Miracle-dz/UniFAR-FTUs huggingface dataset:
Complex Emotion Profiling: An Incremental Active Learning Based Approach With Sparse Annotations

Generally, in-the-wild emotions are complex in nature. They often occur in combinations of multiple basic emotions, such as fear, happy, disgust, anger, sadness and surprise. Unlike the basic emotions, annotation of complex emotions, such as pain, is a time-consuming and expensive exercise. Moreover, there is an increasing demand for profiling such complex emotions as they are useful in many real-world application domains, such as medical, psychology, security and computer science. The traditional emotion recognition systems require a significant amount of annotated training samples to understand the complex emotions. This limits the direct applicability of those methods for complex emotion detection from images and videos. Therefore, it is important to learn the profile of the in-the-wild complex emotions accurately using limited annotated samples. In this paper, we propose a deep framework to incrementally and actively profile in-the-wild complex emotions, from sparse data. Our approach consists of three major components, namely a pre-processing unit, an optimization unit and an active learning unit. The pre-processing unit removes the variations present in the complex emotion images extracted from an uncontrolled environment. Our novel incremental active learning algorithm along with an optimization unit effectively predicts the complex emotions present in-the-wild. Evaluation using multiple complex emotions benchmark datasets reveals that our proposed approach performs close to the human perception capability in effectively profiling complex emotions. Further, our proposed approach shows a significant performance enhancement, in comparison with the state-of-the-art deep networks and other benchmark complex emotion profiling approaches.
Micro-batch training based on Residual Attention Network for Acoustic Scene Classification with Mismatched Recording Devices

In this paper, we provide micro-batch training based on Residual Attention Network for Acoustic Scene Classification (ASC) with mismatched recording devices task. The systems for this task are based on various convolutional networks such as Residual Attention Network due to its excellent performance on image domain. First, we modify the input of the Residual Attention Network so that they operate on the log-mel spectrogram domain of audios. Then, we fine-tune the numbers of layers and kernels to provide better performance for ASC. However, deep learning micro-batch training setup is difficult. For ending this problem, we train Residual Attention Network with Switchable Normalization (SN) and Weight Standardization (WS), respectively. Finally, we verify our method on the TUT Urban Acoustic Scenes 2018 Mobile development dataset, and obtain 0.586 of class-wise accuracy on the dataset. Our best model achieves an improvement of more than 10% in terms of classification accuracy with respect to the Detection and classification of acoustic scenes and events (DCASE) 2018 baseline system.
Herpes Zoster and Post-Herpetic Neuralgia: Changing Incidence Rates from 1994 to 2018 in the United States.

INTRODUCTION The incidence of herpes zoster (HZ) has been increasing in recent decades. Although two vaccines for HZ are available, there have been few studies on the incidence rates of HZ and post-herpetic neuralgia (PHN) since their introduction. This study examined the incidence rates of HZ and PHN from 1994-2018 in the United States to determine if they have continued to increase since introduction of the herpes zoster vaccines. METHODS A de-identified longitudinal administrative claims database, the OptumLabs® Data Warehouse, was used to assess incidence rates among individuals continuously enrolled in the database for ≥365 days with no prior history of HZ or PHN. Unstandardized and standardized incidence rates were calculated by year, 10-year age groups, sex, and race/ethnicity. RESULTS There were 610 766 individuals with HZ (median age, 56.3 [IQR: 43.0, 68.7] years; 59.8% women; 70.6% white). From 1994-2018, the incidence of HZ increased from 286.0 (95% CI: 259.1, 312.8) to 579.6 (95% CI: 554.2, 605.0) cases per 100 000 person-years, an annual increase of 3.1% (95% CI: 2.5, 3.6). Since 2007, annual HZ incidence rates have decreased in individuals ≤20 and >60 years old. The overall incidence rate of PHN was 57.5 (95% CI: 56.0, 59.0) cases per 100 000 person-years. The proportion of individuals with HZ who developed PHN was higher from 2007 to 2018 than from 1994 to 2006. CONCLUSION Herpes zoster incidence rates have continued to increase in age groups for which HZ vaccines are not currently recommended, warranting a review of current vaccine recommendations.
Relative effects of survival and reproduction on the population dynamics of emperor geese

