This study was carried out to delineate the occurrence and spatial distribution of groundwater fluoride in a loess area of China and to determine the geochemical and anthropogenic factors that ...influence its concentration. Water quality was assessed for drinking purpose by comparing with the national and WHO drinking water guidelines, and the impacts of fluoride on human health were also quantified using the health risk assessment model recommended by the USEPA. The results demonstrate that groundwater in the study area is slightly alkaline in nature, and its quality is generally good except slightly excessive TDS, TH, Na
+
, F
−
, and nitrate at some local locations. High-fluoride groundwater is mainly distributed in the southeast part of the study area, which is in accordance with the groundwater flow direction in this area. Groundwater fluoride is mainly of natural origin and is dominantly controlled by natural factors such as pH, specific hydrochemical environment, ion exchange, and saturation state of minerals. Fluoride contributes the most to the total health risk in the present study. Children are at higher health risk than adults in this area. Establishing central water supply system and rainwater harvesting system are suggested to guarantee safe drinking water supply in this area.
As the rapid development of electronic devices and wireless communication tools, it is urgent to design and fabricate low-cost, lightweight and effective electromagnetic absorption materials to solve ...electromagnetic interference of electromagnetic wave. Herein, a feasible and low-cost method has been developed to synthesize an effective carbon-based microwave absorber. The hierarchically porous structures derived from eggshell membrane (ESM) and the anchored CoFe2O4 nanoparticles (NPs) helped the C/CoFe2O4 nanocomposites perform a favor for strengthening electromagnetic absorption capability. The porous C@CoFe2O4 nanocomposites achieves the maximum reflection loss value of −49.6 dB at 9.2 GHz with 30% loading in the paraffin matrix. Detailed investigation reveals that the porous structure as well as strong synergetic effect between porous carbon matrix and magnetic nanoparticles can effectively improve the impedance matching condition and attenuation ability of the nanocomposites, leading to a high microwave absorption performance. In addition, the simulation results indicate that the radar cross section (RCS) of a strong metal scattering sources at different degrees can be significantly reduced after coating with C@CoFe2O4 nanocomposites. These achievements light the way to prepare low-cost and sustainable microwave absorbents with excellent electromagnetic wave absorbing performances, utilizing the wasted eggshell membrane biomasses as precursors.
Display omitted
The problem of quantizing the activations of a deep neural network is considered. An examination of the popular binary quantization approach shows that this consists of approximating a classical ...non-linearity, the hyperbolic tangent, by two functions: a piecewise constant sign function, which is used in feedforward network computations, and a piecewise linear hard tanh function, used in the backpropagation step during network learning. The problem of approximating the widely used ReLU non-linearity is then considered. An half-wave Gaussian quantizer (HWGQ) is proposed for forward approximation and shown to have efficient implementation, by exploiting the statistics of of network activations and batch normalization operations. To overcome the problem of gradient mismatch, due to the use of different forward and backward approximations, several piece-wise backward approximators are then investigated. The implementation of the resulting quantized network, denoted as HWGQ-Net, is shown to achieve much closer performance to full precision networks, such as AlexNet, ResNet, GoogLeNet and VGG-Net, than previously available low-precision networks, with 1-bit binary weights and 2-bit quantized activations.
This paper presents stacked attention networks (SANs) that learn to answer natural language questions from images. SANs use semantic representation of a question as query to search for the regions in ...an image that are related to the answer. We argue that image question answering (QA) often requires multiple steps of reasoning. Thus, we develop a multiple-layer SAN in which we query an image multiple times to infer the answer progressively. Experiments conducted on four image QA data sets demonstrate that the proposed SANs significantly outperform previous state-of-the-art approaches. The visualization of the attention layers illustrates the progress that the SAN locates the relevant visual clues that lead to the answer of the question layer-by-layer.
Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even ...multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, achieving CIDEr / SPICE / BLEU-4 scores of 117.9, 21.5 and 36.9, respectively. Demonstrating the broad applicability of the method, applying the same approach to VQA we obtain first place in the 2017 VQA Challenge.
