Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic dataset generation for object pose estimation

Object pose estimation based on a RGB image is essential in accomplishing many computer vision tasks, such as augmented reality and robot vision for grasping. Using structure from motion and domain randomization, we propose a method that, from a set of images, allows us to quickly generate large datasets to train a Convolutional Neural Network (ConvNet) for object pose estimation.
Interaction system with mid-air CG character that has own eyes

This paper proposes an optical system that can capture a user from the viewpoint of a mid-air CG character. The mid-air imaging system enables us to display a CG character in real space. In order for users to interact with this character, we must observe their behavior from the character's viewpoint. Therefore, we propose a method of capturing from the mid-air image position by arranging a light source display and a camera at a conjugate position using a half mirror, optically transferring them with micro-mirror array plates. The contribution of this system is capturing the full face of a user from the position of the mid-air image.
Timing Prediction for Dynamic Application Migration on Multi-core Embedded Systems

To accommodate execution mode change and hardware malfunction, dynamic system reconfiguration, which invokes application migration across different processing cores, needs to be supported on multi-core embedded systems. Different application migration strategies will impact system's timing behaviors in different manners, it is important to select an appropriate one such that the system's timing performance after the migration process is still acceptable. The focus of our research is to predict the system's timing change of possible migration strategies and upon which to choose the optimal one. Extensive experiments have been set up by running multiple benchmarks and experimental results validate the effectiveness of our proposed approach. "
The Clinical Value of Autoantibodies in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a highly heterogeneous syndrome in terms of clinical presentation, progression, and response to therapy. In such a complicated context, the identification of disease-related biomarkers would be undoubtedly helpful in assisting tailored approaches for every patient. Despite remarkable efforts, however, progress in new biomarker development and validation is dramatically slow. At present, none of the candidate genetic, cellular, or molecular biomarker has yet surpassed the clinical value of RA-specific autoantibodies, including rheumatoid factor (RF) and anti-citrullinated protein autoantibodies (ACPA). Rather, recent years have witnessed significant advancements in our understanding of the multiple roles that RF and ACPA play in RA pathophysiology. This has helped clarifying the mechanistic basis of the clinical associations of autoantibodies in RA. In this short review, we will briefly summarize the effector functions of RF and ACPA, and analyse how autoantibodies may help subclassifying RA patients in terms of clinical presentation and response to therapy.
Efficacy of Prednisolone in Generated Myotubes Derived From Fibroblasts of Duchenne Muscular Dystrophy Patients

Duchenne muscular dystrophy (DMD) is a recessive X-linked form of muscular dystrophy characterized by progressive muscle degeneration. This disease is caused by the mutation or deletion of the dystrophin gene. Currently, there are no effective treatments and glucocorticoid administration is a standard care for DMD. However, the mechanism underlying prednisolone effects, which leads to increased walking, as well as decreased muscle wastage, is poorly understood. Our purpose in this study is to investigate the mechanisms of the efficacy of prednisolone for this disease. We converted fibroblasts of normal human cell line and a DMD patient sample to myotubes by MyoD transduction using a retroviral vector. In myotubes from the MyoD-transduced fibroblasts of the DMD patient, the myotube area was decreased and its apoptosis was increased. Furthermore, we confirmed that prednisolone could rescue these pathologies. Prednisolone increased the expression of not utrophin but laminin by down-regulation of MMP-2 mRNA. These results suggest that the up-regulation of laminin may be one of the mechanisms of the efficacy of prednisolone for DMD.
Neural Correlates of Social Inclusion in Borderline Personality Disorder

Humans engage in social interactions and have a fundamental need and motivation to establish and maintain social connections. Neuroimaging studies particularly focused on the neural substrates of social exclusion in healthy subjects (HC), borderline personality disorder (BPD), and major depression (MD). However, there is evidence regarding neural alterations also during social inclusion in BPD that we intended to elucidate in our study. Considering that patients with BPD often have comorbid MD, we investigated patients with BPD, and comorbid MD, patients with MD without BPD, and a sample of HC. By investigating these two clinical samples within one study design, we attempted to disentangle potential confounds arising by psychiatric disorder or medication and to relate neural alterations under social inclusion specifically to BPD. We investigated 48 females (15 BPD and MD, 16 MD, and 17 HC) aged between 18 and 40 years by fMRI (3T), using the established cyberball paradigm with social exclusion, inclusion, and passive watching conditions. Significant group-by-condition interaction effects (p < 0.05, FWE-corrected on cluster level) were observed within the dorsolateral (dlPFC) and dorsomedial prefrontal cortex (dmPFC), the temporo-parietal junction (TPJ), the posterior cingulate cortex (PCC), and the precuneus. Comparisons of estimated neural activations revealed that significant interaction effects were related to a relative increase in neural activations during social inclusion in BPD. In detail, we observed a significant increase in differential (social inclusion vs. passive watching) neural activation within the dmPFC and the PCC in BPD compared to both, MD and HC. However, significant interaction effects within the dlPFC and the TPJ could not specifically be linked to BPD considering that they did not differ significantly between the two clinical groups in post-hoc comparisons. Our study supports previous results on effects of social and inclusion in BPD, and provides further evidence regarding disorder specific neural alterations in BPD for brain regions associated with self-referential and mentalizing processes during social inclusion.
A Novel Approach of Identifying Immunodominant Self and Viral Antigen Cross-Reactive T Cells and Defining the Epitopes They Recognize

