Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events

In this paper, we propose a new strategy for acoustic scene classification (ASC) , namely recognizing acoustic scenes through identifying distinct sound events. This differs from existing strategies, which focus on characterizing global acoustical distributions of audio or the temporal evolution of short-term audio features, without analysis down to the level of sound events. To identify distinct sound events for each scene, we formulate ASC in a multi-instance learning (MIL) framework, where each audio recording is mapped into a bag-of-instances representation. Here, instances can be seen as high-level representations for sound events inside a scene. We also propose a MIL neural networks model, which implicitly identifies distinct instances (i.e., sound events). Furthermore, we propose two specially designed modules that model the multi-temporal scale and multi-modal natures of the sound events respectively. The experiments were conducted on the official development set of the DCASE2018 Task1 Subtask B, and our best-performing model improves over the official baseline by 9.4% (68.3% vs 58.9%) in terms of classification accuracy. This study indicates that recognizing acoustic scenes by identifying distinct sound events is effective and paves the way for future studies that combine this strategy with previous ones.Comment: code URL typo, code is available at https://github.com/hackerekcah/distinct-events-asc.gi

Automatic Summarization in Chinese Product Reviews

With the increasing number of online comments, it was hard for buyers to find useful information in a short time so it made sense to do research on automatic summarization which fundamental work was focused on product reviews mining. Previous studies mainly focused on explicit features extraction whereas often ignored implicit features which hadn't been stated clearly but containing necessary information for analyzing comments. So how to quickly and accurately mine features from web reviews had important significance for summarization technology. In this paper, explicit features and “feature-opinion” pairs in the explicit sentences were extracted by Conditional Random Field and implicit product features were recognized by a bipartite graph model based on random walk algorithm. Then incorporating features and corresponding opinions into a structured text and the abstract was generated based on the extraction results. The experiment results demonstrated the proposed methods outpreferred baselines

Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention

Recent years have witnessed the great potential of attention mechanism in graph representation learning. However, while variants of attention-based GNNs are setting new benchmarks for numerous real-world datasets, recent works have pointed out that their induced attentions are less robust and generalizable against noisy graphs due to lack of direct supervision. In this paper, we present a new framework which utilizes the tool of causality to provide a powerful supervision signal for the learning process of attention functions. Specifically, we estimate the direct causal effect of attention to the final prediction, and then maximize such effect to guide attention attending to more meaningful neighbors. Our method can serve as a plug-and-play module for any canonical attention-based GNNs in an end-to-end fashion. Extensive experiments on a wide range of benchmark datasets illustrated that, by directly supervising attention functions, the model is able to converge faster with a clearer decision boundary, and thus yields better performances

Effects of Chloride ions on Carbonation Rate of Hardened Cement Paste by X-ray CT Techniques

Corrosion of steel bars in concrete structures is initiated as a result of concrete carbonation and/or chloride intrusion, and influenced by their interaction. This paper presents an experimental investigation into the effect of chloride ions on carbonation of cement paste by means of X-ray CT techniques and mercury intrusion porosimetry(MIP), which is benchmarked by the conventional phenolphthalein method. A group of the cement paste cylinders with different amounts of chlorides ions were manufactured and cured before they were subjected to an accelerated carbonation process in a conditional cabinet regime for different ages. The carbonation front of the cement paste was first evaluated using phenolphthalein method. This was followed by an investigation of microstructure evolution of the cement paste using XCT and MIP techniques. The experimental results show that the carbonation of a cement paste increases with its water to cement ratio and with carbonation ages, but decrease with its amount of chloride ions. In particular, it has been found that increases of chloride ion of a cement paste refine its porous structures, decrease its porosity and eventually mitigate its carbonation rate. The relevant results can be referred to for durability design and prediction of reinforced concrete structures

Room-temperature nonequilibrium growth of controllable ZnO nanorod arrays

In this study, controllable ZnO nanorod arrays were successfully synthesized on Si substrate at room temperature (approx. 25°C). The formation of controllable ZnO nanorod arrays has been investigated using growth media with different concentrations and molar ratios of Zn(NO3)2 to NaOH. Under such a nonequilibrium growth condition, the density and dimension of ZnO nanorod arrays were successfully adjusted through controlling the supersaturation degree, i.e., volume of growth medium. It was found that the wettability and electrowetting behaviors of ZnO nanorod arrays could be tuned through variations of nanorods density and length. Moreover, its field emission property was also optimized by changing the nanorods density and dimension

Left Anterior Temporal Lobe and Bilateral Anterior Cingulate Cortex Are Semantic Hub Regions: Evidence from Behavior-Nodal Degree Mapping in Brain-Damaged Patients

The organizational principles of semantic memory in the human brain are still controversial. Although studies have shown that the semantic system contains hub regions that bind information from different sensorimotoric modalities to form concepts, it is unknown whether there are hub regions other than the anterior temporal lobe (ATL). Meanwhile, previous studies have rarely used network measurements to explore the hubs or correlated network indexes with semantic performance, although the most direct supportive evidence of hubs should come from the network perspective. To fill this gap, we correlated the brain-network index with semantic performance in 86 brain-damaged patients. We especially selected the nodal degree measure that reflects how well a node is connected in the network. The measure was calculated as the total number of connections of a given node with other nodes in the resting-state functional MRI network. Semantic ability was measured using the performance of both general and modality-specific (object form, color, motion, sound, manipulation, and function) semantic tasks. We found that the left ATL and the bilateral anterior cingulate cortex could be semantic hubs because the reduced nodal degree values of these regions could effectively predict the deficits in both general and modality-specific semantic performance. Moreover, the effects remained when the analyses were performed only in the patients who did not have lesions in these regions. The two hub regions might support semantic representations and executive control processes, respectively. These data provide empirical evidence for the distributed-plus-hub theory of semantic memory from the network perspective.</p

Anthriscifolcine A, a C18-diterpenoid alkaloid

The title compound, C26H39NO7, which was isolated from Delphinium anthriscifolium var. majus, has a lycoctonine carbon skeleton containing four six-membered rings (A, B, D and E) and three five-membered rings (C, F and G). Rings A, B and E adopt chair conformation, while ring D adopts a boat conformation. Rings C and F adopt envelope conformations

Development and test of a spring-finger roller-type hot pepper picking header

In order to improve picking efficiency, performance and reduce harvesting costs, this study developed a spring-finger roller type hot pepper picking header. The picking header was mainly composed of a roller, spring-fingers, a steel frame, an upper cover plate, and transmission parts. According to the mechanical characteristics of the hot pepper, the picking structure model of hot pepper was developed based on SolidWorks. The movement law of the picking spring-finger was analyzed based on the working principle to select appropriate motion parameters of main factors. A prototype of hot pepper picking header was developed based on these selected parameters, and field experiments were carried out to study main factors affecting the harvesting effect. The rotation speed of the roller and the travel speed of picking machine were monitored by setting sensors. The test results were analyzed and optimized based on Central Composite Design (CCD) by using Design Expert. The orthogonal test results showed that: when roller rotation speed was 215.00 r⋅min−1, and travel speed was 3.59 km⋅h−1, the hot pepper picking header had the best harvest effect. The picking rate was 98.47%, and the breakage rate was 3.87%. The field experiment proved that the spring-finger roller type hot pepper picking header had a good picking effect