Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Curiosity for Real-Time Training Onboard Robotic Agents

Learning requires both study and curiosity. A good learner is not only good at extracting information from the data given to it, but also skilled at finding the right new information to learn from. This is especially true when a human operator is required to provide the ground truth—such a source should only be queried sparingly. In this work, we address the problem of curiosity as it relates to online, real-time, human-in-the-loop training of an object detection algorithm onboard a robotic platform, one where motion produces new views of the subject. We propose a deep reinforcement learning approach that decides when to ask the human user for ground truth, and when to move. Through a series of experiments, we demonstrate that our agent learns a movement and request policy that is at least 3x more effective at using human user interactions to train an object detector than untrained approaches, and is generalizable to a variety of subjects and environments.
General Transcription Factor IIA-γ Increases Osteoblast-specific Osteocalcin Gene Expression via Activating Transcription Factor 4 and Runt-related Transcription Factor 2*

ATF4 (activating transcription factor 4) is an osteoblast-enriched transcription factor that regulates terminal osteoblast differentiation and bone formation. ATF4 knock-out mice have reduced bone mass (severe osteoporosis) throughout life. Runx2 (runt-related transcription factor 2) is a runt domain-containing transcription factor that is essential for bone formation during embryogenesis and postnatal life. In this study, we identified general transcription factor IIAγ (TFIIAγ) as a Runx2-interacting factor in a yeast two-hybrid screen. Immunoprecipitation assays confirmed that TFIIAγ interacts with Runx2 in osteoblasts and when coexpressed in COS-7 cells or using purified glutathione S-transferase fusion proteins. Chromatin immunoprecipitation assay of MC3T3-E1 (clone MC-4) preosteoblast cells showed that in intact cells TFIIAγ is recruited to the region of the osteocalcin promoter previously shown to bind Runx2 and ATF4. A small region of Runx2 (amino acids 258–286) was found to be required for TFIIAγ binding. Although TFIIAγ interacts with Runx2, it does not activate Runx2. Instead, TFIIAγ binds to and activates ATF4. Furthermore, TFIIAγ together with ATF4 and Runx2 stimulates osteocalcin promoter activity and endogenous mRNA expression. Small interfering RNA silencing of TFIIAγ markedly reduces levels of endogenous ATF4 protein and Ocn mRNA in osteoblastic cells. Overexpression of TFIIAγ increases levels of ATF4 protein. Finally, TFIIAγ significantly prevents ATF4 degradation. This study shows that a general transcription factor, TFIIAγ, facilitates osteoblast-specific gene expression through interactions with two important bone transcription factors ATF4 and Runx2.
A dual‐function sRNA from B. subtilis: SR1 acts as a peptide encoding mRNA on the gapA operon

Small non‐coding RNAs (sRNAs) have been found to regulate gene expression in all three kingdoms of life. So far, relatively little is known about sRNAs from Gram‐positive bacteria. SR1 is a regulatory sRNA from the Bacillus subtilis chromosome that inhibits by base‐pairing translation initiation of ahrC mRNA encoding a transcriptional activator of the arginine catabolic operons. Here we present a novel target of SR1, the glycolytic gapA operon. Both microarray and Northern blot analyses show that the amount of gapA operon mRNA is significantly higher in the presence of SR1 when cells were grown in complex medium until stationary phase. Translational lacZ fusions and toeprinting analyses demonstrate that SR1 does not promote translation of gapA mRNA. By contrast, the half‐life of gapA operon mRNA is strongly reduced in the sr1 knockout strain. SR1 does not act as a base‐pairing sRNA on gapA operon mRNA. Instead, we demonstrate that the 39 aa peptide encoded by SR1, SR1P, is responsible for the effect of SR1 on the gapA operon. We show that SR1P binds GapA, thereby stabilizing the gapA operon mRNA by a hitherto unknown mechanism. SR1 is the first dual‐function sRNA found in B. subtilis.
An Improved Method to Transform Triangular Fuzzy Number Into Basic Belief Assignment in Evidence Theory