Infection and vaccination can lead to activation of autoreactive T cells, including the activation of cross-reactive T cells. However, detecting these cross-reactive T cells and identifying the non-self and self-antigen epitopes is difficult. The current study demonstrates the utility of a novel approach that effectively accomplishes both. We utilized surface expression of CD38 on newly activated CD4 memory T cells as a strategy to identify type 1 diabetes associated autoreactive T cells activated by influenza vaccination in healthy subjects. We identified an influenza A matrix protein (MP) specific CD4+ T cell clone that cross-recognizes an immunodominant epitope from Glutamic Acid Decarboxylase 65 (GAD65) protein. The sequences of the MP and GAD65 peptides are rather distinct, with only 2 identical amino acids within the HLA-DR binding region. This result suggests that activation of autoreactive T cells by microbial infection under certain physiological conditions can occur amongst peptides with minimum amino acid sequence homology. This novel strategy also provides a new research pathway in which to examine activation of autoreactive CD4+ T cells after vaccination or natural infection.
Beltrami-net: domain-independent deep D-bar learning for absolute imaging with electrical impedance tomography (a-EIT)

Objective: To develop, and demonstrate the feasibility of, a novel image reconstruction method for absolute electrical impedance tomography (a-EIT) that pairs deep learning techniques with real-time robust D-bar methods and examine the influence of prior information on the reconstruction. Approach: A D-bar method is paired with a trained convolutional neural network (CNN) as a post-processing step. Training data is simulated for the network using no knowledge of the boundary shape by using an associated nonphysical Beltrami equation rather than simulating the traditional current and voltage data specific to a given domain. This allows the training data to be boundary shape independent. The method is tested on experimental data from two EIT systems (ACT4 and KIT4) with separate training sets of varying prior information. Main results: Post-processing the D-bar images with a CNN produces significant improvements in image quality measured by structural SIMilarity indices (SSIMs) as well as relative and image errors. Significance: This work demonstrates that more general networks can be trained without being specific about boundary shape, a key challenge in EIT image reconstruction. The work is promising for future studies involving databases of anatomical atlases.
VarioLight: hybrid dynamic projection mapping using high-speed projector and optical axis controller

Projection mapping have attracted much attention in recent years and many researches have tried to expand it to dynamic objects. However, it has some problems in the range of motion, the resolution, and the kinds of object that can be projected. In this paper, we propose a method for realizing dynamic projection mapping by combining a high-speed/low-latency projector and a mirror-based high-speed optical axis controller. Based on high-speed visual feedback with multiple dot markers, dynamic projection mapping onto rotating/deforming objects moving in a wide range, with almost-imperceptible delay, has become possible.
Color enhancement factors to control spectral power distribution of illumination

We introduced color enhancement factors to control the spectral power distribution of illumination, which enabled us to enhance one or more colors at once while retaining the color appearance of white. In experiments, color enhancement factors corresponding to red, green, and blue were calculated using color patches of a color chart and used for controlling a sixteen-color LED lighting system. The color chart and an old wood-block print were illuminated by the modulated light from the lighting system. By changing only three parameters, each color was enhanced continuously and independently with metameric white and the color balance under daylight maintained.
Automatic generation of interactive projection mapping for leaves

Recently, the targets of projection mapping have included various objects ranging from buildings to fish. However, it is difficult to project onto dynamic and deformable objects, such as leaves. Therefore, we propose a semi-automatic system to calculate the image registrations of projections for leaves and to interactively track the projection area. We describe our results with some animated effects on various shapes of leaves (Fig. 1).
Deep Multi-Agent Reinforcement Learning with Relevance Graphs

Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains. In this paper, we propose a novel approach, called MAGnet, to multi-agent reinforcement learning (MARL) that utilizes a relevance graph representation of the environment obtained by a self-attention mechanism, and a message-generation technique inspired by the NerveNet architecture. We applied our MAGnet approach to the Pommerman game and the results show that it significantly outperforms state-of-the-art MARL solutions, including DQN, MADDPG, and MCTS.
Large-Scale and Fine-Grained Evaluation of Popular JPEG Forgery Localization Schemes

