Miracle-dz/UniFAR-FTUs huggingface dataset:
An accelerated sequential algorithm for producing D -optimal designs

An extremely efficient algorithm is presented for generating D-optimal designs. It is shown that the underlying set function of the sequential design algorithm is submodular and thus the so-called accelerated greedy algorithm may be applied to this problem. While the new algorithm's statistical basis is identical to that of the Wynn and Federov algorithm, it requires significantly fewer function evaluations. The new algorithm is particularly useful when no prior information concerning the structure of the optimal design is available.
The Identification and Driving Factor Analysis of Ecological-Economi Spatial Conflict in Nanjing Metropolitan Area Based on Remote Sensing Data

The rapid socio-economic development of the metropolitan area has led to the continuous deterioration of the ecological environment. This leads to intense competition and conflict between different spatial use types. Spatial conflict research is essential to achieve ecological-economic coordination and high-quality development. However, existing studies lack comprehensive and direct ecological-economic spatial conflicts, especially those on the spatial-temporal evolution and potential drivers of spatial conflict. In this study, we identified the ecological-economic spatial conflicts in the Nanjing metropolitan area in 2010, 2015, and 2020. This study used the random forest to analyze the factors that influenced the change of spatial conflict. Results show that: (1) From 2010 to 2020, the ecological-economic spatial conflict in the Nanjing metropolitan area changed significantly. (2) Land use change has an important effect on spatial conflicts, which are easily triggered by uncontrolled urban expansion, but ecological land can mitigate spatial conflicts. (3) Relevant driving factors of spatial conflicts show multi-level features, so the development of conflict reconciliation countermeasures needs to be tailored to local conditions. This study provides a significant foundation for the high-quality development of the Nanjing metropolitan area and provides a reference for the planning and management of the territorial space.
Organizational resilience under COVID-19: the role of digital technology in R&D investment and performance

PurposeThis study aims at the sudden outbreak of COVID-19, which had an unprecedented negative impact on the Chinese economy, with firms being affected most. Firms differ in terms of their specific internal environment, shaping their ability to respond to the outbreak, so the impact may also vary.Design/methodology/approachIn this paper Chinese listed firms are selected as samples to investigate the mediating effect of prior digital technology on the relationship between R&D (research and development) investment (funds and staff) and firm performance during the epidemic. Firm size and diversification are then introduced as moderating variables to explore the conditional mediating effect of digital technology.FindingsThe results indicate that the higher the firm's prior R&D investment, the higher its digital technology level, and thus the stronger its resistance to the epidemic. Moreover, compared with large-scale firms, small-scale firms have the advantage of strategic flexibility to technological changes, which can help them accumulate experience from R&D activities for digital transformation, thus attenuating the negative impact of the COVID-19 on firm performance. Finally, the results also show that digital technology mediates more strongly between R&D investment and firm performance in diversified firms than in centralized firms.Originality/valueThe study builds a mediation model to reveal the process mechanism through which R&D investment affects firm performance via digital technology. Firm size and diversification are then innovatively introduced as situational factors to build the moderated mediation model, which opens up a new perspective for understanding the effect of firm internal factors on the relationship between R&D investment, digital transformation and firm performance.
Direct Inference of Base-Pairing Probabilities with Neural Networks Improves Prediction of RNA Secondary Structures with Pseudoknots

Existing approaches to predicting RNA secondary structures depend on how the secondary structure is decomposed into substructures, that is, the architecture, to define their parameter space. However, architecture dependency has not been sufficiently investigated, especially for pseudoknotted secondary structures. In this study, we propose a novel algorithm for directly inferring base-pairing probabilities with neural networks that do not depend on the architecture of RNA secondary structures, and then implement this approach using two maximum expected accuracy (MEA)-based decoding algorithms: Nussinov-style decoding for pseudoknot-free structures and IPknot-style decoding for pseudoknotted structures. To train the neural networks connected to each base pair, we adopt a max-margin framework, called structured support vector machines (SSVM), as the output layer. Our benchmarks for predicting RNA secondary structures with and without pseudoknots show that our algorithm outperforms existing methods in prediction accuracy.
Millimeter-Wave Wideband Wide-Angle Scanning Proactive Conformal Phased Array Antenna

