I2SRM: Intra- and Inter-Sample Relationship Modeling for Multimodal Information Extraction

Multimodal information extraction is attracting research attention nowadays, which requires aggregating representations from different modalities. In this paper, we present the Intra- and Inter-Sample Relationship Modeling (I2SRM) method for this task, which contains two modules. Firstly, the intra-sample relationship modeling module operates on a single sample and aims to learn effective representations. Embeddings from textual and visual modalities are shifted to bridge the modality gap caused by distinct pre-trained language and image models. Secondly, the inter-sample relationship modeling module considers relationships among multiple samples and focuses on capturing the interactions. An AttnMixup strategy is proposed, which not only enables collaboration among samples but also augments data to improve generalization. We conduct extensive experiments on the multimodal named entity recognition datasets Twitter-2015 and Twitter-2017, and the multimodal relation extraction dataset MNRE. Our proposed method I2SRM achieves competitive results, 77.12% F1-score on Twitter-2015, 88.40% F1-score on Twitter-2017, and 84.12% F1-score on MNRE

Belief Evolution Network-based Probability Transformation and Fusion

Smets proposes the Pignistic Probability Transformation (PPT) as the decision layer in the Transferable Belief Model (TBM), which argues when there is no more information, we have to make a decision using a Probability Mass Function (PMF). In this paper, the Belief Evolution Network (BEN) and the full causality function are proposed by introducing causality in Hierarchical Hypothesis Space (HHS). Based on BEN, we interpret the PPT from an information fusion view and propose a new Probability Transformation (PT) method called Full Causality Probability Transformation (FCPT), which has better performance under Bi-Criteria evaluation. Besides, we heuristically propose a new probability fusion method based on FCPT. Compared with Dempster Rule of Combination (DRC), the proposed method has more reasonable result when fusing same evidence

Providing Proximity Safety and Speeding Alerts to Workers on Construction Sites Using Bluetooth Low Energy RTLS

The construction sector is one of the most dangerous industrial sectors. Struck-by object or equipment is one of the main causes of fatal accidents on construction sites. Although many regulations have been designed for struck-by accidents, these accidents are still causing many injuries and fatalities. According to the U.S. Bureau of Labor Statistic, the struck-by accidents has led to 112 deaths on construction site in 2018. The application of real-time location systems (RTLS) on construction sites provides new possibilities in construction safety management. Previous researchers have proposed using RTLS to track the location of workers and equipment on construction sites to improve construction safety. However, the previous methods have some limitations (e.g. cabling problems, positioning quality). Furthermore, providing effective safety alerts to workers within dangerous proximity to equipment has not been addressed in previous research. This research aims to develop a method for providing near real-time proximity alerts to workers on construction sites using Bluetooth Low Energy (BLE) RTLS based on angle of arrival (AOA). This RTLS can provide acceptable accuracy coupled with large coverage without the need of timing cables. Also, with the support of two-way communications between the tags and sensors, it is possible to provide vibro-tactile alerts to the workers using wristbands. In addition, alerts representing different cases of proximities and speeding were defined. The prototype system has the following features: (1) less cabling by using wireless technologies for data transmission, (2) less false alerts by generating the alerts to specific entities based on the micro-schedule of activities, (3) easily perceived alerts. Tests were conducted on a construction site of an electric substation to test the accuracy of the RTLS and the performance of the prototype system. The test results indicated that the prototype system is capable of detecting proximities and generating timely alerts to the involved entities

Comparison of hair from rectum cancer patients and from healthy persons by Raman microspectroscopy and imaging

AbstractIn this work, Raman microspectroscopy and imaging was employed to analyze cancer patients’ hair tissue. The comparison between the hair from rectum cancer patients and the hair from healthy people reveals some remarkable differences, such as for the rectum cancer patients, there are more lipids but less content of α-helix proteins in the hair medulla section. Though more statistic data are required to establish universary rules for practical and accurate diagnosis, this work based on case study demonstrates the possibility of applying Raman microspectroscopy to reveal abnormality in non-cancer tissues such as hair in order to predict and diagnose cancers

Production of gamma-aminobutyric acid by Lactobacillus brevis NCL912 using fed-batch fermentation

