Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation

Past works on multimodal machine translation (MMT) elevate bilingual setup by incorporating additional aligned vision information. However, an image-must requirement of the multimodal dataset largely hinders MMT's development -- namely that it demands an aligned form of [image, source text, target text]. This limitation is generally troublesome during the inference phase especially when the aligned image is not provided as in the normal NMT setup. Thus, in this work, we introduce IKD-MMT, a novel MMT framework to support the image-free inference phase via an inversion knowledge distillation scheme. In particular, a multimodal feature generator is executed with a knowledge distillation module, which directly generates the multimodal feature from (only) source texts as the input. While there have been a few prior works entertaining the possibility to support image-free inference for machine translation, their performances have yet to rival the image-must translation. In our experiments, we identify our method as the first image-free approach to comprehensively rival or even surpass (almost) all image-must frameworks, and achieved the state-of-the-art result on the often-used Multi30k benchmark. Our code and data are available at: https://github.com/pengr/IKD-mmt/tree/master..Comment: Long paper accepted by EMNLP2022 main conferenc

Better Sign Language Translation with Monolingual Data

Sign language translation (SLT) systems, which are often decomposed into video-to-gloss (V2G) recognition and gloss-to-text (G2T) translation through the pivot gloss, heavily relies on the availability of large-scale parallel G2T pairs. However, the manual annotation of pivot gloss, which is a sequence of transcribed written-language words in the order in which they are signed, further exacerbates the scarcity of data for SLT. To address this issue, this paper proposes a simple and efficient rule transformation method to transcribe the large-scale target monolingual data into its pseudo glosses automatically for enhancing the SLT translation. Empirical results show that the proposed approach can significantly improve the performance of SLT, especially achieving state-of-the-art results on two SLT benchmark datasets PHEONIX-WEATHER 2014T and ASLG-PC12. Our code has been released at: https://github.com/pengr/Mono\_SLT

Additional Positive Enables Better Representation Learning for Medical Images

This paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning ability. Unlike conventional BYOL which relies on only one positive pair generated by two augmented views of the same image, we argue that information from different images with the same label can bring more diversity and variations to the target features, thus benefiting representation learning. To identify such pairs without any label, we investigate TracIn, an instance-based and computationally efficient influence function, for BYOL training. Specifically, TracIn is a gradient-based method that reveals the impact of a training sample on a test sample in supervised learning. We extend it to the self-supervised learning setting and propose an efficient batch-wise per-sample gradient computation method to estimate the pairwise TracIn to represent the similarity of samples in the mini-batch during training. For each image, we select the most similar sample from other images as the additional positive and pull their features together with BYOL loss. Experimental results on two public medical datasets (i.e., ISIC 2019 and ChestX-ray) demonstrate that the proposed method can improve the classification performance compared to other competitive baselines in both semi-supervised and transfer learning settings.Comment: 8 page

Secure Single-Server Fuzzy Deduplication without Interactive Proof-of-Ownership in Cloud

The redundant of multimedia data made an unnecessary waste in encrypted cloud storage, unlike text with completely consistent content, multimedia data allows a certain degree of similarity in deduplication, In this work, we focus on the multimedia data which takes a seriously proportion of storage in scenarios such as data outsourcing to propose secure fuzzy deduplication without the additional servers based on Convergent Encryption(CE), say the Single-server Fuzzy Deduplication (SSFD). Compared to the related fuzzy deduplication, SSFD is strong at resisting brute-force attacks caused by server-server collusion, moreover, we also put server-client collusion attacks into security solutions. Additionally, to enhance the security of data, the proposed scheme provides both protection against replay attacks and verification of label consistency and adds no extra communication such as Proof of Ownership(PoW) in interaction. We separately presented a formal security analysis and performed performance at last to prove security solutions and evaluate the experimental results, it shows SSFD provides both a reliable fuzzy images secure deduplication protocol and a computationally feasible solution

A panoramic review on phytochemistry, pharmacological potential, health benefits, and versatility of Solanum tuberosum L.