Though conformal phased array antennas have better wide-angle scanning capability than their traditional planar counterparts do, their fabrication is generally complex. Therefore, this letter presents a millimeter-wave (mmWave) proactive conformal (PC) phased array antenna (PAA) formed by high-low alternate elements/subarrays to proactively modulate the array aperture, achieving wide-angle scanning and easy fabrication. A 1×6-element endfire high-gain low sidelobe level subarray is designed, based on which a 4×6-element PC PAA is implemented. The measured results show that the reflection coefficient of the fabricated prototype is mostly <−10 dB, the port isolation is >18 dB, the endfire scanning gain is >15.5 dBi, and the 3-dB gain variation scanning range is >±60° with a peak up to ±68° within 24.25–29.5 GHz (19.5%).
A Theory of Momentary Distress Tolerance: Toward Understanding Contextually Situated Choices to Engage With or Avoid Distress

As a transdiagnostic vulnerability factor, low distress tolerance predicts a wide variety of psychopathology. The current article extends past research, which has typically adopted an individual-differences approach, by articulating a social-cognitive theory of momentary distress tolerance. This new model intentionally separates actions taken to remain engaged with distress (i.e., tolerance) from avoidance/escape actions associated with intolerance, and highlights willingness to experience distress as central to tolerance. In addition, self-efficacy for tolerating distress is highlighted as a central predictor of the tolerance/intolerance process, influenced by distress intensity and momentary psychological resources (e.g., hunger, tiredness, degree of life stressors, social support). Finally, the model also explains how repeated experiences of distress tolerance and/or intolerance coalesce into a global self-perception indicative of individual differences in distress tolerance. Reasons why the proposed model has the potential to overcome current challenges in distress tolerance research are reviewed, and future directions for both research and clinical practice are highlighted.
Cycle Spaces of Digraphs

The cycle space of a graph corresponds to the kernel of an incidence matrix. We investigate an analogous subspace for digraphs. In the case of digraphs of graphs, where every edge is replaced by two oppositely directed arcs, we give a combinatorial description of a basis of such a space. We are motivated by a connection to the transition matrices of discrete-time quantum walks.
The spectral determination of the multicone graphs Kw+P

A multicone graph is obtained from the join of a clique and a regular graph. Let P and Kw denote the Petersen graph and a complete graph on w vertices, respectively. In this paper, we show that multicone graphs Kw ▽ P are determined by their signless Laplacian spectra, their Laplacian spectra, their complement with respect to signless Laplacian spectra, and their adjacency spectra.
Visualizing Cathepsin K‐Cre Expression at the Single‐Cell Level with GFP Reporters

The Cre/lox system is a fundamental tool for functional genomic studies, and a number of Cre lines have been generated to target genes of interest spatially and temporally in defined cells or tissues; this approach has greatly expanded our knowledge of gene functions. However, the limitations of this system have recently been recognized, and we must address the challenge of so‐called nonspecific/off‐target effects when a Cre line is utilized to investigate a gene of interest. For example, cathepsin K (Ctsk) has been used as a specific osteoclast marker, and Cre driven by its promoter is widely utilized for osteoclast investigations. However, Ctsk‐Cre expression has recently been identified in other cell types, such as osteocytes, periosteal stem cells, and tenocytes. To better understand Ctsk‐Cre expression and ensure appropriate use of this Cre line, we performed a comprehensive analysis of Ctsk‐Cre expression at the single‐cell level in major organs and tissues using two green fluorescent protein (GFP) reporters (ROSA nT‐nG and ROSA tdT) and a tissue clearing technique in young and aging mice. The expression profile was further verified by immunofluorescence staining and droplet digital RT‐PCR. The results demonstrate that Ctsk‐Cre is expressed not only in osteoclasts but also at various levels in osteoblast lineage cells and other major organs/tissues, particularly in the brain, kidney, pancreas, and blood vessels. Furthermore, Ctsk‐Cre expression increases markedly in the bone marrow, skeletal muscle, and intervertebral discs in aging mice. These data will be valuable for accurately interpreting data obtained from in vivo studies using Ctsk‐Cre mice to avoid potentially misleading conclusions. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
A note on non-unique enhancements

