The endless search for better alloys Hu, Qing-Miao; Yang, Rui
Science (American Association for the Advancement of Science),
10/2022, Letnik:
378, Številka:
6615
Journal Article
Recenzirano
Machine learning narrows down the enormous search space for functional materials
Researchers and engineers are constantly searching for materials with specific properties to drive the rapid ...development of various technologies. Because of the practically infinite combinations of materials, these searches need to be strategized. In the case of conventional alloys, they generally consist of a single principal metal element accompanied by other elements. More recently, researchers have ventured into looking for alloys with multiple principal elements (
1
,
2
). This type of alloy, called a high-entropy alloy (HEA), greatly expands the search space of alloys for materials design. On page 78 of this issue, Rao
et al.
(
3
) present a physics-informed machine-learning approach to screen alloys with low thermal expansion coefficient within the huge iron-cobalt-nickel-chromium (Fe–Co–Ni–Cr) and iron-cobalt-nickel-chromium-copper (Fe–Co–Ni–Cr–Cu) composition space. These materials, which expand and contract very little with temperature changes, make them valuable for application on precision instruments for which high dimensional stability of the components is required.
A number of recent efforts have attempted to design accelerators for popular machine learning algorithms, such as those involving convolutional and deep neural networks (CNNs and DNNs). These ...algorithms typically involve a large number of multiply-accumulate (dot-product) operations. A recent project, DaDianNao, adopts a near data processing approach, where a specialized neural functional unit performs all the digital arithmetic operations and receives input weights from adjacent eDRAM banks. This work explores an in-situ processing approach, where memristor crossbar arrays not only store input weights, but are also used to perform dot-product operations in an analog manner. While the use of crossbar memory as an analog dot-product engine is well known, no prior work has designed or characterized a full-fledged accelerator based on crossbars. In particular, our work makes the following contributions: (i) We design a pipelined architecture, with some crossbars dedicated for each neural network layer, and eDRAM buffers that aggregate data between pipeline stages. (ii) We define new data encoding techniques that are amenable to analog computations and that can reduce the high overheads of analog-to-digital conversion (ADC). (iii) We define the many supporting digital components required in an analog CNN accelerator and carry out a design space exploration to identify the best balance of memristor storage/compute, ADCs, and eDRAM storage on a chip. On a suite of CNN and DNN workloads, the proposed ISAAC architecture yields improvements of 14.8×, 5.5×, and 7.5× in throughput, energy, and computational density (respectively), relative to the state-of-the-art DaDianNao architecture.
Single image fog removal is important for surveillance applications, and, recently, many defogging methods have been proposed. Due to the adverse atmospheric conditions, the scattering properties of ...foggy images depend on not only the depth information of scene but also the atmospheric aerosol model, which has a more prominent influence on illumination in a fog scene than that in a haze scene. However, the recent defogging methods confuse haze and fog, and they fail to consider fully the scattering properties. Thus, these methods are not sufficient to remove fog effects, especially for images in maritime surveillance. Therefore, this paper proposes a single image defogging method for visual maritime surveillance. First, a comprehensive scattering model is proposed to formulate a fog image in the glow-shaped environmental illumination. Then, an illumination decomposition algorithm is proposed to eliminate the glow effect on the airlight radiance and recover a fog layer, in which the objects at the infinite distance have uniform luminance. Second, a transmission-map estimation based on the non-local haze-lines prior is utilized to constrain the transmission map into a reasonable range for the input fog image. Finally, the proposed illumination compensation algorithm enables the defogging image to preserve the natural illumination information of the input image. In addition, a fog image dataset is established for the visual maritime surveillance. The experimental results based on the established dataset demonstrate that the proposed method can outperform the state-of-the-art methods in terms of both the subjective and objective evaluation criteria. Moreover, the proposed method can effectively remove fog and maintain naturalness for fog images.
