund selection demonstrate
the advantage of the proposed method over several state-of-the-art methods.Comment: 29 pages, 4 table
A General Pipeline for 3D Detection of Vehicles
Autonomous driving requires 3D perception of vehicles and other objects in
the in environment. Much of the current methods support 2D vehicle detection.
This paper proposes a flexible pipeline to adopt any 2D detection network and
fuse it with a 3D point cloud to generate 3D information with minimum changes
of the 2D detection networks. To identify the 3D box, an effective model
fitting algorithm is developed based on generalised car models and score maps.
A two-stage convolutional neural network (CNN) is proposed to refine the
detected 3D box. This pipeline is tested on the KITTI dataset using two
different 2D detection networks. The 3D detection results based on these two
networks are similar, demonstrating the flexibility of the proposed pipeline.
The results rank second among the 3D detection algorithms, indicating its
competencies in 3D detection.Comment: Accepted at ICRA 201
Analysis of two pheromone-responsive conjugative multiresistance plasmids carrying the novel mobile optrA locus from Enterococcus faecalis
Background: The acquired optrA gene, which encodes a ribosomal protection protein of the ABC-F family, can confer cross-resistance to linezolid and florfenicol, posing a serious therapeutic challenge to both human and veterinary medicine.
Purpose: The objective of this study was to investigate the two Enterococcus faecalis (E. faecalis) plasmids for their fine structure, their transferability and the presence of mobile antimicrobial resistance loci.
Methods: To elucidate their fine structure, the two plasmids were completely sequenced and the sequences analysed. Besides conjugation experiments, inverse PCR assays were conducted to see whether minicircles are produced from the mobile antimicrobial resistance loci.
Results: Two pheromone-responsive conjugative optrA-carrying plasmids from E. faecalis, pE211 and pE508 were identified, which can transfer with frequencies of 2.6 ×10−2 and 3.7 ×10−2 (transconjugant per donor), respectively. In both plasmids, optrA was located on the novel mobile optrA locus with different sizes (12,834 bp in pE211 and 7,561 bp in pE508, respectively), flanked by two copies of IS1216 genes in the same orientation. Inverse PCR revealed that circular forms can be generated, consisting of optrA and one copy of IS1216, indicating they are all active. The 77,562 bp plasmid pE211 also carried Tn558 and a mobile bcrABDR locus, and the 84,468 bp plasmid pE508 also harbored the genes fexA, tet(L), tet(O/W/32/O) and a mobile aac(A)-aph(D) locus.
Conclusion: The presence of mobile genetic elements in these plasmids renders them flexible and these elements will aid to the persistence and dissemination of these plasmids among enterococci and potentially also other gram-positive bacteria
Dynamic Non-Rigid Objects Reconstruction with a Single RGB-D Sensor
This paper deals with the 3D reconstruction problem for dynamic non-rigid objects with a single RGB-D sensor. It is a challenging task as we consider the almost inevitable accumulation error issue in some previous sequential fusion methods and also the possible failure of surface tracking in a long sequence. Therefore, we propose a global non-rigid registration framework and tackle the drifting problem via an explicit loop closure. Our novel scheme starts with a fusion step to get multiple partial scans from the input sequence, followed by a pairwise non-rigid registration and loop detection step to obtain correspondences between neighboring partial pieces and those pieces that form a loop. Then, we perform a global registration procedure to align all those pieces together into a consistent canonical space as guided by those matches that we have established. Finally, our proposed model-update step helps fixing potential misalignments that still exist after the global registration. Both geometric and appearance constraints are enforced during our alignment; therefore, we are able to get the recovered model with accurate geometry as well as high fidelity color maps for the mesh. Experiments on both synthetic and various real datasets have demonstrated the capability of our approach to reconstruct complete and watertight deformable objects
The hidden spin-momentum locking and topological defects in unpolarized light fields
Electromagnetic waves characterized by intensity, phase, and polarization
degrees of freedom are widely applied in data storage, encryption, and
communications. However, these properties can be substantially affected by
phase disorders and disturbances, whereas high-dimensional degrees of freedom
including momentum and angular momentum of electromagnetic waves can offer new
insights into their features and phenomena, for example topological
characteristics and structures that are robust to these disturbances. Here, we
discover and demonstrate theoretically and experimentally spin-momentum locking
and topological defects in unpolarized light. The coherent spin is locked to