Dempster-Shafer evidence theory (D-S theory) is a developing theory to solve the uncertain problems and it has an important impact on many fields such as information fusion, expert systems, and machine learning. One of the main points of D-S theory is about how to generate a reliable basic belief assignment. Furthermore, the data collected from the multi-sources may be influenced by noise or other factors which cause conflicts in practical applications. Since the fuzzy number is useful to construct the target model for generating basic belief assignments, in this paper, an improved method to obtain basic belief assignment is proposed based on the triangular fuzzy number and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means++ algorithm. First, the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means++ clustering method is used to construct the target model. Then, the difference degree between the target model and sample model is calculated to generate the initial basic belief assignments. After that, the conflicts will be resolved by using the discount coefficient method. Finally, Dempster’s combination rule is used to combine initial basic belief assignments to obtain the final result. The applications in recognition problems of the Iris data set and Wine Quality data set illustrate that the proposed method is effective to generate the basic belief assignments and keeps a high recognition rate even under a noisy environment.
Electrical Stability of Solution-Processed a-IGZO TFTs Exposed to High-Humidity Ambient for Long Periods

The variations in the electrical and mechanical properties of solution-processed amorphous indium–gallium–zinc–oxide thin-film transistors exposed to high-humidity ambient conditions for long periods were analyzed. When the TFT was exposed to high-humidity conditions, field-effect mobility severely decreased, while ON/OFF current ratio improved and subthreshold slope value remained nearly constant, which is different from that exposed to low-humidity condition. We found that the H2O molecules induce mechanical peeling of the active layer such that they act as acceptor-like deep states, which is very different from the prior results under low humidity condition. The variations in electrical characteristics were systematically analyzed using a technology-CAD simulation before and after exposure to highhumidity conditions.
The Role of AR and VR Technologies in Education Developments: Opportunities and Challenges

Technology has been growing fast and noticeably influencing different aspects of life such as education. Studies have revealed that (AR) and virtual reality (VR) have strong potentials for helping students to improve their skills and knowledge. In fact, bridging AR/VR and education can bring teaching and learning experiences in an attractive and effective way.In this review paper, we initially present an introduction to and a definition of AR/VR. We then briefly study ongoing research and latest products in AR/VR, that have pedagogical values and potentials to improve educational systems. We then highlight the capabilities and limitations of AR/VR to identify what AR/VR can provide for learners and teachers.
An Empirical Investigation on a Pedagogical Pattern Language for Mobile Learning Applications

This Research to Practice Full Paper presents an empirical investigation of a pedagogical pattern language for mobile learning apps. Mobile learning (m-learning) applications can provide several benefits to learners. However, there are still several problems to be further investigated, such as the challenge to keep the learner motivated while using the application during educational activities. The application design can help to solve this kind of problems. There is a need to understand such problems in the pedagogical level and then properly eliciting requirements that would address those problems. Pedagogical patterns try to capture expert knowledge regarding the practice of teaching and learning in a way that it is possible for others to reuse this experience. Therefore, pedagogical patterns can be a tool to assist in the design of new m-learning applications as well as to the improvement of the existing ones. Aiming to bridge this gap, a pedagogical pattern language entitled MLearning-PL has been proposed to guide the requirements elicitation of m-learning applications projects. We conducted an empirical investigation in order to evaluate the proposed pattern language by means of a experimental study, whose main purpose was to evaluate the solutions provided using MLearning-PL in comparison to an ad hoc approach. A feedback questionnaire was also applied to understand the participants’ perceptions about MLearning-PL on its clearness and completeness. The obtained results provided preliminary evidences of the applicability, effectiveness and efficiency of MLearning-PL.
Camera Identification Based on Domain Knowledge-Driven Deep Multi-Task Learning

