Img2Logo:Generating Golden Ratio Logos from Images

Logos are one of the most important graphic design forms that use an abstracted shape to clearly represent the spirit of a community. Among various styles of abstraction, a particular golden-ratio design is frequently employed by designers to create a concise and regular logo. In this context, designers utilize a set of circular arcs with golden ratios (i.e., all arcs are taken from circles whose radii form a geometric series based on the golden ratio) as the design elements to manually approximate a target shape. This error-prone process requires a large amount of time and effort, posing a significant challenge for design space exploration. In this work, we present a novel computational framework that can automatically generate golden ratio logo abstractions from an input image. Our framework is based on a set of carefully identified design principles and a constrained optimization formulation respecting these principles. We also propose a progressive approach that can efficiently solve the optimization problem, resulting in a sequence of abstractions that approximate the input at decreasing levels of detail. We evaluate our work by testing on images with different formats including real photos, clip arts, and line drawings. We also extensively validate the key components and compare our results with manual results by designers to demonstrate the effectiveness of our framework. Moreover, our framework can largely benefit design space exploration via easy specification of design parameters such as abstraction levels, golden circle sizes, etc

Prognostic values of a combination of intervals between respiratory illness and onset of neurological symptoms and elevated serum IgM titers in Mycoplasma pneumoniae encephalopathy

Background/PurposeTo retrospectively analyze the clinical manifestations of Mycoplasma pneumoniae (M. pneumoniae)-associated encephalopathy in pediatric patients.MethodsPediatric patients with positive serum anti-M. pneumoniae immunoglobulin M (IgM) were enrolled in this study. Clinical signs and symptoms, laboratory data, neuroimaging findings, and electrophysiological data were reviewed.ResultsOf 1000 patients identified, 11 (1.1%; male:female ratio = 7:4) had encephalopathy and were admitted to the pediatric intensive care unit. Clinical presentation included fever, symptoms of respiratory illness, and gastrointestinal upset. Neurological symptoms included altered consciousness, seizures, coma, focal neurological signs, and personality change. Neuroimaging and electroencephalographic findings were non-specific. Specimens of cerebrospinal fluid (CSF) for M. pneumoniae polymerase chain reaction (PCR) were negative. Higher M. pneumoniae IgM titers and longer intervals between respiratory and CNS manifestations were associated with worse outcomes.ConclusionClinical manifestations of M. pneumoniae-associated encephalopathy were variable. Diagnosis of M. pneumoniae encephalopathy should not rely on CSF detection of M. pneumoniae by PCR. M. pneumoniae IgM titers and intervals between respiratory and CNS manifestations might be possibly related to the prognosis of patients with M. pneumoniae-associated encephalopathy

Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots

Conventional Chinese calligraphy robots often suffer from the limited sizes of predefined font databases, which prevent the robots from writing new characters. This paper presents a robotic handwriting system to address such limitations, which extracts Chinese characters from textbooks and uses a robot’s manipulator to write the characters in a different style. The key technologies of the proposed approach include the following: (1) automatically decomposing Chinese characters into strokes using Harris corner detection technology and (2) matching the decomposed strokes to robotic writing trajectories learned from human gestures. Briefly, the system first decomposes a given Chinese character into a set of strokes and obtains the stroke trajectory writing ability by following the gestures performed by a human demonstrator. Then, it applies a stroke classification method that recognizes the decomposed strokes as robotic writing trajectories. Finally, the robot arm is driven to follow the trajectories and thus write the Chinese character. Seven common Chinese characters have been used in an experiment for system validation and evaluation. The experimental results demonstrate the power of the proposed system, given that the robot successfully wrote all the testing characters in the given Chinese calligraphic style

Effect of hydrogen sulfide on PC12 cell injury induced by high ATP concentration

Purpose: To investigate the potential protective effect of hydrogen sulfide against neural cell damage induced by a high-concentration of adenosine triphosphate (ATP).Methods: PC12 cells were incubated with ATP in order to induce cell damage. The extracellular level of H2S and protein expression of cystathionine-β-synthase (CBS) were determined. The PC12 cells pretreated with NaHS, aminooxyacetic acid (AOAA) and KN-62, prior to further incubation with ATP, and the effect of the treatments on cell viability was investigated.Results: High-concentration ATP induced cell death in PC12 cells, and this was accompanied by markedly increased contents of extracellular H2S and CBS expression (p < 0.05). The ATP-induced cytotoxicity was significantly compromised after pretreatment with H2S. (p < 0.05). The viability of PC12 cells pretreated with NaHS and AOAA was significantly higher than that of PC12 cells treated with ATP alone. In addition, the viability of ATP-treated PC12 cells was further markedly increased after pretreatment with NaHS and KN-62 (p < 0.05).Conclusion: ATP induced a concentration- and time-dependent cytotoxicity in PC12 cells via theendogenous H2S/CBS system. Supplementation with exogenous H2S mitigated the cell damageinduced by high concentration of ATP via a specific mechanism which may be specifically related to P2X7R

Enhanced robotic hand-eye coordination inspired from human-like behavioral patterns