A major bottleneck of pedestrian detection lies on the sharp performance deterioration in the presence of small-size pedestrians that are relatively far from the camera. Motivated by the observation ...that pedestrians of disparate spatial scales exhibit distinct visual appearances, we propose in this paper an active pedestrian detector that explicitly operates over multiple-layer neuronal representations of the input still image. More specifically, convolutional neural nets, such as ResNet and faster R-CNNs, are exploited to provide a rich and discriminative hierarchy of feature representations, as well as initial pedestrian proposals. Here each pedestrian observation of distinct size could be best characterized in terms of the ResNet feature representation at a certain layer of the hierarchy. Meanwhile, initial pedestrian proposals are attained by the faster R-CNNs techniques, i.e., region proposal network and follow-up region of interesting pooling layer employed right after the specific ResNet convolutional layer of interest, to produce joint predictions on the bounding-box proposals' locations and categories (i.e., pedestrian or not). This is engaged as an input to our active detector, where for each initial pedestrian proposal, a sequence of coordinate transformation actions is carried out to determine its proper x-y 2D location and the layer of feature representation, or eventually terminated as being background. Empirically our approach is demonstrated to produce overall lower detection errors on widely used benchmarks, and it works particularly well with far-scale pedestrians. For example, compared with 60.51% log-average miss rate of the state-of-the-art MS-CNN for far-scale pedestrians (those below 80 pixels in bounding-box height) of the Caltech benchmark, the miss rate of our approach is 41.85%, with a notable reduction of 18.66%.
By mimicking the highly parallel biological systems, neuromorphic hardware provides the capability of information processing within a compact and energy-efficient platform. However, traditional Von ...Neumann architecture and the limited signal connections have severely constrained the scalability and performance of such hardware implementations. Recently, many research efforts have been investigated in utilizing the latest discovered memristors in neuromorphic systems due to the similarity of memristors to biological synapses. In this paper, we explore the potential of a memristor crossbar array that functions as an autoassociative memory and apply it to brain-state-in-a-box (BSB) neural networks. Especially, the recall and training functions of a multianswer character recognition process based on the BSB model are studied. The robustness of the BSB circuit is analyzed and evaluated based on extensive Monte Carlo simulations, considering input defects, process variations, and electrical fluctuations. The results show that the hardware-based training scheme proposed in the paper can alleviate and even cancel out the majority of the noise issue.
The pedestrian attribute recognition aims at generating the structured description of pedestrian, which plays an important role in surveillance. However, it is difficult to achieve accurate ...recognition results due to diverse illumination, partial body occlusion and limited resolutions. Therefore, this paper proposes a comprehensive relationship framework for comprehensively describing and utilizing relations among attributes, describing different type of relations in the same dimension, and implementing complex transfers of relations in a GCN manner. This framework is named Correlation Graph Convolutional Network (CGCN). Based on the proposed framework, the feature vectors are built to associate attributes with image features and generate different relation matrices through self-attention among different feature vectors, describing different attribute relations. Then, we conduct multi-layer transfer of attribute relations by means of graph convolution, realizing complex utilization of attribute relations. In addition, the relations among attributes are fully exploited and two types of relations, namely the explicit and implicit relations, are proposed to be integrate into the proposed comprehensive relationship framework. The experimental results on RAP and PETA demonstrate that the recognition performance of the proposed CGCN can obviously outperform the state-of-the-arts, and moreover, the CGCN can achieve a better synergy with different relations.
