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In this paper, a CMOS wideband low-pass filter is proposed. In the wide band active filter design, the unit gain frequency (GBW) of OPAMP should be larger than GHz. The GBW of the proposed OPAMP is around 2.3 GHz. Additionally, the OPAMP with a complementary structure in signal and common mode feedback is proposed and has a wide operating voltage range at the input and output. The 1-dB gain compression point (P1dB) at the output is 4.9 dBm. A source follower is employed for driving 50 Ω output impedance without degrading the bandwidth and linearity of the filter. The power consumption is 27 mW from a 1-V supply voltage. The 3 dB bandwidth of the filter ranges from 330 MHz to 660 MHz with 3-bit cap tuning. This work is implemented in a 65-nm CMOS process, with a chip area of 0.18 mm2.

Multimodal emotion recognition has gained much traction in the field of affective computing, human–computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals. This has led to process emotions from multiple modalities, usually combined through ensemble-based systems with static weights. Due to numerous limitations like missing modality data, inter-class variations, and intra-class similarities, an effective weighting scheme is thus required to improve the aforementioned discrimination between modalities. This article takes into account the importance of difference between multiple modalities and assigns dynamic weights to them by adapting a more efficient combination process with the application of generalized mixture (GM) functions. Therefore, we present a hybrid multimodal emotion recognition (H-MMER) framework using multi-view learning approach for unimodal emotion recognition and introducing multimodal feature fusion level, and decision level fusion using GM functions. In an experimental study, we evaluated the ability of our proposed framework to model a set of four different emotional states (Happiness, Neutral, Sadness, and Anger) and found that most of them can be modeled well with significantly high accuracy using GM functions. The experiment shows that the proposed framework can model emotional states with an average accuracy of 98.19% and indicates significant gain in terms of performance in contrast to traditional approaches. The overall evaluation results indicate that we can identify emotional states with high accuracy and increase the robustness of an emotion classification system required for UX measurement.

Background: The molecular mechanisms for the genome-wide association studies (GWAS)-identified prostate cancer (PCa) risk-associated single-nucleotide polymorphisms (SNP) remain largely unexplained. One recent finding that the PCa risk SNPs are enriched in genomic regions containing androgen receptor (AR)-binding sites has suggested altered AR signaling as a potentially important mechanism.Methods: To explore novel associations by leveraging this knowledge, we utilized a meta-analysis previously done over SNPs harbored in ChIP-on-chip identified AR-binding genomic regions using the GWAS data from the Johns Hopkins Hospital (JHH) and the Cancer Genetic Markers of Susceptibility (CGEMS) study, and subsequently evaluated the top associations in a third population from the CAncer of the Prostate in Sweden (CAPS) study.Results: One SNP (rs4919743: G>A), located at the KRT8 locus at 12q13.13 which encodes a keratin protein (K8) long used as a prostate epithelial malignancy marker and implicated in the tumorigenesis of several cancer types, was identified to be associated with PCa risk. The frequency of its minor “A” allele was consistently higher in PCa cases than in controls in all three study populations, with a combined OR of 1.22 (95% CI: 1.13–1.32) and an overall P value of 4.50 × 10−7 (Bonferroni corrected, P = 0.006).Conclusion: We have identified a novel genetic locus that is associated with PCa risk.Impact: This study illustrated the great potential of prior biological knowledge in facilitating the search for novel disease-associated genetic loci. This finding warrants further replication in other studies. Cancer Epidemiol Biomarkers Prev; 20(11); 2396–403. ©2011 AACR.

Disease risk–associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies have the potential to be used for disease risk prediction. An important feature of these risk-associated SNPs is their weak individual effect but stronger cumulative effect on disease risk. Several approaches are commonly used to model the combined effect in risk prediction, but their performance is unclear. We compared two methods to model the combined effect of 14 prostate cancer risk–associated SNPs and family history for the estimation of absolute risk for prostate cancer in a population-based case-control study in Sweden (2,899 cases and 1,722 controls). Method 1 weighs each risk allele equally using a simple method of counting the number of risk alleles, whereas method 2 weighs each risk SNP differently based on its odds ratio. We found considerable differences between the two methods. Absolute risk estimates from method 1 were generally higher than those of method 2, especially among men at higher risk. The difference in the overall discriminative performance, measured by area under the curve of the receiver operating characteristic, was small between method 1 (0.614) and method 2 (0.618), P = 0.20. However, the performance of these two methods in identifying high-risk individuals (2- or 3-fold higher than average risk), measured by positive predictive values, was higher for method 2 than for method 1. These results suggest that method 2 is superior to method 1 in estimating absolute risk if the purpose of risk prediction is to identify high-risk individuals. Cancer Epidemiol Biomarkers Prev; 19(4); 1083–8. ©2010 AACR.