the kinetic momentum except for a small coupling spin term, due to the
simultaneous presence of transverse magnetic and electric components in
unpolarized light. To cancel the coupling term, we employ a metal film acting
as a polarizer to form some skyrmion-like spin textures at the metal/air
interface. Using an in-house scanning optical microscopic system to image the
out-of-plane spin density of the focused unpolarized vortex light, we obtained
experimental results that coincide well with our theoretical predictions. The
theory and technique promote the applications of topological defects in optical
data storage, encryption, and decryption, and communications.Comment: 9 pages, 3 figures, 47 reference
Rapid determination of trace Cu 2+ by an in-syringe membrane SPE and membrane solid-phase spectral technique
A new in-syringe membrane SPE and solid-phase visible spectral method was proposed for the rapid extraction and visible spectral determination of trace Cu2+. The chelation and membrane SPE can be accomplished in a syringe. The yellow Cu(DDTC)2 complex was separated using a polyethersulfone membrane from the sample solution. Then, the complex can be detected directly on the polyethersulfone membrane utilizing solid-phase visible absorbance spectra without elution. The proposed method simplified the experimental procedure and improved the sensitivity to the μg L-1 level. Furthermore, this method is environmentally friendly since it avoids the use of organic solvents. After the investigation of the influence of different variables on the membrane SPE procedure, water and blood plasma were analyzed to validate the proposed method. A LOD of 0.04 μg L-1 and recoveries of 96.0-103.7% confirmed that the present work can be applied for the determination of trace Cu2+ in water and blood plasma samples
Transmission infrared micro-spectroscopic study of individual human hair
Understanding the optical transmission property of human hair, especially in
the infrared regime, is vital in physical, clinical, and biomedical research.
However, the majority of infrared spectroscopy on human hair is performed in
the reflection mode, which only probes the absorptance of the surface layer.
The direct transmission spectrum of individual hair without horizontal cut
offers a rapid and non-destructive test of the hair cortex but is less
investigated experimentally due to the small size and strong absorption of the
hair. In this work, we conduct transmission infrared micro-spectroscopic study
on individual human hair. By utilizing direct measurements of the transmission
spectrum using a Fourier-transform infrared microscope, the human hair is found
to display prominent band filtering behavior. The high spatial resolution of
infrared micro-spectroscopy further allows the comparison among different
regions of hair. In a case study of adult-onset Still's disease, the
corresponding infrared transmission exhibits systematic variations of spectral
weight as the disease evolves. The geometry effect of the internal hair
structure is further quantified using the finite-element simulation. The
results imply that the variation of spectral weight may relate to the
disordered microscopic structure variation of the hair cortex during the
inflammatory attack. Our work reveals the potential of hair infrared
transmission spectrum in tracing the variation of hair cortex retrospectively
TNANet: A Temporal-Noise-Aware Neural Network for Suicidal Ideation Prediction with Noisy Physiological Data
The robust generalization of deep learning models in the presence of inherent
noise remains a significant challenge, especially when labels are subjective
and noise is indiscernible in natural settings. This problem is particularly
pronounced in many practical applications. In this paper, we address a special
and important scenario of monitoring suicidal ideation, where time-series data,
such as photoplethysmography (PPG), is susceptible to such noise. Current
methods predominantly focus on image and text data or address artificially
introduced noise, neglecting the complexities of natural noise in time-series
analysis. To tackle this, we introduce a novel neural network model tailored
for analyzing noisy physiological time-series data, named TNANet, which merges
advanced encoding techniques with confidence learning, enhancing prediction
accuracy. Another contribution of our work is the collection of a specialized
dataset of PPG signals derived from real-world environments for suicidal
ideation prediction. Employing this dataset, our TNANet achieves the prediction
accuracy of 63.33% in a binary classification task, outperforming
state-of-the-art models. Furthermore, comprehensive evaluations were conducted
on three other well-known public datasets with artificially introduced noise to
rigorously test the TNANet's capabilities. These tests consistently
demonstrated TNANet's superior performance by achieving an accuracy improvement
of more than 10% compared to baseline methods
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