Camera identification has recently attracted considerable attention in the image forensic field of research. Several algorithms have been established based on the hand-crafted features and deep learning, through analysis of the traces achieved by the digital image acquisition process. Although these approaches have led to a breakthrough in the image forensics, some important problems still remain unsolved. For instance, extracting the hand-crafted features with human efforts is a difficult and time-consuming process, while data-driven deep learning methods tend to learn features that represent image contents rather than cameras’ characteristics. To fully take advantages of both hand-crafted and data-driven technologies, we propose a domain knowledge-driven method, which consists of one pre-processing module, one feature extractor, and one hierarchical multi-task learning procedure. The pre-processing module can introduce the domain knowledge to the subsequent deep learning network. Moreover, for device-level identification, hierarchical multi-task learning can provide more supervise information from the brand and model. The proposed framework is evaluated on three different tasks, i.e., the brand, model, and device-level identification using original and manipulated images. Our classification results demonstrate that the proposed method is effective and robust. To evaluate the robustness of the proposed method, we create a new database for the cell-phone identification and evaluate the proposed method. It is found that the accuracy of the cell-phone device identification can reach 84.3%, which is much higher than that of the camera identification. Moreover, the t-distributed stochastic neighbor embedding visualization results confirm that the features of different cell-phone devices are visually more separable than cameras.
Observability Analysis and Performance Evaluation of EKF-Based Visual-Inertial Odometry With Online Intrinsic Camera Parameter Calibration

In this paper, we focus on the problem of online intrinsic camera parameter calibration for a visual-inertial system. Imprecise intrinsic camera parameters will result in unreliable pose estimation or even cause estimator divergence. Specifically, we present a nonlinear observability analysis of the system and prove that there are four unobservable directions spanning the right nullspace of the observability matrix, i.e., the rotation about the gravity vector and the positions in the global frame. We propose an extended Kalman filter-based visual-inertial odometry method for calibrating intrinsic camera parameters while estimating the pose simultaneously. The observability properties and the performance of the estimator are validated using both the simulated and real-world datasets.
Small‐molecule activators of TMEM16A, a calcium‐activated chloride channel, stimulate epithelial chloride secretion and intestinal contraction

TMEM16A (ANO1) is a calcium‐activated chloride channel (CaCC) expressed in secretory epithelia, smooth muscle, and other tissues. Cell‐based functional screening of ~110,000 compounds revealed compounds that activated TMEM16A CaCC conductance without increasing cytoplasmic Ca2+. By patch‐clamp, N‐aroylaminothiazole “activators” (Eact) strongly increased Cl‐ current at 0 Ca2+, whereas tetrazolylbenzamide “potentiators” (Fact) were not active at 0 Ca2+ but reduced the EC50 for Ca2+‐dependent TMEM16A activation. Of 682 analogs tested, the most potent activator (Eact) and potentiator (Fact) produced large and more sustained CaCC Cl‐currents than general agonists of Ca2+ signaling, with EC50 3–6 μM and Cl‐ conductance comparable to that induced transiently by Ca2+‐elevating purinergic agonists. Analogs of activators were identified that fully inhibited TMEM16A Cl‐ conductance, providing further evidence for direct TMEM16A binding. The TMEM16A activators increased CaCC conductance in human salivary and airway submucosal gland epithelial cells, and IL‐4 treated bronchial cells, and stimulated submucosal gland secretion in human bronchi and smooth muscle contraction in mouse intestine. Small‐molecule, TMEM16A‐targeted activators may be useful for drug therapy of cystic fibrosis, dry mouth, and gastrointestinal hypomotility disorders, and for pharmacological dissection of TMEM16A function. —Namkung, W. , Yao, Z. , Finkbeiner, W. E. , Verkman, A. S. Small‐molecule activators of TMEM16A, a calcium‐activated chloride channel, stimulate epithelial chloride secretion and intestinal contraction. FASEB J. 25, 4048–4062 (2011). www.fasebj. org
Where to Focus on for Human Action Recognition?