Populations of emperor geese (Chen canagica) in Alaska declined sometime between the mid1960s and the mid-1980s and have increased little since. To promote recovery of this species to former levels, managers need to know how much their perturbations of survival and/or reproduction would affect population growth rate (X). We constructed an individual-based population model to evaluate the relative effect of altering mean values of various survival and reproductive parameters on X and fall age structure (AS, defined as the proportion of juv), assuming additive rather than compensatory relations among parameters. Altering survival of adults had markedly greater relative effects on A than did equally proportionate changes in either juvenile survival or reproductive parameters. We found the opposite pattern for relative effects on AS. Due to concerns about bias in the initial parameter estimates used in our model, we used 5 additional sets of parameter estimates with this model structure. We found that estimates of survival based on aerial survey data gathered each fall resulted in models that corresponded more closely to independent estimates of A than did models that used mark-recapture estimates of survival. This disparity suggests that mark-recapture estimates of survival are biased low. To further explore how parameter estimates affected estimates of X, we used values of survival and reproduction found in other goose species, and we examined the effect of an hypothesized correlation between an individual's clutch size and the subsequent survival of her young. The rank order of parameters in their relative effects on A was consistent for all 6 parameter sets we examined. The observed variation in relative effects on A among the 6 parameter sets is indicative of how relative effects on A may vary among goose populations. With this knowledge of the relative effects of survival and reproductive parameters on A, managers can make more informed decisions about which parameters to influence through management or to target for future study. J. WILDL. MANAGE. 61(1):191-201
Enhanced Relative Localization Based on Persistent Excitation for Multi-UAVs in GPS-Denied Environments

Intelligent unmanned aerial vehicles (UAVs) have been applied for civil and military uses. Relative localization (RL) is crucial for multi-UAVs to accomplish complex tasks successfully and safely. In global positioning system (GPS) denied environments, where accurate or meaningful location information is hard to obtain, persistent excitation based RL is a promising approach for multi-UAVs to achieve RL without any needs of external infrastructures. However, for many cases, existing persistent excitation based RL method suffers precision loss, error accumulation and divergence. This article tackles these issues, and proposes an enhanced approach to ensure the practical usage of persistent excitation based RL. Synchronized sensor sample prediction is introduced to confine and reduce RL error, and RL estimation is redesigned to avoid RL error accumulation. We evaluated our solution by simulating various scenarios. The results show that the proposed approach can effectively decrease RL error and prevent RL error accumulation.
The effects of wind on foraging strategies of Atta cephalotes leaf-cutter ants

Successful foragers alter their behavior in response to variation in local conditions, resulting in reduction of foraging costs and maximization of resource gain. In eusocial colonies, individuals may adjust their own efforts to maximize the productivity of the colony as a whole. Maximization of colony productivity can be achieved through sub-maximal individual performance. Attine leaf-cutter ant foragers often cut leaf fragments shorter than the hind legs could allow, suggesting other factors contribute to load size determination. Several studies have shown reasons why leaf-cutter ants cut smaller loads than they could maximally carry. The effects of wind speed on leaf-cutter load size selection were examined in this study and showed conditions in which leaf-cutter foragers change their behavior to cut larger loads than normal. In response to wind treatments, foragers cut larger leaf loads and fewer minima workers hitchhiked on those leaves. This study highlights behavioral plasticity of leaf-cutter foragers in response to local conditions on the foraging trail, and it adds to our knowledge of resource allocation in central-place foraging systems.
Brain connectivity alteration detection via matrix‐variate differential network model

