Design of Web Agents Inspired by Brain Research

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Design of Web Agents Inspired by Brain ResearchMaya Dimitrova1, Hiroaki Wagatsuma2, and Yoko Yamaguchi21 Institute of Control and System Research, Bulgarian Academy of Sciences,Acad. G. Bonchev Str., Block 2, P.O.Box 79, 1113 Sofia, Bulgariadimitrova@icsr.bas.bg2 Laboratory for Dynamics of Emergent Intelligence, RIKEN BSI,Hirosawa, Wako-shi, Saitama 351-0198, Japan{waga, yokoy}@brain.riken.jpAbstract. The paper presents an approach to combine knowledge from memoryand brain sciences with information retrieval research in the design of Webagents. An information retrieval agent for classification of Web pages based ongenre features is used. In developing the agent to adapt to users’ searchpreferences, a neuro-cognitive model of human episodic memory is employed.Our studies show that neuro-realistic models, capable of abstraction ofmeaningful fragments of knowledge, rather than snapshots of the retrieved Webpages, are closer to the human way of interacting with the Web and can be usedfor optimization of agent performance.Keywords: information retrieval, Web genre, theta phase precession theory,episodic memory.1 IntroductionUsable Web agents have to be adaptive to the demands of the users as well as to the constant dynamic change of Web knowledge. In a way, Web agents have to mimic the natural flow of human memory processing - to filter useful information and remember meaningful facts - in the context of the unceasing flow of new knowledge from the resources on the Web. It is becoming essential that Web agents are able to predict future knowledge needs of the diversity of Web users based on their own – the agents’ – experience [1], [2].It has been proposed that artificial agents – as an agent subgroup – have autobiography – in terms of the ability of “story-telling”, i.e. reconstruction of their own past [1]. The framework is applied to autonomous robot behavior. Web agents are similar to autonomous robots in many ways – in terms of learning, remembering, planning, and decision-making. In this paper we outline a distinction between the navigational (automatic) and the autobiographical (cognitive) aspects of Web browsing on the part of the user, which makes the navigational analogy between the artificial and the human agents’ performance incomplete.Design of Web agents is also concerned with identifying lexical or linguistic features of Web genre (not just topic relevance) like, for example, expertise, detail, subjectivity, etc. that can be captured by automatically scanning/tokenizing htmlscripts and extracting meaningful information [3], [4], [5]. In order to make the process more efficient and sufficiently fast, a lot of heuristically based knowledge is employed. In some cases, these heuristics are grounded in long-tradition research areas like rule-based reasoning, neural networks, linguistics and cognitive science. Our aim is to relate the derived heuristics closer to insights from recent memory and brain modeling studies.In the present study we have focused on the phenomenology of episodic memory, and on one facet of its contribution to the formation of human autobiographical memory in Web context – the extraction of meaningful fragments from the search episodes and its relevance to optimization of Web agent performance. The theta phase precession theory of hippocampal memory is chosen among related models for its account of complex cognitive phenomena like object-context integration, single-trial learning and time-space contextualization [6], [7]. The employed neuro-realistic model is capable of abstraction of meaningful fragments of knowledge, rather than snapshots of the retrieved Web pages, which makes it closer to the human way of interacting with the Web and is appropriate for information retrieval optimization.2 Information Retrieval Studies based on Cognitive Heuristics One application area of the information retrieval studies and information extraction algorithms dealing with a variety of stylistic and genre features is the Semantic Web, where software agents are built to analyze, restructure, transform and display the information, which currently exists in human-readable form. The algorithms used for discovering regularities in the html script that are meaningful from a user-viewer perspective and capable of extracting structural and semantic meaning from the text are called wrappers. The term wrapper is first proposed in [8] and, by definition, is a set of “relatively simple information extraction procedures for semi-structured resources like texts”. Since the amount of regularities is enormous and constantly changing in the Web pages, a wrapper is a learner and in many cases learning is based on exposing to examples to extract the inner data or linguistic/text model. Structural meaning is conveyed by domain ontologies, which can be both designer-given and user-modified. The process of wrapper construction is called wrapper induction, which can be either handcrafted or automatically learned [9]. Semantic meaning is not limited to semantic structure only and can account for the diversity of user styles, preferences, attitudes, experiences, etc. [3], [10]. Being on the server side and used for server performance optimization via collecting and processing user requests, wrapper performance and evolution as a learning