Bias and Fairness in Chatbots: An Overview

Chatbots have been studied for more than half a century. With the rapid development of natural language processing (NLP) technologies in recent years, chatbots using large language models (LLMs) have received much attention nowadays. Compared with traditional ones, modern chatbots are more powerful and have been used in real-world applications. There are however, bias and fairness concerns in modern chatbot design. Due to the huge amounts of training data, extremely large model sizes, and lack of interpretability, bias mitigation and fairness preservation of modern chatbots are challenging. Thus, a comprehensive overview on bias and fairness in chatbot systems is given in this paper. The history of chatbots and their categories are first reviewed. Then, bias sources and potential harms in applications are analyzed. Considerations in designing fair and unbiased chatbot systems are examined. Finally, future research directions are discussed

Reservoir properties and hydrocarbon enrichment law of Chang 1 oil layer group in Yanchang Formation, Wanhua area, Ordos Basin

Evaluation of tight oil reservoir properties is of great significance to the exploration of oil and gas in tight reservoirs. The Chang 1 Member of the Yanchang Formation in the Wanhua Area, Ordos Basin is a new exploration stratum for tight sandstone oil. The lack of understanding of reservoir characteristics and crude oil enrichment rules has seriously restricted the efficient development of oil and gas resources in this stratum. In this study, the reservoir characteristics of the Chang 1 Member in the Wanhua area and the effects of superimposed sand bodies, structures and paleogeomorphology on accumulation of hydrocarbons were systematically studied. The Chang 1 sandstone is a typical ultra-low porosity-ultra-low permeability reservoir, and it has experienced destructive diagenesis of mechanical compaction, pressure solution and cementation, and constructive diagenesis of dissolution. Strong pressure solution caused the secondary enlargement of quartz and feldspar and the formation of patchy dense mosaic structures. The target layer has experienced argillaceous, siliceous and carbonate cementations. Moreover, the sandstone reservoir in the Chang 1 Member also experienced strong dissolution, and it is the main factor for the formation of secondary pores and the improvement of reservoir physical properties. The study also found that the main types of pores in the Chang 1 Member are intergranular dissolved pores and remaining intergranular pores. Superimposed sand bodies, nose-shaped uplifts, dominant facies and eroded paleo-highlands have significant effects on the hydrocarbon accumulation. Based on this study, it was found that the migration and accumulation mode of hydrocarbons in the Chang 1 reservoir belongs to the ladder-like climbing migration + structural ridge accumulation type. In addition, sand body thickness is an important controlling factor for the hydrocarbon accumulation. At present, the discovered crude oil in the Chang 1 Member is always distributed in the areas with thick sand bodies (>20 m), and most of the sand bodies have a thickness in the range of 25–40 m, and the effective thickness is in the range of 2–6 m. In addition, the eroded highlands are the highest topographic units, they are favorable areas for the large-scale accumulation of oil and gas

Genetic basis of the early heading of high-latitude weedy rice

Japonica rice (Oryza sativa L.) is an important staple food in high-latitude regions and is widely distributed in northern China, Japan, Korea, and Europe. However, the genetic diversity of japonica rice is relatively narrow and poorly adapted. Weedy rice (Oryza sativa f. spontanea) is a semi-domesticated rice. Its headings are earlier than the accompanied japonica rice, making it a potential new genetic resource, which can make up for the defects of wild rice that are difficult to be directly applied to japonica rice improvement caused by reproductive isolation. In this study, we applied a natural population consisting of weedy rice, japonica landrace, and japonica cultivar to conduct a genome-wide association study (GWAS) of the heading date and found four loci that could explain the natural variation of the heading date in this population. At the same time, we developed recombinant inbred lines (RILs) crossed by the early-heading weedy rice WR04-6 and its accompanied japonica cultivar ShenNong 265 (SN265) to carry out a QTL mapping analysis of the heading date and mapped four quantitative trait locus (QTLs) and three epistatic effect gene pairs. The major locus on chromosome 6 overlapped with the GWAS result. Further analysis found that two genes, Hd1 and OsCCT22, on chromosome 6 (Locus 2 and Locus 3) may be the key points of the early-heading character of weedy rice. As minor effect genes, Dth7 and Hd16 also have genetic contributions to the early heading of weedy rice. In the process of developing the RIL population, we introduced fragments of Locus 2 and Locus 3 from the weedy rice into super-high-yielding japonica rice, which successfully promoted its heading date by at least 10 days and expanded the rice suitable cultivation area northward by about 400 km. This study successfully revealed the genetic basis of the early heading of weedy rice and provided a new idea for the genetic improvement of cultivated rice by weedy rice