Robotic hand-eye coordination is recognized as an important skill to deal with complex real environments. Conventional robotic hand-eye coordination methods merely transfer stimulus signals from robotic visual space to hand actuator space. This paper introduces a reverse method: Build another channel that transfers stimulus signals from robotic hand space to visual space. Based on the reverse channel, a human-like behavior pattern: “Stop-to-Fixate”, is imparted to the robot, thereby giving the robot an enhanced reaching ability. A visual processing system inspired by the human retina structure is used to compress visual information so as to reduce the robot’s learning complexity. In addition, two constructive neural networks establish the two sensory delivery channels. The experimental results demonstrate that the robotic system gradually obtains a reaching ability. In particular, when the robotic hand touches an unseen object, the reverse channel successfully drives the visual system to notice the unseen object

A Robot Calligraphy System: From Simple to Complex Writing by Human Gestures

Robotic writing is a very challenging task and involves complicated kinematic control algorithms and image processing work. This paper, alternatively, proposes a robot calligraphy system that firstly applies human arm gestures to establish a font database of Chinese character elementary strokes and English letters, then uses the created database and human gestures to write Chinese characters and English words. A three-dimensional motion sensing input device is deployed to capture the human arm trajectories, which are used to build the font database and to train a classifier ensemble. 26 types of human gesture are used for writing English letters, and 5 types of gesture are used to generate 5 elementary strokes for writing Chinese characters. By using the font database, the robot calligraphy system acquires a basic writing ability to write simple strokes and letters. Then, the robot can develop to write complex Chinese characters and English words by following human body movements. The classifier ensemble, which is used to identify each gesture, is implemented through using feature selection techniques and the harmony search algorithm, thereby achieving better classification performance. The experimental evaluations are carried out to demonstrate the feasibility and performance of the proposed method. By following the motion trajectories of the human right arm, the end-effector of the robot can successfully write the English words or Chinese characters that correspond to the arm trajectories

Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection

Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification. Nevertheless, such methods usually require laborious object-level annotations (i.e., object labels and bounding boxes) for effective learning of the object-level visual features. In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations. Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs. The WSD model is the teacher model and the classification model is the student model. After this cross-task knowledge distillation, the performance of the classification model is significantly improved and the efficiency is maintained since the WSD model can be safely discarded in the test phase. Extensive experiments on two large-scale datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior performances over the state-of-the-art methods on both performance and efficiency.Comment: accepted by ACM Multimedia 2018, 9 pages, 4 figures, 5 table

Abnormalities of Hippocampal Subfield and Amygdalar Nuclei Volumes and Clinical Correlates in Behavioral Variant Frontotemporal Dementia with Obsessive–Compulsive Behavior—A Pilot Study

(1) Background: The hippocampus (HP) and amygdala are essential structures in obsessive–compulsive behavior (OCB); however, the specific role of the HP in patients with behavioral variant frontotemporal dementia (bvFTD) and OCB remains unclear. (2) Objective: We investigated the alterations of hippocampal and amygdalar volumes in patients with bvFTD and OCB and assessed the correlations of clinical severity with hippocampal subfield and amygdalar nuclei volumes in bvFTD patients with OCB. (3) Materials and methods: Eight bvFTD patients with OCB were recruited and compared with eight age- and sex-matched healthy controls (HCs). Hippocampal subfield and amygdalar nuclei volumes were analyzed automatically using a 3T magnetic resonance image and FreeSurfer v7.1.1. All participants completed the Yale–Brown Obsessive–Compulsive Scale (Y-BOCS), Neuropsychiatric Inventory (NPI), and Frontal Behavioral Inventory (FBI). (4) Results: We observed remarkable reductions in bilateral total hippocampal volumes. Compared with the HCs, reductions in the left hippocampal subfield volume over the cornu ammonis (CA)1 body, CA2/3 body, CA4 body, granule cell layer, and molecular layer of the dentate gyrus (GC-ML-DG) body, molecular layer of the HP body, and hippocampal tail were more obvious in patients with bvFTD and OCB. Right subfield volumes over the CA1 body and molecular layer of the HP body were more significantly reduced in bvFTD patients with OCB than in those in HCs. We observed no significant difference in amygdalar nuclei volume between the groups. Among patients with bvFTD and OCB, Y-BOCS score was negatively correlated with left CA2/3 body volume (τb = −0.729, p < 0.001); total NPI score was negatively correlated with left GC-ML-DG body (τb = −0.648, p = 0.001) and total bilateral hippocampal volumes (left, τb = −0.629, p = 0.002; right, τb = −0.455, p = 0.023); and FBI score was negatively correlated with the left molecular layer of the HP body (τb = −0.668, p = 0.001), CA4 body (τb = −0.610, p = 0.002), and hippocampal tail volumes (τb = −0.552, p < 0.006). Mediation analysis confirmed these subfield volumes as direct biomarkers for clinical severity, independent of medial and lateral orbitofrontal volumes. (5) Conclusions: Alterations in hippocampal subfield volumes appear to be crucial in the pathophysiology of OCB development in patients with bvFTD