algorithm is based on the dynamics of the regularities in the page content as well as on the multiple user interactions with the Web.In earlier work we designed a Java-servlet (a wrapper to a search engine) to diagnose the level of expertise and detail of a corpus of on-line available Web pages, employing cognitive science based heuristics [4], [11]. One application area of these studies is search for cardiological knowledge on the Web. Currently, we have implemented a MatLab GUI version of the wrapper to test the neurologically inspired algorithm of remembering the significant fragments of the retrieved pages duringWeb search. The idea is that the evolution of the meaningful fragment search for the current user can facilitate the definition of the needed expert level of the text by the agent.Fig. 1. Fragment extraction and recombination of textual elements as a process of encoding of search eventsFigure 1 presents the general idea of wrapper performance (fragment extraction and recombination of textual elements) as a process of encoding of search events. In following the history of interaction, optimization is needed much the way natural cognitive systems perform - learn, abstract, remember and forget. We have used the ‘autobiographical memory’ metaphor to apply to such kind of Web agents and the theta phase precession theory to model it [6], [7], [12].3 Theta Phase Coding in the Brain and Episodic Memory Synaptic connections among nerve cells are widely thought to contribute to the functional selectivity of the nerve assemblies involved in specific cognitive tasks. Rhythmic neural activity is another collective property very frequently observed in the brain. One focus of brain research is how the instantaneous property of rhythmic activity benefits the programming of the brain. Theta rhythmic activity in the rat hippocampus is observed in spatial navigation, and it has recently been also found in cortical areas during mental calculation tasks [12]. Theta phase modulation is a promising candidate for a neural-plasticity correlate of episodic memory tasksinvolving hippocampal-cortical relations within a global network of brain computation.In freely running rats, a stable theta oscillation, 8-10 Hz,1 is observed in the hippocampus as the local field potential (LFP). According to the cognitive map theory [13], any given set of cells that act selectively according to the rat's spatial location, called place cells, represents an external map. O'Keefe and Recce [14] recently discovered a phenomenon of systematic acceleration 2of the firing phase of the cell relative to theta rhythm as the animal traversed the cell’s receptive field, called “theta phase precession”. In other words, place cells fire not only depending on the position, but also depending on the behavioral context. In every theta cycle, the firing phase shifts in advance, so that cell assemblies in the cycle appear to be a temporal sequence representing the visiting places according to the running direction. As a result, the duration of the behavioral sequence is compressed ten-fold and embedded in the cycle of the theta rhythm [6], [7].The relevance to learning attracted researchers’ interest in phase precession in the rat hippocampus. The basic properties of the rhythm interaction system, known as the entrainment phenomenon, appear to govern this phase precession and it is possible that the rhythmic pattern appears in the absence of memory to cause memory formation [6], [7]. The emergence of rhythmic patterns in the absence of memory to cause its formation is a plausible candidate for a neural correlate of the formation of complex and durable cognitive effects, governing future behavior i.e. autobiographical memories.The Theta Phase Model. Traditionally, a phase model is used as a model of a limit cycle with a single variable of oscillation phase, assuming the amplitude converging to a unit circle, with states either the oscillation or the resting. Sustained oscillation is given as a rotary motion on the unit circle in the presence of the external input. To generate the phase precession, the important assumption is a gradual increase of the natural frequency of the phase model during the oscillation state. In the network model of the hippocampus with the phase model description, the stable oscillation of the theta rhythm, a LFP theta unit, and neurons with oscillatory activities are assumed in the entrance of the hippocampus, called the entorhinal cortex (EC). When an input is coming, the neuron starts to fire and sustains its oscillation with the gradual increase of the natural frequency. According to the coupling with the LFP theta unit, the firing phase regularly advances from the late phase to the earlier phase.Through the anatomical projection the EC to the hippocampal CA3 layer (CA3), the temporal firing pattern generated in EC is inherited to CA3 units and it modifies the recurrent synaptic connections among CA3 units in accordance with the Hebbian synaptic plasticity with asymmetric time window. As a result, in the temporal coding by using theta phase precession, the behavioral sequence is compressed into the temporal sequence of firing phases in the theta cycle, and it makes possible the encoding of behavioral episodes even in one-time experience, because of the time-compression and the repetition of the firing pattern in several theta cycles. After the formation of