The first author was a Postdoctoral Research Fellow at the University of Edinburgh. She was supported by EPSRC grant EP/N021649/1. The second author was a senior researcher at the Research Foundation - Flanders (FWO). He was supported by the FWO-grant G0D8616N "Hochschild cohomology and deformation theory of triangulated categories".
How many people have to die over a type II error?

Although much concern over type I errors has permeated psychology for decades, there is less concern over type II errors. In fact, type II errors constitute a serious problem in safety research that can result in accidents and fatalities because researchers fail to reject the null hypothesis due to arbitrary probability thresholds. The purpose of this paper is to reveal how often type II errors occur and the effect they have on applied ergonomics research. Computer simulations using population parameters were generated, revealing that type II errors happen quite often, particularly with effect sizes between 0.2 and 1.2. A utility analysis also reveals that the cost of type II errors on society is much greater than it needs to be. Solutions for avoiding type II errors are discussed.
MM-LMF: A Low-Rank Multimodal Fusion Dangerous Driving Behavior Recognition Method Based on FMCW Signals

Multimodal research is an emerging field of artificial intelligence, and the analysis of dangerous driving behavior is one of the main application scenarios in the field of multimodal fusion. Aiming at the problem of data heterogeneity in the process of behavior classification by multimodal fusion, this paper proposes a low-rank multimodal data fusion method, which utilizes the complementarity between data modalities of different dimensions in order to classify and identify dangerous driving behaviors. This method uses tensor difference matrix data to force low-rank fusion representation, improves the verification efficiency of dangerous driving behaviors through multi-level abstract tensor representation, and solves the problem of output data complexity. A recurrent network based on the attention mechanism, AR-GRU, updates the network input parameter state and learns the weight parameters through its gated structure. This model improves the dynamic connection between modalities on heterogeneous threads and reduces computational complexity. Under low-rank conditions, it can quickly and accurately classify and identify dangerous driving behaviors and give early warnings. Through a large number of experiments, the accuracy of this method is improved by an average of 1.76% compared with the BiLSTM method and the BiGRU-IAAN method in the training and verification of the self-built dataset.
Studies on the Green-Griffiths-Lang Conjecture

In this short note we explain some generalizations of the works by J. P. Demailly, J. Merker, Y. T. Siu on Green-Griffiths-Lang conjecture for the entire curve locus. We try to apply the Holomorphic Morse inequailties for the invariant jet differentials. Also we apply the similar argument to examine a generalization of a result of J. Merker and Y. T. Siu.
Compact 5G Nonuniform Transmission Line Interdigital Bandpass Filter for 5G/UWB Reconfigurable Antenna