Over the years, researchers have proposed various approaches to JPEG forgery detection and localization. In most cases, experimental evaluation was limited to JPEG quality levels that are multiples of 5 or 10. Each study used a different dataset, making it difficult to directly compare the reported results. The goal of this work is to perform a unified, large-scale and fine-grained evaluation of the most popular state-of-the-art detectors. The obtained results allow to compare the detectors with respect to various criteria, and shed more light on the compression configurations where reliable tampering localization can be expected.
Selective Metaphor Impairments After Left, Not Right, Hemisphere Injury

The relative contributions of the left and right hemispheres to the processing of metaphoric language remains unresolved. Neuropsychological studies of brain-injured patients have motivated the hypothesis that the right hemisphere plays a critical role in understanding metaphors. However, the data are inconsistent and the hypothesis is not well-supported by neuroimaging research. To address this ambiguity about the right hemisphere’s role, we administered a metaphor sentence comprehension task to 20 left-hemisphere injured patients, 20 right hemisphere injured patients, and 20 healthy controls. Stimuli consisted of metaphors of three different types: predicate metaphors based on action verbs, nominal metaphors based on event nouns, and nominal metaphors based on entity nouns. For each metaphor (n = 60), a closely matched literal sentence with the same source term was also generated. Each sentence was followed by four adjective–noun answer choices (target + three foil types) and participants were instructed to select the phrase that best matched the meaning of the sentence. As a group, both left and right hemisphere patients performed worse on metaphoric than literal sentences, and the degree of this difficulty varied for the different types of metaphor – but there was no difference between the two patient groups. Tests for literal-metaphor dissociations at the level of single cases revealed two types of impairments: general comprehension deficits affecting metaphors and literal sentences equally, and selective metaphor impairments that were specific to different types of metaphor. All cases with selective metaphor deficits had injury to the left hemisphere, and no known comprehension difficulties with literal language. Our results argue against the hypothesis of a specific or necessary contribution of the right hemisphere for understanding metaphoric language. Further, they reveal deficits in metaphoric language comprehension not captured by traditional language assessments, suggesting overlooked communication difficulties in left hemisphere patients.
Text Summarization of Spanish Documents

Text Summarization is the process of gathering the thought and line of thought from a unique content and teaching the same into a short sound content. Mechanized content synopsis plans to meet this goal of holding all the key thoughts imparted in the content while skipping upon the excess and monotonous bits of data. The decreased content therefore accumulated must be cognizant in itself with a specific end goal to encounter the syntactic and semantic association of a language. Here an attempt is made to introduce an extraction based programmed content synopsis calculation. The procedure proposed includes developing of a coordinated weighted chart out of the first content. A position calculation is utilized to register the most critical sentences in the content and that ought to be available in the synopsis in light of the weighted diagram. The main aim of the project is to take news articles and give a short summary of the article computationally keeping in mind that no significant information is left out in the summary.
SwishNet: A Fast Convolutional Neural Network for Speech, Music and Noise Classification and Segmentation

Speech, Music and Noise classification/segmentation is an important preprocessing step for audio processing/indexing. To this end, we propose a novel 1D Convolutional Neural Network (CNN) - SwishNet. It is a fast and lightweight architecture that operates on MFCC features which is suitable to be added to the front-end of an audio processing pipeline. We showed that the performance of our network can be improved by distilling knowledge from a 2D CNN, pretrained on ImageNet. We investigated the performance of our network on the MUSAN corpus - an openly available comprehensive collection of noise, music and speech samples, suitable for deep learning. The proposed network achieved high overall accuracy in clip (length of 0.5-2s) classification (>97% accuracy) and frame-wise segmentation (>93% accuracy) tasks with even higher accuracy (>99%) in speech/non-speech discrimination task. To verify the robustness of our model, we trained it on MUSAN and evaluated it on a different corpus - GTZAN and found good accuracy with very little fine-tuning. We also demonstrated that our model is fast on both CPU and GPU, consumes a low amount of memory and is suitable for implementation in embedded systems.
Design and Provision of Traffic Grooming for Optical Wireless Data Center Networks