Brain functional connectivity reveals the synchronization of brain systems through correlations in neurophysiological measures of brain activities. Growing evidence now suggests that the brain connectivity network experiences alterations with the presence of numerous neurological disorders, thus differential brain network analysis may provide new insights into disease pathologies. The data from neurophysiological measurement are often multidimensional and in a matrix form, posing a challenge in brain connectivity analysis. Existing graphical model estimation methods either assume a vector normal distribution that in essence requires the columns of the matrix data to be independent or fail to address the estimation of differential networks across different populations. To tackle these issues, we propose an innovative matrix‐variate differential network (MVDN) model. We exploit the D‐trace loss function and a Lasso‐type penalty to directly estimate the spatial differential partial correlation matrix and use an alternating direction method of multipliers algorithm for the optimization problem. Theoretical and simulation studies demonstrate that MVDN significantly outperforms other state‐of‐the‐art methods in dynamic differential network analysis. We illustrate with a functional connectivity analysis of an attention deficit hyperactivity disorder dataset. The hub nodes and differential interaction patterns identified are consistent with existing experimental studies.
An Improved Parallel Matched Filter and Symbol Synchronzation Algorithm Based On Frequency Domain Timing Error Estimation

Traditional serial demodulator cannot meet the requirement of data rates up to Multi-Gigabits due to the constraints of digital chips. Compared with time domain parallel demodulator, frequency domain parallel demodulator has the lowest complexity. The paper presents an improved full frequency domain parallel matched filter and symbol synchronization algorithm on the basis of Lin's algorithm. Here, we adopt Barton's algorithm to estimate timing error instead of O&M algorithm. On one hand, we can estimate timing error in frequency domain directly with Barton's algorithm. On the other hand, the oversample rate for Barton's algorithm is a half of O&M algorithm when the symbol rate is the same. Also, we propose frequency aliasing to decrease the points of IFFT to a half of FFT. Final, the MATLAB floating simulation results shows that the performance our proposed improved algorithm is close to the theoretical curve with the employment of two samples per symbol and the performance loss is less than 0.2dB compared with Lin's algorithm with the employment of four samples per symbol.
Analysis of 1.2 kV SiC SWITCH-MOS after Short-circuit Stress

In this study, the authors investigated residual damage in a 1.2 kV SWITCH-MOS after application of short-circuit stress. After the stress application, corresponding to about 80 % of the SWITCH-MOS withstanding time, the leakage current in the forward blocking state dramatically increased. It was found that the SBD regions in the SWITCH-MOS were affected by the high lattice temperature during the short-circuit stress, which exceeded the fabrication temperature of the Schottky metal layer, meaning that the SBD region could suffer physical damage.
A distributed MIMO radar oriented barrage jamming strategy

Distributed multiple input multiple output (MIMO) radar has attracted much attention for its improved detection and estimation performance as well as enhanced Electronic Counter-Counter Measures (ECCM) ability. In order to protect the target from being detected and tracked by such radar, we consider a barrage jamming strategy orienting towards a distributed MIMO. We first derive the Cramer-Rao bound (CRB) of target parameters estimation using a distributed MIMO under barrage jamming environments. We then set maximizing the CRB as the criterion for jamming resource allocation, aiming at degrading the accuracy of target parameters estimation. Particle swarm optimization is utilized to solve the maximizing problem. Simulation results demonstrate the advantages of the the proposed strategy over traditional jamming methods.
Preamble-Based MMSE Channel Estimation With Low Pilot Overhead in MIMO-FBMC Systems

As is well-known, high pilot overhead in channel estimation is a critical problem due to the imaginary interference in multiple input multiple output (MIMO) employing filter bank multicarrier (FBMC) (MIMO-FBMC) systems. In this paper, we propose a novel preamble design with low overhead and present a minimum mean square error (MMSE) algorithm for the channel estimation, which can improve the system performance in harsh environments. In the proposed preamble, 4 columns of pilots are required, including two columns of zeros to avoid the imaginary interference from data. It is shown that, the imaginary interference between non-zero pilots can be removed by adopting symmetry design. Therefore, pilot overhead is reduced compared with the conventional scheme with 5 columns of pilots. In addition, the noises at adjacent subcarriers are demonstrated to be correlated in MIMO-FBMC systems, and the correlation is considered in the proposed MMSE algorithm. Numerical simulations are conducted to verify the proposed scheme, and for comparison, we also give the performance of the conventional interference approximation method (IAM).
Decisions and Trade-Offs in the Design of a Mass Spectrometer for Jupiter's Icy Moons