In this study, at two different fifth generation (5G) low-frequency bands (3.7–4.2 GHz and 5.975–7.125 GHz) and based on nonuniform transmission lines (NTLs) theory, a compact three-quarter-wave resonators interdigital bandpass filter (IBPF) is analyzed, designed, and fabricated. The compact proposed filter is considered as a good candidate for reconfigurable 5G low-frequency bands and ultrawide band (UWB) antenna, which will reduce the size of the final RF communication system. Firstly, a uniform transmission line (UTL) IBPF at these two bands is designed and tested; then the NTL concept is applied for compactness. For both UTL and NTL IBPFs, different parametric studies are performed for optimization. At the first frequency band, size reductions of 16.88% and 16.83% are achieved in the first (symmetrical to the third resonator) and second λ/4 resonator of UTL IBPF, respectively, with up to 36.6% reduction in the total area. However, 16.46% and 16.33% size reductions are obtained in the first (symmetrical to the third resonator) and second λ/4 resonator, respectively, at the second frequency band with a 40.53% reduction in the whole circuit area. The performance of the proposed NTL IBPF is compared with the UTL IBPF. The measured reflection coefficient of the proposed NTL IBPF, S11, appears to be less than −10.53 dB and −11.27 dB through 3.7–4.25 GHz and 5.94–7.67 GHz, respectively. However, the transmission coefficient, S12 is around −0.86 dB and–1.7 dB at the center frequencies, fc = 3.98 GHz and 6.81 GHz, respectively. In this study, simulations are carried out using high-frequency structure simulator (HFSS) software based on the finite element method (FEM). The validity of the proposed theoretical schematic of this filter is proved by design simulations and measured results of its prototype.
Analysis of a Diffusion LMS Algorithm With Communication Delays for Cyclostationary White Gaussian Inputs

Available analyses of the diffusion LMS (DLMS) algorithm assume that the nodes probe the unknown system with zero delay. This assumption is unrealistic, since the unknown system is usually distant from the nodes. The present paper studies the behavior of the algorithm without this assumption. The analysis is done for a network having a central combiner. This structure reduces the dimensionality of the resulting stochastic models while preserving important diffusion properties. Communication delays between the nodes and the central combiner are also considered in the analysis. The analysis is done for system identification for cyclostationary white Gaussian nodal inputs. Mean and mean-square behaviors of the algorithm are analyzed. It is found that delays in probing the unknown system yield a bias in the algorithm without increasing its convergence time. The communication delays between the nodes and the central combiner increase the convergence time without affecting the steady-state behavior. The stability of the algorithm is not affected by either type of delay. The analysis exactly matches the simulations.
A Machine Learning Empowered Shape Memory Alloy Gripper With Displacement-Force-Stiffness Self-Sensing

This article proposes a multiple self-sensing gripper (MssGripper) driven by the shape memory alloy (SMA) and empowered by machine learning algorithms. The MssGripper can identify objects without external sensors. A single SMA wire can drive the gripper for self-sensing accurately. This article confirms the resistance of the SMA can reflect the phase transition and can be used for displacement and force prediction, as well as for object stiffness prediction. Through machine learning, a backpropagation neural network and long-short-term-memory (LSTM) are used to establish multiple self-sensing models for prediction. The robustness experimental results show that the self-sensing models based on LSTM have higher prediction accuracy. The average root-mean-square errors of displacement prediction and force prediction are 0.063 mm and 0.236 N, respectively, and the stiffness prediction error is less than 9.4%. Moreover, the accuracy of the classifier in stiffness identification is 97.2%. The MssGripper can accurately predict the displacement, force, and stiffness and identify objects such as springs, rubber bars and steel bars. The establishment of the models expands the novel idea of gripper sensing, which is beneficial to promoting miniaturization and compactness.
A Receding Horizon Trajectory Tracking Strategy for Input-Constrained Differential-Drive Robots via Feedback Linearization

This brief proposes a novel solution to the trajectory tracking control problem for input-constrained differential-drive robots. In particular, we develop a robust set-based receding horizon tracking scheme capable of dealing with state-dependent input constraints arising when the vehicle’s dynamics are approached by a standard feedback linearization (FL) technique. First, offline, we characterize the worst case input constraint set and compute an admissible, although not optimal, controller. Then, online, we leverage the knowledge of the robot’s orientation to enlarge the constraint set in a receding horizon fashion and, consequently, improve the tracking performance. Recursive feasibility and constraints’ fulfillment are formally proven. The approach’s effectiveness is experimentally validated on a Khepera IV differential-drive robot by comparing the control performance with several competitor schemes.
Using ensemble learning and genetic algorithm on magnetic resonance imaging radiomics to classify molecular subtypes of breast cancer