<p>Abstract</p> <p>Background</p> <p>Gamma-aminobutyric acid is a major inhibitory neurotransmitter in mammalian brains, and has several well-known physiological functions. Lactic acid bacteria possess special physiological activities and are generally regarded as safe. Therefore, using lactic acid bacteria as cell factories for gamma-aminobutyric acid production is a fascinating project and opens up a vast range of prospects for making use of GABA and LAB. We previously screened a high GABA-producer <it>Lactobacillus brevis </it>NCL912 and optimized its fermentation medium composition. The results indicated that the strain showed potential in large-scale fermentation for the production of gamma-aminobutyric acid. To increase the yielding of GABA, further study on the fermentation process is needed before the industrial application in the future. In this article we investigated the impacts of pyridoxal-5'-phosphate, pH, temperature and initial glutamate concentration on gamma-aminobutyric acid production by <it>Lactobacillus brevis </it>NCL912 in flask cultures. According to the data obtained in the above, a simple and effective fed-batch fermentation method was developed to highly efficiently convert glutamate to gamma-aminobutyric acid.</p> <p>Results</p> <p>Pyridoxal-5'-phosphate did not affect the cell growth and gamma-aminobutyric acid production of <it>Lb. brevis </it>NCL912. Temperature, pH and initial glutamate concentration had significant effects on the cell growth and gamma-aminobutyric acid production of <it>Lb. brevis </it>NCL912. The optimal temperature, pH and initial glutamate concentration were 30-35°C, 5.0 and 250-500 mM. In the following fed-batch fermentations, temperature, pH and initial glutamate concentration were fixed as 32°C, 5.0 and 400 mM. 280.70 g (1.5 mol) and 224.56 g (1.2 mol) glutamate were supplemented into the bioreactor at 12 h and 24 h, respectively. Under the selected fermentation conditions, gamma-aminobutyric acid was rapidly produced at the first 36 h and almost not produced after then. The gamma-aminobutyric acid concentration reached 1005.81 ± 47.88 mM, and the residual glucose and glutamate were 15.28 ± 0.51 g L^-1and 134.45 ± 24.22 mM at 48 h.</p> <p>Conclusions</p> <p>A simple and effective fed-batch fermentation method was developed for <it>Lb. brevis </it>NCL912 to produce gamma-aminobutyric acid. The results reveal that <it>Lb. brevis </it>NCL912 exhibits a great application potential in large-scale fermentation for the production of gamma-aminobutyric acid.</p

Asymmetric Polynomial Loss For Multi-Label Classification

Various tasks are reformulated as multi-label classification problems, in which the binary cross-entropy (BCE) loss is frequently utilized for optimizing well-designed models. However, the vanilla BCE loss cannot be tailored for diverse tasks, resulting in a suboptimal performance for different models. Besides, the imbalance between redundant negative samples and rare positive samples could degrade the model performance. In this paper, we propose an effective Asymmetric Polynomial Loss (APL) to mitigate the above issues. Specifically, we first perform Taylor expansion on BCE loss. Then we ameliorate the coefficients of polynomial functions. We further employ the asymmetric focusing mechanism to decouple the gradient contribution from the negative and positive samples. Moreover, we validate that the polynomial coefficients can recalibrate the asymmetric focusing hyperparameters. Experiments on relation extraction, text classification, and image classification show that our APL loss can consistently improve performance without extra training burden.Comment: ICASSP 202

Rain Statistics Investigation and Rain Attenuation Modeling for Millimeter Wave Short-range Fixed Links

Millimeter wave (mmWave) communication is a key technology for fifth generation (5G) and beyond communication networks. However, the communication quality of the radio link can be largely affected by rain attenuation, which should be carefully taken into consideration when calculating the link budget. In this paper, we present results of weather data collected with a PWS100 disdrometer and mmWave channel measurements at 25.84 GHz (K band) and 77.52 GHz (E band) using a custom-designed channel sounder. The rain statistics, including rain intensity, rain events, and rain drop size distribution (DSD) are investigated for one year. The rain attenuation is predicted using the DSD model with Mie scattering and from the model in ITU-R P.838-3. The distance factor in ITU-R P.530-17 is found to be inappropriate for a short-range link. The wet antenna effect is investigated and additional protection of the antenna radomes is demonstrated to reduce the wet antenna effect on the measured attenuation