synaptic connections asymmetrically and locally connected, the 1Theta rhythm is 4-10Hz in human, but 8-10 Hz in rat experiments2The firing phase advances from the late phase to the earlier phase in the phase precessionhippocampal network can replay the spatio-temporal firing pattern that represents the animal’s past experience, as the memory retrieval. The retrieved activity may cause further synaptic plasticity in other cortical areas for memory consolidation or memory transfer from the hippocampus to the cortex.Fig. 2. Model of hippocampal nerve circuit: (a) mechanism of theta phase coding. Behavioral inputs are transferred to the firing phase in the entorhinal cortex (EC) and provide the asymmetric connections in the hippocampus; (b) when the rat runs to the right, the phase shift in firing within each theta rhythm cycle occurs in place cells 1 to 3, which are activated sequentially; the phase is arranged in order of firing within one phase cycleThis phase coding has an important advantage over rate coding in storing temporal sequences of events. The temporal compression of an input sequence into every theta cycle ensures a highly selective synaptic plasticity over the entire time period of an on-going experience, while in rate coding the synaptic selectivity is lost easily when the sequence has a variety of time scales. This precession model bridges the gap between the fixed value of a time interval (several tens of milliseconds) that is recognized by the synapse and the experienced event (seconds). A variety of input time series are translated by the theta rhythm, thereby giving the synapse the proper temporal difference and enabling experienced events to be encoded on the spot.Relevance to Episodic Memory. Episodic memory as the building element of autobiographical memory has been the focus of intensive and elaborate studies and modeling at various levels of memory and brain research – human, animal, cognitive, neural-physiological, brain-imaging, and so on. In human memory research the term ‘episode’ has been used both operationally i.e. ‘the learning episode’ and metaphorically, i.e. ‘episodic memory’. Both derive from Tulving’s theory [15], [16] and refer, in the first case, to the situation or the environment where learning occurs, and in the second, to the information processing system underlying the acquisition of new knowledge - and is therefore stressing its processing complexity. Autobiographical memory, on the other hand, is responsible for remembering the significant events in one’s biography [17]. The distinction is also in terms of the different kind (or scale) of information extraction to build the idea/memory of (a fragment of) an episode or an event.The phenomenon we have aimed to observe in our study is the structure of the episodic trace in terms of stored page (in)completeness, search path memorizing and insight-related experiences that may support optimized future retrieval from memory.4 Autobiographical Memory in Web ContextAutobiographical memory includes all that we have ever experienced, but more importantly, all that we have ever learned. In this sense, as a significant part of one’s daily life, the user-Web interaction is a powerful autobiographical learning experience. We have hypothesized that the process of episodic learning in Web environment is both navigational and autobiographical at the same time. Navigation focuses on the optimization of the trajectory (the traversed links) to a desired goal for subsequent reuse of the shortest path to reach the same goal. With using the Web for educational purposes, however, the situation is different in terms of the cognitive functions and memorizing strategies that are involved, including their autobiographical elements. Our first assumption is that the interaction with the present-day Web is an autobiographical experience. Our second assumption is that memorizing events in a human-like memory system undergoes constant synthesis principally the same way as any learning process takes place and therefore can be implemented in autonomous agents on the Web.We use ‘episodic memory’ in its conceptual meaning – the system of processing episodes of Web search and transforming them into autobiographical events of successful or unsuccessful Web navigation to reach the needed goal. Every Web page viewed by the user is a complex perceptual and semantic stimulus. We assume that what is stored in episodic memory from the rapid viewing of a succession of pages is a set of fragments. The stored fragments are cues for subsequent retrieval. These cues can be perceptual or semantic, random or meaningful to the goal. The theoretical hypothesis is that episodic memory extracts meaningful cues from the fragments of the page-retrieval episodes, rather than random perceptual or semantic cues.The Search Task. The search task was to reach a significant personal goal from incomplete initial information. We involved two participants in the study in order to monitor the autobiographical elements of the task – a European researcher, browsing for an art gallery (motivated) and a Japanese colleague, invited to find the gallery based on Japanese orthography (neutral). The controlled elements were: a) personal motivation in reaching the goal and b) emotional involvement in terms of feelings of anticipation to reach the goal, i.e. anticipation of success. We have aimed to show that the