Breast cancer (BRCA) is one of the most frequent malignant tumors with the highest incidence of cancer and the second most common oncologic cause of death in women. BRCA can be classified into different molecular subtypes, such as basal‐like, represented by triple‐negative BRCA (estrogen receptor [ER] negative, progesterone receptor [PR] negative, and human epidermal growth factor receptor 2 [HER‐2] negative). This study aims to determine whether radiomics features extracted from magnetic resonance imaging (MRI) could be used to distinguish various BRCA molecular subtypes. This study retrospectively collected a dataset of 922 BRCA patients with MRIs and experimental genomic profiles. A genetic algorithm is then employed to select the optimal MRI features for each subproblem. Subsequently, stacking ensemble learning is implemented to learn these features and generate the prediction outcomes. Our model showed a significant performance of 0.700, 0.732, and 0.642 (area under the curve; AUC) in predicting ER, PR, and HER‐2 statuses. For multiclassification of Luminal A, Luminal B, HER2, and TNBC, the AUCs reached 0.672, 0.624, 0.639, and 0.669, respectively. Our model is superior in most subtypes compared to the state‐of‐the‐art predictors on the same dataset. In conclusion, genetic algorithm and ensemble learning can be suitable for BRCA subtype classification with high performance.
Mapping Surficial Radium Content as a Partial Indicator of Radon Concentrations in US Houses

In connection with the problem of indoor radon, we discuss the use of a database developed from the National Aerial Radiometric Reconnaissance in the development of a map of radium in soil for the contiguous 48 states. We examine the relationship between the results of measurements of radon in houses and the indications of the US map, noting that some, but by no means all, of the areas known to have elevated radon concentrations appear as areas of higher radium concentration than their surroundings and that there are other areas, in which measurements of high radon levels have not been made, which are suggested as deserving of interest. We discuss mapping techniques for smaller areas and possible methods of dealing with apparent discrepancies between adjacent areas. We show that, on a national basis, as much as half the variation in radon from region to region may be accounted for by the level of radium in the soil, but that there are regions for which the radium concentration does not account for the relatively high observed radon. 3 refs., 3 figs.
Discrete element method for simulation and calibration of cotton stalk contact parameters

To improve the accuracy of the discrete element research, physical and simulation experiments were used to calibrate the cotton stalk contact parameters. Based on the stalk-stalk and stalk-steel contact mechanics, the parameters were measured in physical experiments, and the discrete element simulation software was used to build the stalk model. In the simulation process, the Plackett-Burman experiment was used to screen three significant factors from six initial factors. The steepest Plackett-Burman experiment was used to determine the optimal interval of the significant factors. A second-order regression model of the significant factors and the angle of repose was established according to the Central Composite design experiment. The best parameter combination of the significant factors was then obtained: the coefficient of static friction on stalk-steel contact was 0.31, the coefficient of static friction on stalk-stalk contact was 0.62, and the coefficient of rolling friction on stalk-stalk contact was 0.02. The relative error between the physical angle of repose and the simulated angle was 3.27%, indicating that it is feasible to apply the simulation experiment instead of the physical one. It offers insights into cotton stalk contact parameter settings and film-stalk separation in the simulation.
On $H^2$-gradient Flows for the Willmore Energy

We show that the concept of $H^2$-gradient flow for the Willmore energy and other functionals that depend at most quadratically on the second fundamental form is well-defined in the space of immersions of Sobolev class $W^{2,p}$ from a compact, $n$-dimensional manifold into Euclidean space, provided that $p \geq 2$ and $p>n$. We also discuss why this is not true for Sobolev class $H^2=W^{2,2}$. In the case of equality constraints, we provide sufficient conditions for the existence of the projected $H^2$-gradient flow and demonstrate its usability for optimization with several numerical examples.