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe

Adaptive Object Tracking via Multi-Angle Analysis Collaboration

Although tracking research has achieved excellent performance in mathematical angles, it is still meaningful to analyze tracking problems from multiple perspectives. This motivation not only promotes the independence of tracking research but also increases the flexibility of practical applications. This paper presents a significant tracking framework based on the multi-dimensional state⁻action space reinforcement learning, termed as multi-angle analysis collaboration tracking (MACT). MACT is comprised of a basic tracking framework and a strategic framework which assists the former. Especially, the strategic framework is extensible and currently includes feature selection strategy (FSS) and movement trend strategy (MTS). These strategies are abstracted from the multi-angle analysis of tracking problems (observer’s attention and object’s motion). The content of the analysis corresponds to the specific actions in the multidimensional action space. Concretely, the tracker, regarded as an agent, is trained with Q-learning algorithm and ϵ -greedy exploration strategy, where we adopt a customized rewarding function to encourage robust object tracking. Numerous contrast experimental evaluations on the OTB50 benchmark demonstrate the effectiveness of the strategies and improvement in speed and accuracy of MACT tracker

Microstructure and Mechanical Properties of Hot- Rolled and Cold-Rolled Medium-Mn TRIP Steels

This study investigated the microstructure and mechanical properties of hot-rolled and cold-rolled medium-Mn transformation-induced plasticity (TRIP) steel. The experimental steel, processed by quenching and tempering (Q & T) heat treatment, exhibited excellent mechanical properties for hot-rolled and Q & T steels (strength of 1050⁻1130 MPa and ductility of 16⁻34%), as well as for cold-rolled and Q & T steels (strength of 878⁻1373 MPa and ductility of 18⁻40%). The mechanical properties obtained after isothermal holding at 775 °C for one hour for cold-rolled/Q & T steel were superior to that of hot-rolled/Q & T steel. Excellent mechanical properties were attributed to the large amount of retained austenite, which produced a discontinuous TRIP effect. Additionally, the differences in mechanical properties correlated with the morphology, stability and content of retained austenite. The cold-rolled sample, quenched from 650 °C (CR 650°C) had extensive TRIP effects in the middle and late stages of the deformation, leading to better mechanical properties. The fracture modes of the hot-rolled sample, quenched from 650 °C, and the cold-rolled sample quenched from 650 °C, were ductile fractures, resulting in excellent ductility

High-sensitivity HBV DNA test for the diagnosis of occult HBV infection: commonly used but not reliable

Occult hepatitis B virus (HBV) infection (OBI) is a condition in which replication-competent viral DNA is detected in the liver (with detectable or undetectable HBV DNA in serum) of individual testing negative for HBV surface antigen (HBsAg). It is a risk factor for transfusion or transplant transmission, reactivation after immunosuppression or chemotherapy, and progression of chronic liver disease and hepatocarcinogenesis. The long-term stable presence of covalently closed circular DNA (cccDNA), which is fully replicative in the nucleus of infected hepatocytes is the molecular basis for the formation of OBI. HBV genome in liver tissue, HBV DNA and anti-HBc test in serum are the gold standard, common method and alternative markers for OBI diagnosis, respectively. Due to the stability of covalently closed circular DNA (cccDNA) and the long half-life of hepatocytes, the existence of OBI is extensive and prolonged. The low and/or intermittent replication of HBV in OBI patients, the limitations of the sensitivity of serological tests, and the non-standardized and invasive nature of liver histology render the “commonly used” serological tests are unreliable and the “gold standard” liver histology is impractical, thus the findings from studies on the formation, diagnosis and transplantation or transfusion transmission of HBV in OBI strongly suggest that the “alternative” marker, the anti-HBc test, may be the most reliable and practical approach for OBI diagnosis

An Overview on Generative AI at Scale With Edge–Cloud Computing

As a specific category of artificial intelligence (AI), generative artificial intelligence (GenAI) generates new content that resembles what humans create. The rapid development of GenAI systems has created a huge amount of new data on the Internet, posing new challenges to current computing and communication frameworks. Currently, GenAI services rely on the traditional cloud computing framework due to the need for large computation resources. However, such services will encounter high latency because of data transmission and a high volume of user requests. On the other hand, edge-cloud computing can provide adequate computation power and low latency at the same time through the collaboration between edges and the cloud. Thus, it is attractive to build GenAI systems at scale by leveraging the edge-cloud computing paradigm. In this overview paper, we review recent developments in GenAI and edge-cloud computing, respectively. Then, we use two exemplary GenAI applications to discuss technical challenges in scaling up their solutions using edge-cloud collaborative systems. Finally, we list design considerations for training and deploying GenAI systems at scale and point out future research directions

An Overview on Generative AI at Scale with Edge-Cloud Computing

As a specific category of artificial intelligence (AI), generative artificial intelligence (GenAI) generates new content that resembles what is created by humans. The rapid development of GenAI systems has created a huge amount of new data on the Internet, posing new challenges to current computing and communication frameworks. Currently, GenAI services rely on the traditional cloud computing framework due to the need for large computation resources. However, such services will encounter high latency because of data transmission and a high volume of requests. On the other hand, edge-cloud computing can provide adequate computation power and low latency at the same time through the collaboration between edges and the cloud. Thus, it is attractive to build GenAI systems at scale by leveraging the edge-cloud computing paradigm. In this overview paper, we review recent developments in GenAI and edge-cloud computing, respectively. Then, we use two exemplary GenAI applications to discuss technical challenges in scaling up their solutions using edge-cloud collaborative systems. Finally, we list design considerations for training and deploying GenAI systems at scale and point out future research directions