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Supplementary Table 1 from Comparison of Two Methods for Estimating Absolute Risk of Prostate Cancer Based on Single Nucleotide Polymorphisms and Family History

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Supplementary Table 2 from Comparison of Two Methods for Estimating Absolute Risk of Prostate Cancer Based on Single Nucleotide Polymorphisms and Family History

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Supplementary Figure 1 from A Novel Prostate Cancer Susceptibility Locus at 19q13

We searched for deletions in the germ-line genome among 498 aggressive prostate cancer cases and 494 controls from a population-based study in Sweden [CAncer of the Prostate in Sweden (CAPS)] using Affymetrix SNP arrays. By comparing allele intensities of ∼500,000 SNP probes across the genome, a germ-line deletion at 2p24.3 was observed to be significantly more common in cases (12.63%) than in controls (8.28%); P = 0.028. To confirm the association, we genotyped this germ-line copy number variation (CNV) in additional subjects from CAPS and from Johns Hopkins Hospital (JHH). Overall, among 4,314 cases and 2,176 controls examined, the CNV was significantly associated with prostate cancer risk [odds ratio (OR), 1.25; 95% confidence interval (95% CI), 1.06–1.48; P = 0.009]. More importantly, the association was stronger for aggressive prostate cancer (OR, 1.31; 95% CI, 1.08–1.58; P = 0.006) than for nonaggressive prostate cancer (OR, 1.19; 95% CI, 0.98–1.45; P = 0.08). The biological effect of this germ-line CNV is unknown because no known gene resides in the deletion. Results from this study represent the first novel germ-line CNV that was identified from a genome-wide search and was significantly, but moderately, associated with prostate cancer risk. Additional confirmation of this association and functional studies are warranted. [Cancer Res 2009;69(6):2176–9]

Supplementary Table 1 from A Novel Prostate Cancer Susceptibility Locus at 19q13

A two-stage genome-wide association study (GWAS) of the Cancer Genetic Markers of Susceptibility (CGEMS) initiative identified single nucleotide polymorphisms (SNP) in 150 regions across the genome that may be associated with prostate cancer (PCa) risk. We filtered these results to identify 43 independent SNPs where the frequency of the risk allele was consistently higher in cases than in controls in each of the five CGEMS study populations. Genotype information for 22 of these 43 SNPs was obtained either directly by genotyping or indirectly by imputation in our PCa GWAS of 500 cases and 500 controls selected from a population-based case-control study in Sweden [Cancer of the Prostate in Sweden (CAPS)]. Two of these 22 SNPs were significantly associated with PCa risk (P < 0.05). We then genotyped these two SNPs in the remaining cases (n = 2,393) and controls (n = 1,222) from CAPS and found that rs887391 at 19q13 was highly associated with PCa risk (P = 9.4 × 10−4). A similar trend of association was found for this SNP in a case-control study from Johns Hopkins Hospital (JHH), albeit the result was not statistically significant. Altogether, the frequency of the risk allele of rs887391 was consistently higher in cases than controls among each of seven study populations examined, with an overall P = 3.2 × 10−7 from a combined allelic test. A fine-mapping study in a 110-kb region at 19q13 among CAPS and JHH study populations revealed that rs887391 was the most strongly associated SNP in the region. Additional confirmation studies of this region are warranted. [Cancer Res 2009;69(7):2720–3]

Supplementary Figure 1 from Association of a Germ-Line Copy Number Variation at 2p24.3 and Risk for Aggressive Prostate Cancer

Supplementary Methods, Tables and Figures from Sequence Variants at 22q13 Are Associated with Prostate Cancer Risk

Supplementary Table 3 from A Novel Prostate Cancer Susceptibility Locus at 19q13

Supplementary Table 2 from A Novel Prostate Cancer Susceptibility Locus at 19q13

Supplementary Figure 2 from Association of a Germ-Line Copy Number Variation at 2p24.3 and Risk for Aggressive Prostate Cancer