Traditional wired data center networks (DCNs) suffer from cabling complexity, lack flexibility, and are limited by the speed of digital switches. In this paper, we alternatively develop a top-down traffic grooming (TG) approach to the design and provisioning of mission-critical optical wireless DCNs. While switches are modeled as hybrid optoelectronic cross-connects, links are modeled as wavelength division multiplexing capable free-space optic channels. Using the standard TG terminology, we formulate the optimal mixed-integer TG problem considering the virtual topology, flow conversation, connection topology, non-bifurcation, and capacity constraints. Thereafter, we develop a fast yet efficient sub-optimal solution, which grooms mice flows (MFs), mission-critical flows (CFs), and forward on predetermined rack-to-rack (R2R) lightpaths. On the other hand, elephant flows (EFs) are forwarded over dedicated server-to-server express lightpaths whose routes and capacity are dynamically determined based on the availability of wavelength and capacity. To prioritize the CFs, we consider low and high-priority queues and analyze the delay characteristics such as waiting times, maximum hop counts, and blocking probability. As a result of grooming, the sub-wavelength traffic and adjusting the wavelength capacities, numerical results show that the proposed solutions can achieve significant performance enhancement by utilizing the bandwidth more efficiently, completing the flows faster than delay sensitivity requirements, and avoiding the traffic congestion by treating EFs and MFs separately.
Adaptive In-network Guidance Dissemination from Hot-Spot Nodes of Queries in Breadcrumbs-Based Information-Centric Networks

For several years, server access load and network traffic have greatly increased in content distribution networks because of growing content-sizes and higher request-frequencies. Breadcrumbs (BC) is an architecture that provides guidance information that directs a user to a node holding cached content. The target contents retrieved by this approach have the generalized meanings; videos, functions (programs), files, and any kinds of data, which are duplicated inside network, and also computing resources like CPU time and memory space. The BC is designed for reducing the loads of original servers and for forming autonomous information-centric networks. We have proposed extended methods for BC, called, “Active BC” and “User-Side Flooding,’ These methods provided higher nearby user-cache utilization. However, there still remains technical challenges to enhance BC hit ratio for queries, whereby they often could not provide sufficient guidance information. Thus, in this paper, we develop a BC method that adaptively creates new guidance information from hot-spot nodes to which more queries are routed.
Communication-Efficient Computation Load Scheduling for Edge Computing Systems

In this paper, we develop a stochastic communication-efficient computation load scheduling framework to complete the computation tasks with coded MapReduce considering the intrinsic tradeoff between the communication and computation loads. Our goal is to minimize the communication load under time-varying excess computational resources. We first reduce this problem to a task scheduling problem by exploiting the property of the computing repetition in the coded MapReduce framework. Since the task scheduling problem is still a stochastic optimization problem, it is generally difficult to solve. In the offline setting, we obtain the optimal computation load scheduling algorithm by adopting the augmented Lagrangian method. In the online setting, we derive a lower bound on competitive ratio over all online algorithms by competitive analysis. Furthermore, we make full use of past state information of computing resources to pre-planing and proposed improvements based on the above algorithm in a learning manner. Finally, our proposed algorithm is evaluated by simulation to demonstrate that the performance gap between the online and offline algorithms is fairly small and to demonstrate the superiority of the proposed algorithms over the conventional algorithms.
Botnet Detection based on Fuzzy Association Rules

Difficult to be detected in complex network environments, botnets have been huge threats to network security. As the circumscriptions of normal traffics and botnet traffics are blurring, the commonly used botnet detection methods based on traffic analysis often result in high false positive rates. To overcome this issue, we propose an effective botnet detection method based on fuzzy association rules. The proposed method can calculate the features of botnet traffic accurately, which can be used to recognize the normal traffic and botnet. We first collect the data in the laboratory by setting different botnets in the controlled experiment. The botnet traffic features, association rules support, trust and membership are calculated by the proposed method, which are further used to distinguish the type of botnet. When our method is compared with other methods in our data set, we find the former performs better. For the generality, we also test our method on the public data set and also find the higher accuracy rates, which demonstrates the proposed method is effective in detecting the botnets.
Device-free Crowd Sensing in Dense WiFi MIMO Networks: Channel Features and Machine Learning Tools

The paper addresses the problem of passive crowd sensing in an indoor space by processing baseband radio signals originated from a dense WiFi network. Focusing on unmodified WiFi devices equipped with multi-antenna OFDM physical radio interfaces (IEEE 802.11n/ac), we investigate the selection of statistical features to measure the body-induced alterations of the channel state information (CSI) and we analyze their dependency over the space (antennas) and frequency domains. Different machine learning methods are compared and optimized to discriminate up to 5 people moving inside the smart space. We compare different solutions for classification and target counting based on feed-forward and recurrent neural networks based on long short term memory architecture (LSTM). Experiments with real subjects are conducted to validate the proposed approach. Results confirm that CSI feature selection is crucial to optimize the counting performance and space-frequency diversity needs to be exploited to provide high-accuracy sensing in complex indoor environments