mechanisms of event synthesis from fragments of page-retrieval episodes in the episodic memory system are the same for both participants, volunteering to the study.The Search Goal. The search goal was an example of new concept learning and, for a foreign language speaker, similar to a case of artificial concept learning. The cue concept is composed of two Japanese words ‘kurenai-kai’ which to a non-Japanese speaker may appear like anagrams. Kurenai-kai is related to traditional Japanese craft arts - Japanese style embroidery. The participants did not know the concept ‘kurenai-kai’, although it would have been the keyword, retrieving the home page of the needed art gallery. The discovery of the new concept and its remembering was one important element of the observed episodic memory based retrieval.Procedure. We tested memory of the search process in two steps - each on two subsequent days. The first step was to see if the participants remembered the newly learned concept words to reach the goal the shortest way and the answer was affirmative. The second step tested episodic memory of the actual search process and the remembered search path. The participants were asked to try and recall how theywould have searched the same goal provided they had had forgotten the concept words ‘kurenai-kai’. They copied and pasted their trials to reach the links leading to the needed home page of the art gallery.Results and Discussion. The presented in section 3 model of episodic memory has been successful in reproducing the behavior of rats in a maze in finding the location of a goal. In a maze behavior starts from random search until the shortest path is found. Moreover, the model is capable of reproducing situations of single-trial learning by composing and maintaining retrievable memories of the search-path cues[6], [7]. Our study of Web search has replicated the mechanism of performance of the proposed memory model based on theta phase precession, and has supported its advantages for implementation in autobiographical Web agents. Table 1 gives a summary of the performed searches by our participants. The Web paths resemble random search until a meaningful path is composed.Table 1. User performance on Web search with incomplete initial information about the search goalUser Searches TimeRandom Meaningful Approximate Motivated 23 3-5 30 minNeutral 19 3-5 30 minWe have observed that meaningful paths are composed , rather than encountered that is essential to the understanding of episodic memory. The nature/character of the search was identical in both cases. It started randomly with possible combinations of various cue words and browsing the search engine results until the rewarding (success) combination of cue words was found. The success combination was the same for both users – kurenai-kai – in either English or Japanese orthography. We assume that the ‘event’ of finding meaningful and useful information in terms of the newly learned concept word ‘kurenai-kai’ in one’s autobiographical memory is based on cognitive mechanisms similar to those in learning new concepts in semantic memory [18]. Our results are an illustration to this similarity of processing of knowledge and events in human memory in Web search context.Table 2. The left column represents episodic memory guided retrieval based on a set of meaningful cues and ‘insight’. The right column contains the mappings to the respective initial cues (none of them are identical)Steps NoEpisodic memory guided retrieval Composition of the retrieval path from meaningful fragments Snapshots of the search episodes 1 Looking for a matchingsemantic cue or a usefulword phrase/”Embroidery Center inAtlanta”/2 Closest match with the14th search step, butpicking up anotherhyperlink3 Remembering that the insightful encounter of‘kurenai-kai’ was in the‘links’ section4 Mapping the ‘link’ cuewith the 17th search step(the ‘insight’)5 The goal siteTable 2 shows the meaningful cues on which episodic retrieval was based for our motivated participant. The retrieval process is composed of fragments of meaningful information starting from a matching semantic cue, retrieving previous page, remembering the ‘insightful’ encounter in the links section, taking the remembered abstract cue and finding the goal (5 steps). At ‘episodic’ retrieval our motivated participant usually started from more personalized information about the people running the art gallery (which was her initial motivation for the search, i.e. element of her autobiography). Our neutral, i.e. native-speaker participant started from more general sites about the Japanese craft arts. However, the essential aspect of their search is reflected in the aforementioned 5 steps. An interesting point to be mentioned is that the retrieval pages never matched exactly the search pages. Our participants mapped the meaningful cues , not the exact images of the Web pages.Implementation . Figure 3 gives the temporal evolution of episodic events in the task to try to find ‘kurenai-kai’ with initial keywords of ‘Embroidery Center in Atlanta.’ Gword, TL, and Word correspond to the keywords, used in the Google search, the titles of the search results, seen by the user in the system window, and the words in the summary of the search results, respectively, which are automatically stored in the system. For an autonomous agent on the Web the relevance of this study is in the following: Users base subsequent retrieval of the search path on rememberingthe ‘events’ of their search, which are the meaningful fragments of knowledge inside the Web pages, not the exact ‘page-retrieval’ episodes.Fig. 3. Temporal evolution of episodic events by the neuro-cognitive agentWe propose that the autonomous Web agent behaves in analogy with the event-based (autobiographical) strategy that Web users are applying for retrieval of useful information, which is some state of knowledge with anticipatory value (useful in the future). Next steps will be tests of the agent for optimized storage of fragments of knowledge from user searches, rather than of snapshots of sequences of Web pages.5 Conclusions and Future WorkIn this paper we presented one approach towards the design of Web agents that are able to mimic the natural flow of human memory processing in complex environments, which is based on new knowledge from brain research. We have tried to demonstrate how the theoretical framework of theta phase coding endows real-time process of memory formation of Web search experiences, and the retrieval process of effective routes to the targets through the experienced sites. The proposed approach focuses on bridging the gap between neuroscience and Web technologies to enlighten the importance of the context-dependence in autobiographic memory. Acknowledgement. This research was carried out while the first author was a visiting researcher at the Laboratory for Dynamics of Emergent Intelligence of RIKEN BSI on a scholarship granted by Japan Society for Promotion of Science. The implementation is partially supported by research project contract No MI-1509/2005“Multimodal User and Sensor Interface for a Computer-aided System for Cardiological Diagnosis and Intervention” of the National Research Fund of Bulgaria. References1. Nehaniv, Ch., Dautenhahn, K.: Embodiment and Memories: Algebras of Time and Historyfor Autobiographic Agents. Proc. 14th European Meeting on Cybernetics and Systems Research, EMCSR (1998) 651-6562. Ekdahl, B.: Agents as Anticipatory Systems. Proc. 4th World Multiconference on Systemics,Cybernetics and Informatics (SCI 2000) and 6th International Conference on Information Systems Analysis and Synthesis (ISAS 2000) Orlando, Florida, U.S.A. (2000)3. Kushmerick, N.: Gleaning Answers from the Web. Proc. AAAI Spring Symp. on MiningAnswers from Texts and Knowledge Bases, Palo Alto (2002) 43-454. Dimitrova, M., Kushmerick, N.: Dimensions of Web Genre. WWW2003, Budapest,Hungary, /cdrom/papers/poster/p143/p143-dimitrova.htm (2003)5. zu Eissen, S. M., Stein, B:. Genre Classification of Web Pages: User Study and FeasibilityAnalysis. In: Biundo S., Fruhwirth T., Palm G. (eds.): Advances in Artificial Intelligence, Springer (2004) 256-2696. Yamaguchi, Y.: A Theory of Hippocampal Memory Based on Theta Phase Precession.Biological Cybernetics, Vol.89 (2003) 1-97. Wagatsuma, H, Yamaguchi, Y.: Neural Dynamics of the Cognitive map in the Hippocampus.Cognitive Neurodynamics, 1 (2007) 1871-40808. Kushmerick, N., Weld, D.S., Doorenbos, R.B.: Wrapper Induction for InformationExtraction. Int. Joint Conference on Artificial Intelligence IJCAI (1997) 729-7379. Hogue, A., Karger, D.: Wrapper Induction for End-User Semantic Content Development.Proc. First Int. Workshop on Interaction Design and the Semantic Web ISWC (2004)10. Karlgren J.: Stylistic experiments in information retrieval. In T. Strzalkowski (Ed.) NaturalLanguage Information Retrieval, Kluwer (1999)11. Dimitrova, M.: Cognitive Modelling and Web Search: Some Heuristics and Insights.Cognition, Brain, Behavior, Vol. VII, 3 (2003) 251-25812. Mizuhara, H., Wang, L.Q., Kobayashi, K., Yamaguchi, Y.: A Long-Range CorticalNetwork Emerging with Theta Oscillation in a Mental Task. Neuroreport, Vol.15, 8 (2004) 1233-123813. O’Keefe, J., Nadel, L.: The Hippocampus as a Cognitive Map. Clarendon Press, Oxford(1978)14. O’Keefe J., Recce, M.: Phase Relationship between Hippocampal Place Units and the EEGTheta Rhythm. Hippocampus, 3 (1993) 317-33015. Tulving, E.: Elements of Episodic Memory. New York: Oxford University Press (1983)16. Lepage, M., Ghaffar, O., Nyberg, L., Tulving, E.: Prefrontal Cortex and Episodic MemoryRetrieval Mode. PNAS, Vol. 97, 1 (2000) 506-51117. Conway, M.A.: Autobiographical Knowledge and Autobiographical Memories. In: Rubin,D.C. (Ed.): Remembering Our Past. Studies in Autobiographical Memory, CambridgeUniversity Press (1996)67-9318. Thompson-Schill, S.L.: Neuroimaging Studies of Semantic Memory: Inferring "How" from"Where". Neuropsychologia, 41 (2003) 280-292。

我最难忘的一个人钱学森英语作文

我最难忘的一个人钱学森英语作文全文共6篇示例，供读者参考篇1The Most Unforgettable Person - Qian XuesenHave you ever met someone who completely changed your view of the world? Someone who inspired you to dream bigger and work harder than you ever thought possible? For me, that person was Qian Xuesen - a brilliant scientist, humble teacher, and true patriot. Even though I was just a young kid when I met him, the lessons he taught me will stay with me forever.It was a crisp autumn day when Mr. Qian visited my elementary school. I'll never forget walking into the auditorium and seeing this kind-looking old man with a warm smile on the stage. Little did I know, he was one of the greatest scientific minds of the 20th century! As he began speaking in a soft, grandfatherly voice, I was immediately drawn in by his gentle demeanor and passion