Supplementary Table 4 from A Novel Prostate Cancer Susceptibility Locus at 19q13

In this paper, an energy-efficient switching scheme with additional quarter-reference voltage sources in a successive approximation register (SAR) analog-to-digital converter (ADC) is proposed for a low power and small area device for frequency modulated continuous wave (FMCW) radar transceivers. Recently, state-of-the-art ADCs have adopted a configuration that also uses Vref/2 as the reference voltage of the ADCs to improve the switching energy of capacitive digital-to-analog converter (CDAC). The proposed switching configuration additionally uses Vref/4 and 3Vref/4 reference voltages as the reference voltage of CDAC. Compared to state-of-the-art configurations that use the additional reference voltage of Vref/2, the average switching energy, and the total capacitance of CDAC in the proposed configuration are reduced by about 87.5% and 50%, respectively. In this switching scheme, the CDAC output voltage gradually converges to Vref/2, like with conventional SAR ADCs, which minimizes the dynamic offset that deteriorates the linearity of the SAR ADC.

Along with the outstanding performance of the deep neural networks (DNNs), considerable research efforts have been devoted to finding ways to understand the decision of DNNs structures. In the computer vision domain, visualizing the attribution map is one of the most intuitive and understandable ways to achieve human-level interpretation. Among them, perturbation-based visualization can explain the "black box" property of the given network by optimizing perturbation masks that alter the network prediction of the target class the most. However, existing perturbation methods could make unexpected changes to network predictions after applying a perturbation mask to the input image, resulting in a loss of robustness and fidelity of the perturbation mechanisms. In this paper, we define class distortion as the unexpected changes of the network prediction during the perturbation process. To handle that, we propose a novel visual interpretation framework, Robust Perturbation, which shows robustness against the unexpected class distortion during the mask optimization. With a new cross-checking mask optimization strategy, our proposed framework perturbs the target prediction of the network while upholding the non-target predictions, providing more reliable and accurate visual explanations. We evaluate our framework on three different public datasets through extensive experiments. Furthermore, we propose a new metric for class distortion evaluation. In both quantitative and qualitative experiments, tackling the class distortion problem turns out to enhance the quality and fidelity of the visual explanation in comparison with the existing perturbation-based methods.

Composites were prepared, through hot pressing, using carbon materials with different pore size distributions as additives for commercial Bi0.5Sb1.5Te3 thermoelectric material (BST, p-type). Thermoelectric properties of the composites were measured in a temperature range of 298‒473 K. Thermal conductivity of the composites, especially lattice thermal conductivity, was effectively decreased due to the mesoporous properties of the incorporated carbon additives. The electrical conductivity of the composites slightly decreased due to the electron scattering at the interface between the carbon material and the commercial BST matrix. The composite with 0.2 vol.% mesoporous carbon powder (36% mesoporosity) exhibited a figure of merit value approximately 10.7% higher than that of commercial BST without additives. This behavior resulted in 116% improved output power in the composite block-based single element compared with a bare BST thermoelectric block. The enhanced figure of merit was attributed to the effective reduction of lattice thermal conductivity by acoustic phonons scattering at the interface between the BST matrix and the mesoporous carbon as well as at the pore surfaces within the mesoporous carbon. By utilizing mesoporous carbon materials used in this study, the shortcomings and economic difficulties of the composite process with low dimensional carbon additives (carbon nanotubes, graphene, and nanodiamond) can be overcome for extensive practical applications. Mesoporous carbon powder with a tailored porosity distribution revealed the validity of bulk-type carbon additives to enhance the figure of merit of commercial thermoelectric materials.

Recently, a wide range of research on object detection has shown breakthrough performance. However, in a challenging environment, such as occlusion and small object cases, object detectors still produce inaccurate or erroneous predictions. To effectively cope with such conditions, most of the existing methods have suggested loss functions to guide the object detectors by modulating the magnitude of their loss. However, when modulating the loss function, they are highly dependent on the classification score of the object detector. It is a known fact that deep neural networks tend to be overconfident in their predictions. In this article, to alleviate the problem of the object detectors which heavily rely on the prediction in the training phase, we devise a novel loss function called class uncertainty-aware (CUA) loss. CUA loss considers the predictive ambiguity as well as the predictions on classification score when modulating loss function. In addition to the classification score, CUA loss further modulates the loss gradient in an increasing way when the object detectors output an uncertain prediction. Therefore, object detectors with CUA loss effectively cope with challenging environments where prediction results are uncertain. With comprehensive experiments on three public datasets ( i.e. PASCAL VOC, MS COCO, and Berkeley DeepDrive), we verified that our CUA loss enhanced the accuracy of the object detectors and outperformed previous state-of-the-art loss functions.