In this paper, we present a new attention model for the recognition of human action from RGB-D videos. We propose an attention mechanism based on 3D articulated pose. The objective is to focus on the most relevant body parts involved in the action. For action classification, we propose a classification network compounded of spatio-temporal subnetworks modeling the appearance of human body parts and RNN attention subnetwork implementing our attention mechanism. Furthermore, we train our proposed network end-to-end using a regularized cross-entropy loss, leading to a joint training of the RNN delivering attention globally to the whole set of spatio-temporal features, extracted from 3D ConvNets. Our method outperforms the State-of-the-art methods on the largest human activity recognition dataset available to-date (NTU RGB+D Dataset) which is also multi-views and on a human action recognition dataset with object interaction (Northwestern-UCLA Multiview Action 3D Dataset).
Scalable Reconfigurable Memristive Synaptic Structures as a Basis for the Plasticity Mechanisms in Developing and Self-organizing Networks of Artificial Pulsed Neurons

Of particular importance is the property of plasticity for networks of large dimension and scale, since the large dimension of the network is a necessary condition for the processes of self – organization and for "intellectual" data processing. However, the hardware implementation of this property is rather complicated. Using functional micro and nano-elements with memristive properties is a justified way of achieving the required high degree of reconfigurable synaptic structures integration and scale. Analysis of the chemical and biological mechanisms of STP and LTPl confirms the hypothesis of their relative independence and parallel learning of biological neural systems at different time scales. In the paper, the expediency of spatial and temporal scaling of artificial synaptic structures based on memristive elements, functioning on different physical principles, is proved.
Distributed Generator in Grid-Connected Mode Using Improved Exponential Sliding Mode Control

This paper shows the operation of the distributed generation system using an innovative control scheme. The core of this distributed generation system is the distributed generator, which is based on the 5-level diode clamped inverter. This distributed generator is operated by an improved version of the exponential sliding mode control. This developed sliding mode control perfectly fits the power electronics based distributed generator to control the output injected power, and it gives the shortest reaching time with much less chattering at its output power. The performance of the whole suggested control scheme along with the improved sliding mode controller is verified using simulation results.
Loss of the actin‐like protein MamK has pleiotropic effects on magnetosome formation and chain assembly in Magnetospirillum gryphiswaldense

Magnetotactic bacteria synthesize magnetosomes, which are unique organelles consisting of membrane‐enclosed magnetite crystals. For magnetic orientation individual magnetosome particles are assembled into well‐organized chains. The actin‐like MamK and the acidic MamJ proteins were previously implicated in chain assembly. While MamK was suggested to form magnetosome‐associated cytoskeletal filaments, MamJ is assumed to attach the magnetosome vesicles to these structures. Although the deletion of either mamK in Magnetospirillum magneticum, or mamJ in Magnetospirillum gryphiswaldense affected chain formation, the previously observed phenotypes were not fully consistent, suggesting different mechanisms of magnetosome chain assembly in both organisms. Here we show that in M. gryphiswaldense MamK is not absolutely required for chain formation. Straight chains, albeit shorter, fragmented and ectopic, were still formed in a mamK deletion mutant, although magnetosome filaments were absent as shown by cryo‐electron tomography. Loss of MamK also resulted in reduced numbers of magnetite crystals and magnetosome vesicles and led to the mislocalization of MamJ. In addition, extensive analysis of wild type and mutant cells revealed previously unidentified ultrastructural characteristics in M. gryphiswaldense. Our results suggest that, despite of their functional equivalence, loss of MamK proteins in different bacteria may result in distinct phenotypes, which might be due to a species‐specific genetic context.
Retinoid receptors trigger neuritogenesis in retinal degenerations