The potato (Solanum tuberosum L.) belongs to the family Solanaceae and is one of most versatile crops, vital components of the human diet in numerous countries. It is regarded as one of the most promising crops for reducing world hunger and poverty. It is one of the foremost non-grain crops in the world, being a cost-effective and easily accessible food with several health benefits. The entire plant including peel, tuber, and leaves are used in traditional medicine. Potatoes are high in carbohydrates, lipids, phenolic acids, anthocyanins, carotenoids, proteins, flavonoids, vitamins, potassium, phosphorus, copper, and fiber. The purpose of this review study was to present up-to-date information on novel metabolites discovered in potatoes that play a role in preventing illness and improve human well-being. We attempted to assemble data on the variety of pharmacological activity including antioxidant, anti-diabetic, antihypertensive, anticancer, antiobesity and anti-inflammatory properties of potatoes, as well as their function in enhancing gut health and satiety. In-vitro investigations, human cell culture, experimental animal studies have revealed that potatoes have a variety of health-promoting qualities. The observations and recommendations presented here are scientifically interesting for food chemistry, pharmacology, nanotechnology, and toxicology. These may also contribute to enhance nutrition, food safety, and human health

Thermodynamic analysis of decarbonizing NGCC power plants by the tail-end green ammonia-driven calcium looping

This work proposes a novel ammonia driven tail-end calcium looping (CaL) process to capture carbon emission from natural gas combined cycle (NGCC) power plants for net-zero energy. Two integration schemes are introduced, including sensible heat thermochemical recuperation (SHTR) and carbonation heat thermochemical recuperation (CHTR) driven by combustion of partially cracked ammonia as a zero-carbon fuel. Results show that energy penalties can be reduced from 9.6 % in the NGCC power plant with the CaL-Oxy method to 1.8 % in the SHTR scheme and 1.4 % in the CHTR scheme, respectively. Comparing with the NGCC base power plant and the NH3-based thermochemical recuperation power plant, energy savings can be achieved at 5.44 MJLHV/kg CO2 in the SHTR scheme and 6.73 MJLHV/kg CO2 in the CHTR scheme. Additionally, exergy analysis shows that the reduction of exergy destruction in the carbonation and calcination processes determines the thermodynamic performance enhancement. Cascaded heat recovery of carbonation heat and the heating supply method of calcination narrow the energy level difference, contributing to the reduction of exergy destruction. Sensitivity analysis indicates that reorganizing a more efficient thermodynamic cycle can offset the energy consumption of CO2 capture from flue gas, resulting in an optimized negative energy penalty at −0.8 %.This work was supported by grants from Natural Science Foundation of Guangdong Province (No. 2024A1515012661), and Guangzhou Basic and Applied Basic Research Foundation (No. 2024A04J3828), Engineering and Physical Sciences Research Council, UK (EP/X03593X/1), and China Postdoctoral Science Foundation (No. 2024T170587, No. 2024M752134).Energ

Factors That Influence Perceived Organizational Support for Emotional Labor of Chinese Medical Personnel in Hubei

At the outbreak of coronavirus disease in Wuhan, China, 42,322 medical personnel from other provinces and municipalities in China volunteered to rush to Hubei to assist their colleagues. Their all-out efforts contributed to Hubei finally winning the fight to prevent and control the pandemic. The aim of this study is to explore the influence of perceived organizational support on the emotional labor of medical personnel in Hubei Province. A group of 170 medical personnel from (tertiary) hospitals who participated in the pandemic aid operation in Hubei completed self-administered questionnaires, including the perceived organizational support scale, emotional labor scale, and professional identity scale. This study used Pearson's correlation in SPSS to analyze the three variables of organizational support, emotional labor, and professional identity. Organizational support and emotional labor (r = 0.443, P < 0.01), organizational support and professional identity (r = 0.631, P < 0.01), and emotional labor and occupational identity (r = 0.511, P < 0.01) showed a significant positive correlation. The bootstrapping mediating effect test was used to determine the overall mediating effect of occupational identity. Occupational identity was a complete mediating effect between organizational support and emotional labor. The results show that a strong sense of organizational support can promote higher emotional labor among medical workers in Hubei Province. A strong sense of organizational support will also promote a stronger professional identity; further, a strong professional identity completely mediates the effect of perceived organizational support on emotional labor. These results infer that in emergency medical and health services, medical personnel can realize a high sense of organizational support, which could enhance their professional identity; this enables them to combine their professional goals with organizational goals more actively and to finally pay higher emotional labor to achieve organizational goals