Few-shot action recognition aims to recognize new unseen categories with only a few labeled samples of each class. However, it still suffers from the limitation of inadequate data, which easily leads ...to the overfitting and low-generalization problems. Therefore, we propose a cross-modal contrastive learning network (CCLN), consisting of an adversarial branch and a contrastive branch, to perform effective few-shot action recognition. In the adversarial branch, we elaborately design a prototypical generative adversarial network (PGAN) to obtain synthesized samples for increasing training samples, which can mitigate the data scarcity problem and thereby alleviate the overfitting problem. When the training samples are limited, the obtained visual features are usually suboptimal for video understanding as they lack discriminative information. To address this issue, in the contrastive branch, we propose a cross-modal contrastive learning module (CCLM) to obtain discriminative feature representations of samples with the help of semantic information, which can enable the network to enhance the feature learning ability at the class-level. Moreover, since videos contain crucial sequences and ordering information, thus we introduce a spatial-temporal enhancement module (SEM) to model the spatial context within video frames and the temporal context across video frames. The experimental results show that the proposed CCLN outperforms the state-of-the-art few-shot action recognition methods on four challenging benchmarks, including Kinetics, UCF101, HMDB51 and SSv2.
Remote sensing images play important roles in various earth surface observation applications. However, the hazy state of surface atmosphere can visually decrease the contrast and availability of ...remote sensing images. In this paper, we propose a haze and thin cloud removal method for single visible remote sensing images, which aims to robustly estimate haze thickness, atmospheric light, and transmission value from a remote sensing image with dense haze or thin cloud, and finally recovers a haze-free image. An elliptical boundary prior (EBP) is proposed to transform the haze thickness in each local patch from the pixels cluster in the spectral space, which is surrounded by an ellipse. With the aim of preventing highlight objects influences, an atmospheric light estimation approach is presented. The correlation of transmission and haze thickness is reconstructed to develop the scattering model for remote sensing images. The experimental results demonstrate that the proposed method can not only significantly improve the contrast and restore textures of various kinds of hazy remote sensing images but also well preserve the spectral information of visible bands.
Person re-identification is one of the most important and challenging problems in video analytics systems; it aims to match people across non-overlapping camera views. For person re-identification, ...metric learning is introduced to improve the performance by providing a metric adapted for cross-view matching. The essence of metric learning is to search for an optimal projection matrix to project the original features into a new feature space. However, most existing metric learning methods overlook the inconsistency of feature distributions in multiple cameras. In this paper, we propose a multi-projection metric learning (MPML) method to overcome the inconsistency among multiple cameras in person re-identification. Our solution is to jointly learn multiple projection matrices using paired samples from different cameras to project features from different cameras into a common feature space. To make our method adaptive to newly added cameras without affecting the learned projection matrices, we further propose an adaptive MPML method, which can learn new camera projection matrices without having to update any of the obtained projection matrices. The proposed methods are evaluated on four major person re-identification data sets, with comprehensive experiments showing the effectiveness of the proposed methods and notable improvements over the state-of-the-art approaches.
This study utilizes the unique merits of an 8-L laboratory upflow anaerobic sludge blanket (UASB) reactor for treating synthetic wastewater containing trichloroethylene (TCE). The reactor was ...operated at different hydraulic retention times (HRT) of 25, 20, 15, 10, and 5 h. TCE removal efficiency decreased from 99 to 85 % when the HRT was lowered down from 25 to 5 h, as well as chemical oxygen demand (COD) removal efficiency (from 95 to 84.15 %). Using Illumina 16S rRNA gene MiSeq sequencing, we investigated the evolution of bacterial communities in the anaerobic sludge under five different conditions of HRT. In total, 106,387 effective sequences of the 16S rRNA gene were generated from 5 samples that widely represented the diversity of microbial community. Sequence analysis consisting of several novel taxonomic levels ranging from phyla to genera revealed the percentages of these bacterial groups in each sample under different HRTs. The differences found among the five samples indicated that HRT had effects on the structures of bacterial communities and the changes of bacterial communities associated with the effect of HRT on the performance of the reactor. Sequence analyses showed that Bacteroidetes and Firmicutes were the dominant phyla. It is notable that the class Dehalococcoidia was found in the samples at HRT of 5, 10, 20, and 25 h, respectively, in which there were some dechlorination strains. Moreover, a tremendous rise of TCE removal efficiency from HRT of 5 h to HRT of 10 h was found.
PDF