for learning.Mr. Qian spoke about his incredible journey - how he went from a small village in China to studying under the legendary scientists of CalTech and MIT. He talked about the wonders ofrocketry and aerodynamics with such enthusiasm, you couldn't help but get excited too! But what struck me most was his humility. Despite his brilliance and accomplishments, he never boasted or acted arrogant. He made complex ideas understandable and encouraged us to keep questioning and exploring."The more I learn, the more I realize how little I truly know," he said with a chuckle. "That's the beauty of science - there is always more to discover if you approach it with an open, curious mind."After his speech, a few of us lucky students got to ask Mr. Qian some questions. With each inquisitive youngster, his face would light up with delight, as if being quizzed by a child was the greatest honor. He answered every query patiently and thoroughly, never talking down to us. In fact, he made us feel like the questions we asked were the most profound ever contemplated!I timidly raised my hand, my heart pounding with anticipation of speaking to such a legendary man. "Mr. Qian, what advice would you give to a young student who dreams of becoming a great scientist like you one day?"He looked me straight in the eyes, and with a warm smile said, "My dear young friend, the most important qualities for any scientist are diligence, integrity, and patriotism. Work hard, be honest, and use your talents to serve your country and your people. If you cultivate those virtues, there is no limit to what you can achieve."To this day, I carry those words with me like a torch, guiding me through life's challenges and motivating me to give my best effort. Mr. Qian didn't just teach science - he taught the value of noble character, the importance of contributing to society, and the power of caring more about truth and knowledge than fame or fortune.After the Q&A, I shyly approached Mr. Qian to get his autograph and, though I was nervous, he immediately put me at ease with his gentle kindness. As he signed my notebook, he asked about my interests and ambitions. I told him I loved stargazing and reading about space exploration. His face lit up with childlike wonder as he enthusiastically described the breathtaking marvels of the cosmos, making me feel like my curiosity about the universe was the most wondrous thing. He encouraged me to feed that fascination through studying and hard work."Keep reaching for the stars, young friend," he said warmly as he patted my shoulder. "The greatest journey of exploration begins with the inquisitive mind of a child."From that day forward, I was inspired to learn everything I could about math, physics, chemistry - anything related to unraveling the mysteries of the heavens. Mr. Qian showed me that the universe is an astounding puzzle, begging to be solved by those daring enough to embrace life's biggest questions with scientific reasoning and childlike awe.When I would get frustrated while struggling with a daunting concept, I remembered Mr. Qian's words about diligence and integrity, and I refused to take shortcuts or get discouraged. I thought of how he persevered through numerous challenges and setbacks throughout his remarkable career with quiet determination and dignity. It gave me strength and kept me going, no matter how difficult the problem.Years later, my passion for space sciences took me all the way to university, where I pursued the dream that Mr. Qian first inspired in me as a starry-eyed kid. While the coursework was incredibly demanding, I never lost my sense of wonder about the cosmos. In fact, each new revelation about the majesty of our universe reinvigorated my curiosity, transporting me back tohow I felt listening to Mr. Qian describe the galactic marvels - utterly enthralled and craving to know more.When I struggled with my thesis research, I remembered the humility Mr. Qian displayed in admitting how little he knew compared to the enormity of all there is to learn. It motivated me to embrace setbacks as opportunities rather than failures.Solving complex mysteries takes grit, creativity and childlike inquisitiveness.Now, as an aerospace engineer helping design new satellites and spacecraft, I still carry Mr. Qian's lessons of patriotism with me each day. I know I am fortunate to use my talents in service of exploring the cosmic frontier for the benefit of human knowledge and my nation's advancement. His wise words about contributing to society through diligent scientific work keep me focused on creating innovations that can make the world a better place.I realize how incredibly lucky I was to meet Qian Xuesen as an impressionable young student. His humility, wisdom, and passion for learning shaped the course of my life in ways I never could have imagined that fateful autumn day in the school auditorium. Though he has since passed away, his spirit lives on - in the countless students he inspired, in the groundbreakingwork he pioneered, and in the hopeful minds of all children who still gaze upon the starry night sky and dream of one day unraveling its cosmic secrets.Whenever I witness a rocket blasting off toward the heavens, I smile knowingly and hear Mr. Qian's warm voice echo through my mind... "Keep reaching for the stars, young friend." Thanks to his powerful influence, that's exactly what I'll never stop doing.