Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images

Recovering the 3D representation of an object from single-view or multi-view RGB images by deep neural networks has attracted increasing attention in the past few years. Several mainstream works (e.g., 3D-R2N2) use recurrent neural networks (RNNs) to fuse multiple feature maps extracted from input images sequentially. However, when given the same set of input images with different orders, RNN-based approaches are unable to produce consistent reconstruction results. Moreover, due to long-term memory loss, RNNs cannot fully exploit input images to refine reconstruction results. To solve these problems, we propose a novel framework for single-view and multi-view 3D reconstruction, named Pix2Vox. By using a well-designed encoder-decoder, it generates a coarse 3D volume from each input image. Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e.g., table legs) from different coarse 3D volumes to obtain a fused 3D volume. Finally, a refiner further refines the fused 3D volume to generate the final output. Experimental results on the ShapeNet and Pix3D benchmarks indicate that the proposed Pix2Vox outperforms state-of-the-arts by a large margin. Furthermore, the proposed method is 24 times faster than 3D-R2N2 in terms of backward inference time. The experiments on ShapeNet unseen 3D categories have shown the superior generalization abilities of our method.Comment: ICCV 201

Analysis of index gases of coal spontaneous combustion using fourier transform infrared spectrometer

Analysis of the index gases of coal for the prevention of spontaneous combustion is of great importance for the enhancement of coal mine safety. In this work, Fourier Transform Infrared Spectrometer (FTIRS) is presented to be used to analyze the index gases of coal in real time to monitor spontaneous combustion conditions. Both the instrument parameters and the analysis method are introduced at first by combining characteristics of the absorption spectra of the target analyte with the analysis requirements. Next, more than ten sets of the gas mixture containing ten components (CH 4 , C 2 H 6 , C 3 H 8 , iso-C 4 H 10 , n-C 4 H 10 , C 2 H 4 , C 3 H 6 , C 2 H 2 , CO, and CO 2 ) are included and analyzed with a Spectrum Two FTIRS made by Perkin Elmer. The testing results show that the detection limit of most analytes is less than 2 × 10 −6 . All the detection limits meet the monitoring requirements of coal spontaneous combustion in China, which means that FTIRS may be an ideal instrument and the analysis method used in this paper is sufficient for spontaneous combustion gas monitoring on-line and even in situ, since FTIRS has many advantages such as fast analysis, being maintenance-free, and good safety

Gait parameter fitting and adaptive enhancement based on cerebral blood oxygen information

Accurate recognition of patients’ movement intentions and real-time adjustments are crucial in rehabilitation exoskeleton robots. However, some patients are unable to utilize electromyography (EMG) signals for this purpose due to poor or missing signals in their lower limbs. In order to address this issue, we propose a novel method that fits gait parameters using cerebral blood oxygen signals. Two types of walking experiments were conducted to collect brain blood oxygen signals and gait parameters from volunteers. Time domain, frequency domain, and spatial domain features were extracted from brain hemoglobin. The AutoEncoder-Decoder method is used for feature dimension reduction. A regression model based on the long short-term memory (LSTM) model was established to fit the gait parameters and perform incremental learning for new individual data. Cross-validation was performed on the model to enhance individual adaptivity and reduce the need for individual pre-training. The coefficient of determination (R2) for the gait parameter fit was 71.544%, with a mean square error (RMSE) of less than 3.321%. Following adaptive enhancement, the coefficient of R2 increased by 6.985%, while the RMSE decreased by 0.303%. These preliminary results indicate the feasibility of fitting gait parameters using cerebral blood oxygen information. Our research offers a new perspective on assisted locomotion control for patients who lack effective myoelectricity, thereby expanding the clinical application of rehabilitation exoskeleton robots. This work establishes a foundation for promoting the application of Brain-Computer Interface (BCI) technology in the field of sports rehabilitation

RSQ: a statistical method for quantification of isoform-specific structurome using transcriptome-wide structural profiling data [preprint]

The structure of RNA, which is considered to be a second layer of information alongside the genetic code, provides fundamental insights into the cellular function of both coding and non-coding RNAs. Several high-throughput technologies have been developed to profile transcriptome-wide RNA structures, i.e., the structurome. However, it is challenging to interpret the profiling data because the observed data represent an average over different RNA conformations and isoforms with different abundance. To address this challenge, we developed an RNA structurome quantification method (RSQ) to statistically model the distribution of reads over both isoforms and RNA conformations, and thus provide accurate quantification of the isoform-specific structurome. The quantified RNA structurome enables the comparison of isoform-specific conformations between different conditions, the exploration of RNA conformation variation affected by single nucleotide polymorphism (SNP) , and the measurement of RNA accessibility for binding of either small RNAs in RNAi-based assays or RNA binding protein in transcriptional regulation. The model used in our method sheds new light on the potential impact of the RNA structurome on gene regulation

Chalcogenide Glass-on-Graphene Photonics

Two-dimensional (2-D) materials are of tremendous interest to integrated photonics given their singular optical characteristics spanning light emission, modulation, saturable absorption, and nonlinear optics. To harness their optical properties, these atomically thin materials are usually attached onto prefabricated devices via a transfer process. In this paper, we present a new route for 2-D material integration with planar photonics. Central to this approach is the use of chalcogenide glass, a multifunctional material which can be directly deposited and patterned on a wide variety of 2-D materials and can simultaneously function as the light guiding medium, a gate dielectric, and a passivation layer for 2-D materials. Besides claiming improved fabrication yield and throughput compared to the traditional transfer process, our technique also enables unconventional multilayer device geometries optimally designed for enhancing light-matter interactions in the 2-D layers. Capitalizing on this facile integration method, we demonstrate a series of high-performance glass-on-graphene devices including ultra-broadband on-chip polarizers, energy-efficient thermo-optic switches, as well as graphene-based mid-infrared (mid-IR) waveguide-integrated photodetectors and modulators