Quantitative assessment of the abdominal region from clinically acquired CT scans requires the simultaneous segmentation of abdominal organs. Thanks to the availability of high-performance computational resources, deep learning-based methods have resulted in state-of-the-art performance for the segmentation of 3D abdominal CT scans. However, the complex characterization of organs with fuzzy boundaries prevents the deep learning methods from accurately segmenting these anatomical organs. Specifically, the voxels on the boundary of organs are more vulnerable to misprediction due to the highly-varying intensity of inter-organ boundaries. This paper investigates the possibility of improving the abdominal image segmentation performance of the existing 3D encoder-decoder networks by leveraging organ-boundary prediction as a complementary task. To address the problem of abdominal multi-organ segmentation, we train the 3D encoder-decoder network to simultaneously segment the abdominal organs and their corresponding boundaries in CT scans via multi-task learning. The network is trained end-to-end using a loss function that combines two task-specific losses, i.e., complete organ segmentation loss and boundary prediction loss. We explore two different network topologies based on the extent of weights shared between the two tasks within a unified multi-task framework. To evaluate the utilization of complementary boundary prediction task in improving the abdominal multi-organ segmentation, we use three state-of-the-art encoder-decoder networks: 3D UNet, 3D UNet++, and 3D Attention-UNet. The effectiveness of utilizing the organs' boundary information for abdominal multi-organ segmentation is evaluated on two publically available abdominal CT datasets. A maximum relative improvement of 3.5% and 3.6% is observed in Mean Dice Score for Pancreas-CT and BTCV datasets, respectively., 15 pages, 16 figures, journal paper

Is critical input information encoded in specific sparse pathways within the neural network? In this work, we discuss the problem of identifying these critical pathways and subsequently leverage them for interpreting the network's response to an input. The pruning objective -- selecting the smallest group of neurons for which the response remains equivalent to the original network -- has been previously proposed for identifying critical pathways. We demonstrate that sparse pathways derived from pruning do not necessarily encode critical input information. To ensure sparse pathways include critical fragments of the encoded input information, we propose pathway selection via neurons' contribution to the response. We proceed to explain how critical pathways can reveal critical input features. We prove that pathways selected via neuron contribution are locally linear (in an L2-ball), a property that we use for proposing a feature attribution method: "pathway gradient". We validate our interpretation method using mainstream evaluation experiments. The validation of pathway gradient interpretation method further confirms that selected pathways using neuron contributions correspond to critical input features. The code is publicly available., Comment: Accepted at CVPR 2021 (IEEE/CVF Conference on Computer Vision and Pattern Recognition)

The availability of large labeled datasets is the key component for the success of deep learning. However, annotating labels on large datasets is generally time-consuming and expensive. Active learning is a research area that addresses the issues of expensive labeling by selecting the most important samples for labeling. Diversity-based sampling algorithms are known as integral components of representation-based approaches for active learning. In this paper, we introduce a new diversity-based initial dataset selection algorithm to select the most informative set of samples for initial labeling in the active learning setting. Self-supervised representation learning is used to consider the diversity of samples in the initial dataset selection algorithm. Also, we propose a novel active learning query strategy, which uses diversity-based sampling on consistency-based embeddings. By considering the consistency information with the diversity in the consistency-based embedding scheme, the proposed method could select more informative samples for labeling in the semi-supervised learning setting. Comparative experiments show that the proposed method achieves compelling results on CIFAR-10 and Caltech-101 datasets compared with previous active learning approaches by utilizing the diversity of unlabeled data., International Conference on Pattern Recognition (ICPR) 2022

Longitudinal analysis of a disease is an important issue to understand its progression and design prognosis and early diagnostic tools. From the longitudinal images where data is collected from multiple time points, both the spatial structural information and the longitudinal variations are captured. The temporal dynamics are more informative than static observations of the symptoms, particularly for neurodegenerative diseases such as Alzheimer’s disease, whose progression spans over the years with early subtle changes. In this paper, we propose a new generative framework to predict the lesion progression over time. Our method first encodes images into the structural and longitudinal state vectors, where interpolation or extrapolation of feature vectors in the time axis can be performed for the manipulation of these feature vectors. These processed feature vectors can be decoded into image space to predict the image at the time point which we are interested in. During the training, we force the model to encode longitudinal changes into longitudinal state features and capture the structural information in a separate vector. Moreover, we introduce a personalized memory for the online update scheme, which adapts the model to the target subject, which helps the model preserve fine details of brain image structures in each subject. Experimental results on the public longitudinal brain magnetic resonance imaging dataset show the effectiveness of the proposed method.