篇2My Most Unforgettable Person: Qian XuesenWho is the most unforgettable person in my life? That's an easy question for me to answer - it's the famous Chinese scientist Qian Xuesen! You might be surprised that a kid like me would pick a scientist as the most memorable person, but let me explain why Dr. Qian is so special to me.First of all, Dr. Qian was a total brainiac! He was one of the smartest people who ever lived. As a young man, he studied aerospace engineering at MIT and the California Institute of Technology. Even though he grew up in China, he became an expert on rocket science and aerodynamics in the United States. During World War II, he helped design awesome fighter planesand did secret work for the U.S. military on long-range rocket technology. Talk about a genius!But what really makes Qian Xuesen unforgettable is the crazy life he lived. It's like a novel or movie script! After the war ended in 1945, the American government suspected him of being a communist spy. They never charged him with a crime, but they kept a close eye on him and wouldn't let him work on any more important military projects. Fed up with being mistreated, Dr. Qian decided to return to China in 1955.However, when he got to the airport, he was suddenly arrested by the FBI! The agents accused him of trying to share top-secret information with China. Dr. Qian insisted he was innocent, but he was thrown in prison anyway. He was kept in terrible conditions and denied visits from his wife and children for five agonizing years.In 1960, after years of protests from scientists, the government finally allowed Qian Xuesen to be released and deported back to China. But here's where his story takes an incredible turn - China's leader at the time, Chairman Mao Zedong, welcomed him as a hero! Mao put Dr. Qian in charge of China's brand new rocketry program. With his brilliant mind andAmerican training, Qian helped transform China into a major space power.Under Dr. Qian's leadership, China launched its first satellite in 1970 and its first crewed spacecraft in 2003. He made huge contributions to China's missile defense systems and guided many of the country's top scientists and engineers. Qian Xuesen's work allowed China to catch up to the United States and Russia in some areas of aerospace technology. No wonder he's called the "Father of Chinese Rocketry!"What I find most inspiring about Qian Xuesen is how he never gave up, no matter how many obstacles life threw at him. Being falsely accused as a spy and spending years in prison would have broken most people's spirits. But not Dr. Qian! He remained dedicated to science and to helping China move forward technologically. His perseverance and love for learning in the face of injustice is something I really admire.Dr. Qian passed away in 2009 at the amazing age of 98. But his incredible life story lives on as an inspiration for all Chinese people, especially us students. His journey from a small village to the heights of rocket science, and from an American prison to a position of great honor in China, shows what one person canachieve through brilliance, hard work and an unshakeable dream.I may only be in elementary school, but Qian Xuesen has already taught me so many valuable lessons. If I study hard and never quit when things get tough, I can make my wildest ambitions come true, just like he did. I want to have the same curiosity, courage and commitment to excellence as the great Dr. Qian. He will always be my hero and role model.I feel very lucky to have learned about this unforgettable man while still a child. Qian Xuesen's amazing life journey has inspired me to aim high, work hard, and contribute something meaningful to society when I grow up, just as he did. I know I'll never forget the "Father of Chinese Rocketry" and the powerful example he set through his accomplishments and character. He will forever be my most unforgettable person.篇3Certainly, here is an essay about Qian Xuesen, written in the voice of an elementary school student, with a length of around 2,000 words.My Most Unforgettable Person: Qian XuesenHave you ever heard of Qian Xuesen? He is one of the most amazing people in China's history, and he's someone I really look up to. Let me tell you all about him!Qian Xuesen was born in 1911 in Hangzhou, which is a beautiful city not too far from where I live. He was a very smart kid and loved to learn about math and science. When he grew up, he became a brilliant scientist and engineer, and he helped to build some of the first rockets and missiles in China.What makes Qian Xuesen so special is that he overcame a lot of challenges in his life. When he was young, he went to study in the United States because he wanted to learn from the best scientists in the world. He worked really hard and became an expert in rocket science.However, during World War II, the American government accused him of being a spy for China, even though he didn't do anything wrong. They put him in prison for five years! Can you imagine how scary and unfair that must have been?After he was released, Qian Xuesen was forced to leave the United States and return to China. But instead of feeling angry or bitter, he used his knowledge to help his home country. He became the leader of China's rocket and missile program, and he trained many other scientists and engineers.Thanks to Qian Xuesen's hard work and dedication, China was