Modularité dans l'apprentissage profond

This Ph.D. thesis is dedicated to enhancing the efficiency of Deep Learning by leveraging the principle of modularity. It contains several main contributions: a literature survey on modularity in Deep Learning; the introduction of OmniPrint and Meta-Album, tools that facilitate the investigation of data modularity; case studies examining the effects of episodic few-shot learning, an instance of data modularity; a modular evaluation mechanism named LTU for assessing privacy risks; and the method RRR for reusing pre-trained modular models to create more compact versions. Modularity, which involves decomposing an entity into sub-entities, is a prevalent concept across various disciplines. This thesis examines modularity across three axes of Deep Learning: data, task, and model. OmniPrint and Meta-Album assist in benchmarking modular models and exploring data modularity's impacts. LTU ensures the reliability of the privacy assessment. RRR significantly enhances the utilization efficiency of pre-trained modular models. Collectively, this thesis bridges the modularity principle with Deep Learning and underscores its advantages in selected fields of Deep Learning, contributing to more resource-efficient Artificial Intelligence.L'objectif de cette thèse est de rendre l'apprentissage profond plus efficace en termes de ressources en appliquant le principe de modularité. La thèse comporte plusieurs contributions principales : une étude de la littérature sur la modularité dans l'apprentissage profond; la conception d'OmniPrint et de Meta-Album, des outils qui facilitent l'étude de la modularité des données; des études de cas examinant les effets de l'apprentissage épisodique, un exemple de modularité des données; un mécanisme d'évaluation modulaire appelé LTU pour évaluer les risques en matière de protection de la vie privée; et la méthode RRR pour réutiliser des modèles modulaires pré-entraînés afin d'en construire des versions plus compactes. La modularité, qui implique la décomposition d'une entité en sous-entités, est un concept répandu dans diverses disciplines. Cette thèse examine la modularité sur trois axes de l'apprentissage profond : les données, la tâche et le modèle. OmniPrint et Meta-Album facilitent de comparer les modèles modulaires et d'explorer les impacts de la modularité des données. LTU garantit la fiabilité de l'évaluation de la protection de la vie privée. RRR améliore l'efficacité de l'utilisation des modèles modulaires pré-entraînés. Collectivement, cette thèse fait le lien entre le principe de modularité et l'apprentissage profond et souligne ses avantages dans certains domaines de l'apprentissage profond, contribuant ainsi à une intelligence artificielle plus efficace en termes de ressources

Modularity in Deep Learning: A Survey

International audienceModularity is a general principle present in many fields. It offers attractive advantages, including, among others, ease of conceptualization, interpretability, scalability, module combinability, and module reusability. The deep learning community has long sought to take inspiration from the modularity principle, either implicitly or explicitly. This interest has been increasing over recent years. We review the notion of modularity in deep learning around three axes: data, task, and model, which characterize the life cycle of deep learning. Data modularity refers to the observation or creation of data groups for various purposes. Task modularity refers to the decomposition of tasks into sub-tasks. Model modularity means that the architecture of a neural network system can be decomposed into identifiable modules. We describe different instantiations of the modularity principle, and we contextualize their advantages in different deep learning sub-fields. Finally, we conclude the paper with a discussion of the definition of modularity and directions for future research

MicroRNA-153-3p enhances cell radiosensitivity by targeting BCL2 in human glioma

Abstract Background Glioma is the most prevalent malignant tumor in human central nervous systems. Recently, the development of resistance to radiotherapy in glioma patients markedly vitiates the therapy outcome. MiR-153-3p has been reported to be closely correlated with tumor progression, but its effect and molecular mechanism underlying radioresistance remains unclear in glioma. Methods The expression of miR-153-3p was determined in radioresistant glioma clinical specimens as well as glioma cell lines exposed to irradiation (IR) using quantitative real-time PCR. Cell viability, proliferation and apoptosis were then evaluated by MTT assay, colony formation assay, Flow cytometry analysis and caspase-3 activity assay in glioma cells (U87 and U251). Tumor forming was evaluated by nude mice model in vivo. TUNEL staining was used to detect cell apoptosis in nude mice model. The target genes of miR-153-3p were predicted and validated using integrated bioinformatics analysis and a luciferase reporter assay. Results Here, we found that miR-153-3p was down-regulated in radioresistant glioma clinical specimens as well as glioma cell lines (U87 and U251) exposed to IR. Enhanced expression of miR-153-3p promoted the radiosensitivity, promoted apoptosis and elevated caspase-3 activity in glioma cells in vitro, as well as the radiosensitivity in U251 cell mouse xenografs in vivo. Mechanically, B cell lymphoma-2 gene (BCL2) was identified as the direct and functional target of miR-153-3p. Moreover, restoration of BCL2 expression reversed miR-153-3p-induced increase of radiosensitivity, apoptosis and caspase-3 activity in U251 cells in vitro. In addition, clinical data indicated that the expression of miR-153-3p was significantly negatively associated with BCL2 in radioresistance of glioma samples. Conclusions Our findings suggest that miR-153-3p is a potential target to enhance the effect of radiosensitivity on glioma cells, thus representing a new potential therapeutic target for glioma