We propose a novel framework named StyleNet to address the task of generating attractive captions for images and videos with different styles. To this end, we devise a novel model component, named ...factored LSTM, which automatically distills the style factors in the monolingual text corpus. Then at runtime, we can explicitly control the style in the caption generation process so as to produce attractive visual captions with the desired style. Our approach achieves this goal by leveraging two sets of data: 1) factual image/video-caption paired data, and 2) stylized monolingual text data (e.g., romantic and humorous sentences). We show experimentally that StyleNet outperforms existing approaches for generating visual captions with different styles, measured in both automatic and human evaluation metrics on the newly collected FlickrStyle10K image caption dataset, which contains 10K Flickr images with corresponding humorous and romantic captions.
This paper presents work toward the development of a new family of self-healing materials that hold promise for “crack-free” concrete or other cementitious composites. This innovative system features ...the design of microcapsules with oil core and silica gel shell. Methylmethacrylate monomer and triethylborane were selected as the healing agent and the catalyst, i.e. oil core phases in the system. They were microencapsulated, respectively, through an interfacial self-assembly process and sol–gel reaction. Then the microcapsules were dispersed in fresh cement mortar along with carbon microfibers. The self-healing effect was evaluated using permeability measurements along with a fatigue test under uniaxial compression cyclic loading and further confirmed by surface analytical tools including optical microscope and field emission scanning electron microscope coupled with energy-dispersive X-ray analyzer (FESEM/EDX).
The creation of superelastic, flexible three-dimensional (3D) graphene-based architectures is still a great challenge due to structure collapse or significant plastic deformation. Herein, we report a ...facile approach of transforming the mechanically fragile reduced graphene oxide (rGO) aerogel into superflexible 3D architectures by introducing water-soluble polyimide (PI). The rGO/PI nanocomposites are fabricated using strategies of freeze casting and thermal annealing. The resulting monoliths exhibit low density, excellent flexibility, superelasticity with high recovery rate, and extraordinary reversible compressibility. The synergistic effect between rGO and PI endows the elastomer with desirable electrical conductivity, remarkable compression sensitivity, and excellent durable stability. The rGO/PI nanocomposites show potential applications in multifunctional strain sensors under the deformations of compression, bending, stretching, and torsion.
This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) ...cells. The proposed LSTM-RNN model sequentially takes each word in a sentence, extracts its information, and embeds it into a semantic vector. Due to its ability to capture long term memory, the LSTM-RNN accumulates increasingly richer information as it goes through the sentence, and when it reaches the last word, the hidden layer of the network provides a semantic representation of the whole sentence. In this paper, the LSTM-RNN is trained in a weakly supervised manner on user click-through data logged by a commercial web search engine. Visualization and analysis are performed to understand how the embedding process works. The model is found to automatically attenuate the unimportant words and detect the salient keywords in the sentence. Furthermore, these detected keywords are found to automatically activate different cells of the LSTM-RNN, where words belonging to a similar topic activate the same cell. As a semantic representation of the sentence, the embedding vector can be used in many different applications. These automatic keyword detection and topic allocation abilities enabled by the LSTM-RNN allow the network to perform document retrieval, a difficult language processing task, where the similarity between the query and documents can be measured by the distance between their corresponding sentence embedding vectors computed by the LSTM-RNN. On a web search task, the LSTM-RNN embedding is shown to significantly outperform several existing state of the art methods. We emphasize that the proposed model generates sentence embedding vectors that are specially useful for web document retrieval tasks. A comparison with a well known general sentence embedding method, the Paragraph Vector, is performed. The results show that the proposed method in this paper significantly outperforms Paragraph Vector method for web document retrieval task.
With the noticeable growing rate of electronics, materials with good flexibility and high conductivity that can provide electromagnetic interference (EMI) shielding with minimal thickness are ...urgently needed. Here, for the first time, we demonstrate the potential of a non-woven composite film with unique core-shell and sandwich combined microstructure for EMI shielding. A 0.27-mm-thick reduced graphene oxide coated Fe3O4@SiO2@polypyrrole (FSPG) composite film exhibited an average EMI shielding effectiveness of 32 dB (specific EMI shielding effectiveness can be ∼ 12,608.4 dB cm2 g−1). This performance originates from the relative high electrical conductivity of FSPG films (0.71 S/cm) and multiple internal reflections from the interconnected core-shell and sandwich microstructure in the free-standing films. The mechanical flexibility offered by the FSPG films enable them to be considered as a new alternative material with efficient EMI shielding.
Display omitted
PDF