Anomalous neuritogenesis is a hallmark of neurodegenerative disorders, including retinal degenerations, epilepsy, and Alzheimer's disease. The neuritogenesis processes result in a partial reinnervation, new circuitry, and functional changes within the deafferented retina and brain regions. Using the light‐induced retinal degeneration (LIRD) mouse model, which provides a unique platform for exploring the mechanisms underlying neuritogenesis, we found that retinoid X receptors (RXRs) control neuritogenesis. LIRD rapidly triggered retinal neuron neuritogenesis and up‐regulated several key elements of retinoic acid (RA) signaling, including retinoid X receptors (RXRs). Exogenous RA initiated neuritogenesis in normal adult retinas and primary retinal cultures and exacerbated it in LIRD retinas. However, LIRD‐induced neuritogenesis was partly attenuated in retinol dehydrogenase knockout (Rdh12−/−) mice and by aldehyde dehydrogenase inhibitors. We further found that LIRD rapidly increased the expression of glutamate receptor 2 and β Ca2+/ calmodulin‐dependent protein kinase II (pCaMKII). Pulldown assays demonstrated interaction between PCaMKII and RXRs, suggesting that CaMKII pathway regulates the activities of RXRs. RXR antagonists completely prevented and RXR agonists were more effective than RA in inducing neuritogenesis. Thus, RXRs are in the final common path and may be therapeutic targets to attenuate retinal remodeling and facilitate global intervention methods in blinding diseases and other neurodegenerative disorders. —Lin, Y. , Jones, B. W. , Liu, A. , Tucker, J. F. , Rapp, K. , Luo, L. , Baehr, W. , Bernstein, P. S. , Watt, C. B. , Yang, J.‐H. , Shaw, M. V. , Marc, R. E. Retinoid receptors trigger neuritogenesis in retinal degenerations. FASEB J. 26, 81–92 (2012). www.fasebj. org
Natural Scenes Datasets for Exploration In 6DOF Navigation

Depth Image-Based Rendering (DIBR) and 6 Degrees-of-Freedom (6DoF) navigation are actually very active fields for immersive video, where the availability of common test conditions is mandatory to compare results. For this purpose, we propose different datasets to evaluate a wide range of tools needed by free navigation: a high-density HD image set, composed of 80k camera positions covering the entire 3D space, necessary to simulate all the head rotations and translations of a common user, and a UHD video set, composed of two sequences acquired with 3 by 5 cameras array. All the processing pipeline to obtain quality results is also detailed.
Pansharpening Multispectral Images Based on Unconstrained Least Square Spectral Unmixing

A new fusion framework for pansharpening multi-spectral (MS) image is suggested in this paper. The introduced method relies on linear spectral unmixing, and employs unconstrained least square (ULS) estimation for combining the high spatial features of the panchromatic (PAN) image and the rich spectral features of the MS image (MSI), in one single high spatial-spectral image. Applied to real coincident MS and PAN data sets with different texture complexity, the proposed approach can fuse the spatial and the spectral characteristics in a single image with the minimum of spectral distortion in comparison to some famous methods. Moreover, incorporating the ULS in the unmixing process is quite simple to formulate and straightforward to implement.
An Image Segmentation Method Based on Luminance Distribution and Its Application to Image Enhancement

This paper proposes a novel image segmentation method based on luminance distribution and its application to image enhancement. Many existing image segmentation methods focus on semantic segmentation which separates an image into some meaningful areas. However, those segmentation methods are not effective for image enhancement. The proposed segmentation method separates an image into some areas according to luminance values of pixels. To obtain those areas, the proposed method utilizes a clustering algorithm based on a Gaussian mixture model which is fit by using a variational Bayesian approach. By using the proposed segmentation method, an automatic exposure compensation method is also proposed. The proposed exposure compensation method enables to automatically produce pseudo multi-exposure images from a single image and to improve the image quality by fusing them. Experimental results show that the proposed segmentation method is effective for image enhancement. In addition, the image enhancement method using the proposed segmentation method outperforms state-of-the-art contrast enhancement methods, in terms of the entropy and statistical naturalness.
Folding and Misfolding in a Naturally Occurring Circularly Permuted PDZ Domain*

One of the most extreme and fascinating examples of naturally occurring mutagenesis is represented by circular permutation. Circular permutations involve the linking of two chain ends and cleavage at another site. Here we report the first description of the folding mechanism of a naturally occurring circularly permuted protein, a PDZ domain from the green alga Scenedesmus obliquus. Data reveal that the folding of the permuted protein is characterized by the presence of a low energy off-pathway kinetic trap. This finding contrasts with what was previously observed for canonical PDZ domains that, although displaying a similar primary structure when structurally re-aligned, fold via an on-pathway productive intermediate. Although circular permutation of PDZ domains may be necessary for a correct orientation of their functional sites in multi-domain protein scaffolds, such structural rearrangement may compromise their folding pathway. This study provides a straightforward example of the divergent demands of folding and function.