This paper describes the Neutral gas and Ion Mass spectrometer (NIM) onboard ESA's JUpiter ICy moon Explorer (JUICE). Instead of detailing the nuts and bolts of the design, we recount it in terms of ‘given’, architectural, and design decisions. ‘Given’ decisions are relative to elements of the instrument defined at the outset of the project, which bound the solution space to be explored by the development team. These decisions comprise the definition of the instrument concept (time-of-flight), sample ionization, MCP detector, ion optics technology, radiation shielding, and allocation of electronics' functions. Architectural and design decisions regard other elements of the instrument defined during the preliminary and detailed design phases, after trade-offs accounting for both technical and programmatic factors. These decisions regard the instrument's ion source, mechanism, FPGA, emitters, PCB interconnect, detector, materials, circuitry of the emission controller and high-voltage pulser, and software. Lessons learned are discussed throughout, including decisions that, ex-post, should have been made differently. This recollection ends with the first mass spectrum acquired with the flight model.
Outage Probability and Throughput Analyses in Full-Duplex Relaying Systems With Energy Transfer

In this article, we consider a dual-hop full-duplex (FD), amplify-and-forward, orthogonal frequency division multiplexing (OFDM) relaying network, where the relay operates based on a time-switching architecture to harvest energy from radio frequency signals. We use a polarization-enabled digital self-interference cancellation (PDC) scheme to cancel the self-interference signal at the relay in order to achieve FD communications. The paper provides a comprehensive analysis of the system performances in terms of outage probability and throughput over multipath Rayleigh fading channels. Furthermore, the optimal time split between the duration of energy harvesting and signal transmission to maximize the system throughput is numerically calculated. We also derive the asymptotic lines to simplify the expressions of outage probability and throughput at high transmit signal-to-noise ratios (SNR). Our analysis and simulation results show that the proposed FD relaying system by utilizing a proper time split fraction can boost the system throughput significantly over an appreciable range of transmitting SNR values, compared to half-duplex (HD) relaying systems.
Public Awareness, Individual Prevention Practice, and Psychological Effect at the Beginning of the COVID-19 Outbreak in China

Background The COVID-19 has spread to more than 200 countries and territories. But less is known about the knowledge, protection behavior and anxiety regarding the outbreak among the general population. Methods A cross-sectional, population-based online survey was conducted in China and abroad from January 28 to February 1, 2020. Socio-demographic information was collected and knowledge scores, practice scores, anxiety scores and perceived risk were calculated. General linear model and binary logistic regression were used to identify possible associations. Results We included 9,764 individuals in this study, and 156 (1.6%) were from Hubei Province. The average knowledge score was 4.7 (standard deviation, 1.0) (scored on a 6-point scale); 96.1% maintained hand hygiene, and 90.3% of participants had varying levels of anxiety. People in Hubei Province were the most anxious, followed by those in Beijing and Shanghai. People who had experienced risk behaviors did not pay more attention to wearing masks and hand hygiene. Conclusions The public had high awareness on knowledge of COVID-19 outbreak, and a high proportion of people practiced good hand hygiene behavior. Many people claimed anxiety, especially in heavily affected areas during pandemic, suggesting the importance of closing the gap between risk awareness and good practice and conduct psychological counseling to public and patients.
Sustained Benefits of NCFETs Under Extreme Scaling to the End of the IRDS