The Korean Society of Laryngology, Phoniatrics and Logopedics appointed a task force to establish clinical practice guidelines for the management of unilateral vocal fold paralysis (UVFP). These guidelines cover a comprehensive range of management-related factors, including the diagnosis and treatment of UVFP, and provide in-depth information based on current, up-to-date knowledge. Detailed evidence profiles are provided for each recommendation. The CORE databases, including OVID Medline, Embase, the Cochrane Library, and KoreaMed, were searched to identify all relevant papers, using a predefined search strategy. When insufficient evidence existed, expert opinions and Delphi questionnaires were used to fill the evidence gap. The committee developed 16 evidence-based recommendations in six categories: initial evaluation (R1–4), spontaneous recovery (R5), medical treatment (R6), surgical treatment (R7–14), voice therapy (R15), and aspiration prevention (R16). The goal of these guidelines is to assist general otolaryngologists and speech-language pathologists who are primarily responsible for treating patients with UVFP. These guidelines are also intended to facilitate understanding of the condition among other health-care providers, including primary care physicians, nurses, and policy-makers.

Implied volatility and implied vol-of-vol are two different sources of risk but the latter has been generally neglected. However, recent studies show the importance of both risk factors for investment strategies. The elasticity of vol-of-vol is focused on in this paper. We propose a revised Heston model reflecting the random nature of vol-of-vol and obtain pricing formulas of variance swaps for simple returns. We use a perturbation technique to transform the problem into a partial differential equation problem and then use the Green function and Fourier transform methods to derive explicitly a quasi-closed form approximation of the fair strike price. Subsequent result shows a comparison with the Heston result and the impact of the stochastic elasticity of vol-of-vol on the variance swap price.

Spine injections are commonly performed in several clinical procedures. The localization of the target vertebral level (i.e. the position of a vertebra in a spine) is typically done by back palpation or under X-ray guidance, yielding either higher chances of procedure failure or exposure to ionizing radiation. Preliminary studies have been conducted in the literature, suggesting that ultrasound imaging may be a precise and safe alternative to X-ray for spine level detection. However, ultrasound data are noisy and complicated to interpret. In this study, a robotic-ultrasound approach for automatic vertebral level detection is introduced. The method relies on the fusion of ultrasound and force data, thus providing both "tactile" and visual feedback during the procedure, which results in higher performances in presence of data corruption. A robotic arm automatically scans the volunteer's back along the spine by using force-ultrasound data to locate vertebral levels. The occurrences of vertebral levels are visible on the force trace as peaks, which are enhanced by properly controlling the force applied by the robot on the patient back. Ultrasound data are processed with a Deep Learning method to extract a 1D signal modelling the probabilities of having a vertebra at each location along the spine. Processed force and ultrasound data are fused using a 1D Convolutional Network to compute the location of the vertebral levels. The method is compared to pure image and pure force-based methods for vertebral level counting, showing improved performance. In particular, the fusion method is able to correctly classify 100% of the vertebral levels in the test set, while pure image and pure force-based method could only classify 80% and 90% vertebrae, respectively. The potential of the proposed method is evaluated in an exemplary simulated clinical application., Comment: Under Review in IEEE Robotics and Automation Letters (RA-L) with IROS 2020

Facial landmark detection plays an important role in face analysis tasks. Moreover, it is used as a prerequisite in many facial related applications, the simplicity, as well as effectiveness, is essential in the facial landmark detection. In this paper, we propose an effective facial landmark detection network and an associated learning framework with the geometric prior-generative adversarial network. The geometric prior-generative adversarial network consists of one generator and two discriminators. The generator consists of an encoder and two decoders. The encoder predicts facial landmark points. The decoders generate a facial inner and contour geometric map from predicted landmark points. Generating face geometric maps from predicted landmark points helps the predicted landmark points to represent the face geometric information, including shape and configuration. The discriminators determine that the given geometric maps are generated from actual landmark points or estimated landmark points. Our proposed network is end-to-end trainable, and only the encoder part is used simply as the facial landmark detector in the testing stage. To verify the effectiveness of the proposed method, we have conducted comprehensive experiments with benchmark data sets. The results have shown that the proposed method achieves comparable performances over recently proposed facial landmark detection methods with a simple and effective facial landmark detection network.