Scalable SoftGroup for 3D Instance Segmentation on Point Clouds.

This paper considers a network referred to as SoftGroup for accurate and scalable 3D instance segmentation. Existing state-of-the-art methods produce hard semantic predictions followed by grouping instance segmentation results. Unfortunately, errors stemming from hard decisions propagate into the grouping, resulting in poor overlap between predicted instances and ground truth and substantial false positives.

Clinical Validation of a Wearable Piezoelectric Blood-Pressure Sensor for Continuous Health Monitoring.

Wearable blood-pressure sensors have recently attracted attention as healthcare devices for continuous non-invasive arterial pressure (CNAP) monitoring. However, the accuracy of wearable blood-pressure (BP) monitoring devices has been controversial due to the low signal quality of sensors, the absence of an accurate transfer function to convert the sensor signals into BP values, and the lack of clinical validation regarding measurement precision. Here, a wearable piezoelectric blood-pressure sensor (WPBPS) is reported, which achieves a high normalized sensitivity (0.

Efficient Convolutional Neural Networks for Semiconductor Wafer Bin Map Classification.

The results obtained in the wafer test process are expressed as a wafer map and contain important information indicating whether each chip on the wafer is functioning normally. The defect patterns shown on the wafer map provide information about the process and equipment in which the defect occurred, but automating pattern classification is difficult to apply to actual manufacturing sites unless processing speed and resource efficiency are supported. The purpose of this study was to classify these defect patterns with a small amount of resources and time.

CE-BART: Cause-and-Effect BART for Visual Commonsense Generation.

"A Picture is worth a thousand words". Given an image, humans are able to deduce various cause-and-effect captions of past, current, and future events beyond the image. The task of visual commonsense generation has the aim of generating three cause-and-effect captions for a given image: (1) what needed to happen before, (2) what is the current intent, and (3) what will happen after.

Visual Pretraining via Contrastive Predictive Model for Pixel-Based Reinforcement Learning.

In an attempt to overcome the limitations of reward-driven representation learning in vision-based reinforcement learning (RL), an unsupervised learning framework referred to as the visual pretraining via contrastive predictive model (VPCPM) is proposed to learn the representations detached from the policy learning. Our method enables the convolutional encoder to perceive the underlying dynamics through a pair of forward and inverse models under the supervision of the contrastive loss, thus resulting in better representations. In experiments with a diverse set of vision control tasks, by initializing the encoders with VPCPM, the performance of state-of-the-art vision-based RL algorithms is significantly boosted, with 44% and 10% improvement for RAD and DrQ at 100 steps, respectively.

Dual-Scale Doppler Attention for Human Identification.

This paper considers a Deep Convolutional Neural Network (DCNN) with an attention mechanism referred to as Dual-Scale Doppler Attention (DSDA) for human identification given a micro-Doppler (MD) signature induced as input. The MD signature includes unique gait characteristics by different sized body parts moving, as arms and legs move rapidly, while the torso moves slowly. Each person is identified based on his/her unique gait characteristic in the MD signature.

Fair Facial Attribute Classification via Causal Graph-Based Attribute Translation.

Recent studies have raised concerns regarding racial and gender disparity in facial attribute classification performance. As these attributes are directly and indirectly correlated with the sensitive attribute in a complex manner, simple disparate treatment is ineffective in reducing performance disparity. This paper focuses on achieving counterfactual fairness for facial attribute classification.

Biomimetic and flexible piezoelectric mobile acoustic sensors with multiresonant ultrathin structures for machine learning biometrics.

Flexible resonant acoustic sensors have attracted substantial attention as an essential component for intuitive human-machine interaction (HMI) in the future voice user interface (VUI). Several researches have been reported by mimicking the basilar membrane but still have dimensional drawback due to limitation of controlling a multifrequency band and broadening resonant spectrum for full-cover phonetic frequencies. Here, highly sensitive piezoelectric mobile acoustic sensor (PMAS) is demonstrated by exploiting an ultrathin membrane for biomimetic frequency band control.

Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing.

Flexible piezoelectric acoustic sensors have been developed to generate multiple sound signals with high sensitivity, shifting the paradigm of future voice technologies. Speech recognition based on advanced acoustic sensors and optimized machine learning software will play an innovative interface for artificial intelligence (AI) services. Collaboration and novel approaches between both smart sensors and speech algorithms should be attempted to realize a hyperconnected society, which can offer personalized services such as biometric authentication, AI secretaries, and home appliances.

Image Segmentation Using Higher-Order Correlation Clustering.

In this paper, a hypergraph-based image segmentation framework is formulated in a supervised manner for many high-level computer vision tasks. To consider short- and long-range dependency among various regions of an image and also to incorporate wider selection of features, a higher-order correlation clustering (HO-CC) is incorporated in the framework. Correlation clustering (CC), which is a graph-partitioning algorithm, was recently shown to be effective in a number of applications such as natural language processing, document clustering, and image segmentation.

Task-specific image partitioning.

Image partitioning is an important preprocessing step for many of the state-of-the-art algorithms used for performing high-level computer vision tasks. Typically, partitioning is conducted without regard to the task in hand. We propose a task-specific image partitioning framework to produce a region-based image representation that will lead to a higher task performance than that reached using any task-oblivious partitioning framework and existing supervised partitioning framework, albeit few in number.

Clinical significance of anti-filaggrin antibody recognizing uncitrullinated filaggrin in rheumatoid arthritis.

Filaggrin is expressed in the cornified layer of epidermis and known to be one of the antigenic targets in rheumatoid arthritis. Although the citrulline residue in filaggrin is thought to be an antigenic determinant recognized by autoantibodies, the diagnostic sensitivity of synthetic citrullinated peptide is variable. To investigate the implication of anti-filaggrin antibodies recognizing uncitrullinated filaggrin in rheumatoid arthritis, we assayed antibody titers using unmodified recombinant filaggrin in the sera from 73 patients with rheumatoid arthritis, 150 patients with other connective tissue diseases and 70 normal controls.

Juvenile onset ankylosing spondylitis (JAS) has less severe spinal disease course than adult onset ankylosing spondylitis (AAS): clinical comparison between JAS and AAS in Korea.

Objective: To assess the frequency of juvenile onset ankylosing spondylitis (JAS) in Korean patients with AS and to differentiate the clinical characteristics of JAS from adult onset ankylosing spondylitis (AAS).

Methods: We studied 98 consecutive patients with AS who visited the rheumatology clinic of a tertiary referral center and compared clinical and radiographic features of JAS (n = 41) with those of AAS (n = 57).

Results: Median age at onset in JAS was 14 years (range 7-16) and in AAS 22 years (range 17-38) (p < 0.