We use full quantum-transport simulations by coupling the Landau–Khalatnikov (LK) and Poisson equations self-consistently with the nonequilibrium Green’s function (NEGF) formalism, and calibrated to experimental results, to investigate extremely scaled negative-capacitance, field-effect transistors (NCFETs) having dimensions toward the end of the international roadmap for devices and systems (IRDS), that is, to sub-10-nm gate lengths, where channel transport can be expected to be governed by quantum-mechanical effects. We identify how the ferroelectric affects both thermionic emission and quantum-mechanical tunneling of electrons, both of which are relevant transport mechanisms for these ultrascaled devices. Our detailed results show that while NCFETs are not immune to the increase in the tunneling as they undergo extreme channel-length scaling, the metal–ferroelectric–insulator–semiconductor (MFIS) structure will continue to offer benefits to a subthreshold slope, ON- and OFF-currents, drain-induced barrier lowering, and output conductance until the end of the roadmap. These improvements allow MFIS NCFETs of any given node to achieve similar performance to nonferroelectric devices of the immediately preceding (higher-dimension) node. The fundamental reason for the improvements is identified to be the presence of voltage amplification at the top of the barrier (TOB) and suppression of TOB movement with drain voltage.
Research on Security Detection Technology for Internet of Things Terminal Based on Firmware Code Genes

Internet of Things (IoT) terminals have firmware with heterogeneous, closed-source, and heavy business but light security characteristics, whereas on the edge, there are limited resources and a high code reuse rate. Once there are security risks at the firmware level, these risks are difficult to detect and discover, and the resulting impact quickly spreads over a wide range. Therefore, a similarity and homology analysis of firmware codes in an IoT terminal will be helpful for further research on firmware malicious code detection, vulnerability mining, backdoor discovery and copyright protection. Inspired by biological genes, this paper attempts to break away from the traditional feature-centered approach and focuses on code classification and the qualitative description of code features to discuss the idea of code similarity and homology analysis. Additionally, the proposed approach is information-centric, focusing on the informativeness (essentiality, stability, antivariability, and heritability) of the firmware code genes and the quantitative analysis of firmware code similarity and homology by discussing common methods and mechanisms. This paper presents security detection technology for IoT terminal firmware by measuring the gene distance between the codes. A prototype firmware security detection system (FSDS) for IoT terminals based on firmware code genes is designed and implemented. The experimental results show that this method has a good search matching effect and has certain advantages over traditional firmware security detection methods based on similarity theory.
TRAF3IP2 (TRAF3 Interacting Protein 2) Mediates Obesity-Associated Vascular Insulin Resistance and Dysfunction in Male Mice

Supplemental Digital Content is available in the text. Insulin resistance in the vasculature is a characteristic feature of obesity and contributes to the pathogenesis of vascular dysfunction and disease. However, the molecular mechanisms underlying obesity-associated vascular insulin resistance and dysfunction remain poorly understood. We hypothesized that TRAF3IP2 (TRAF3 interacting protein 2), a proinflammatory adaptor molecule known to activate pathological stress pathways and implicated in cardiovascular diseases, plays a causal role in obesity-associated vascular insulin resistance and dysfunction. We tested this hypothesis by employing genetic-manipulation in endothelial cells in vitro, in isolated arteries ex vivo, and diet-induced obesity in a mouse model of TRAF3IP2 ablation in vivo. We show that ectopic expression of TRAF3IP2 blunts insulin signaling in endothelial cells and diminishes endothelium-dependent vasorelaxation in isolated aortic rings. Further, 16 weeks of high fat/high sucrose feeding impaired glucose tolerance, aortic insulin-induced vasorelaxation, and hindlimb postocclusive reactive hyperemia, while increasing blood pressure and arterial stiffness in wild-type male mice. Notably, TRAF3IP2 ablation protected mice from such high fat/high sucrose feeding-induced metabolic and vascular defects. Interestingly, wild-type female mice expressed markedly reduced levels of TRAF3IP2 mRNA independent of diet and were protected against high fat/high sucrose diet-induced vascular dysfunction. These data indicate that TRAF3IP2 plays a causal role in vascular insulin resistance and dysfunction. Specifically, the present findings highlight a sexual dimorphic role of TRAF3IP2 in vascular control and identify it as a promising therapeutic target in vasculometabolic derangements associated with obesity, particularly in males.