able to launch its first satellite into space in 1970. This was a huge achievement for the country, and it showed the world that China was becoming a leader in science and technology.But Qian Xuesen's impact goes beyond just rockets and satellites. He believed that education was the key to solving many of the world's problems. He encouraged young people to study science and engineering, and he helped to set up new universities and research centers in China.One of the things I admire most about Qian Xuesen is his perseverance. Even when things were really tough, he never gave up on his dreams and his goals. He kept working hard and believing in himself, and that's why he was able to accomplish so much.Another thing I love about Qian Xuesen is his humility. Despite all of his amazing achievements, he remained a humble and down-to-earth person. He never boasted or thought he was better than others. Instead, he focused on using his knowledge and skills to help make the world a better place.Qian Xuesen passed away in 2009 at the age of 98, but his legacy lives on. He is known as the "Father of Chinese Rocketry"and is celebrated as a national hero in China. There are even schools and museums named after him!Whenever I feel discouraged or like giving up on something, I think about Qian Xuesen and how he never gave up, no matter how hard things got. He inspires me to work hard, never stop learning, and always believe in myself.I hope that one day, I can make a positive impact on the world like Qian Xuesen did. Maybe I'll become a scientist or an engineer, or maybe I'll find another way to use my talents and skills to help others. Whatever I do, I'll always remember the example set by this incredible man.Qian Xuesen proved that with hard work, perseverance, and a commitment to learning and helping others, anyone can achieve great things. He is truly one of the most unforgettable people in Chinese history, and I feel lucky to have learned about his amazing life.篇4My Most Unforgettable Person: Qian XuesenHave you ever heard of Qian Xuesen? He's someone really important in China's history, and I find him absolutely fascinating! Let me tell you all about him.Qian Xuesen was born in 1911 in Hangzhou, which is a beautiful city not too far from where I live. Even as a young boy, he was incredibly smart and loved studying math and science. Can you imagine being a kid genius like that? I can barely solve my math homework sometimes!When Qian grew up, he went to study in the United States. He was such a brilliant student that he got to work on top-secret rocket projects for the American government during World War II. Imagine being trusted with secrets like that at such a young age!After the war ended, Qian wanted to return to China to help his home country. But the American government didn't want him to leave because they were afraid he would share the rocket secrets with China. So, they kept him in a house on American soil for five long years, not allowing him to go anywhere! That must have been really hard for Qian.Finally, in 1955, Qian was allowed to return to China after making a deal with the American government. When he cameback, he was welcomed as a hero! The Chinese leaders were so happy to have such a brilliant scientist join their team.Qian went on to lead China's missile and space programs, helping the country develop its first missiles and satellites. He's often called the "Father of Chinese Rocketry" because of his huge contributions to the field.One of the coolest things Qian did was to help send China's first satellite, called Dong Fang Hong I, into space in 1970. Imagine being part of such an important moment in history! I would have been so excited to see that satellite launch.Qian also helped establish many important scientific institutions in China, like the Beijing Institute of Control Devices. He trained countless students and researchers, passing on his knowledge to the next generation.Even though Qian achieved so much in his career, he remained a very humble and hardworking person. He often said that he was just an "ordinary citizen" doing his duty for his country. But we all know he was extraordinary!Qian's life wasn't always easy, though. He faced many challenges and setbacks, like being kept in America against hiswill. But he never gave up on his dreams and always worked hard to make them a reality.To me, Qian Xuesen is a true hero and an inspiration. He showed that with determination, intelligence, and a love for learning, you can overcome any obstacle and achieve great things. He dedicated his life to advancing science and technology, not just for himself, but for the betterment of his country and the world.I hope that one day, I can be as passionate about something as Qian was about rocketry. And even though I might not become a famous scientist, I want to work hard and never give up on my dreams, just like he did.Qian Xuesen's story is one of perseverance, brilliance, and patriotism. He is a true icon in China's history and someone I will always admire and remember. I feel so lucky to have learned about such an amazing person at a young age. Who knows, maybe his story will inspire me to become a scientist or engineer one day too!篇5My Most Unforgettable Person: Qian XuesenHave you ever heard of Qian Xuesen? He's one of the most amazing people in Chinese history! I learned all about him in school, and he's become my biggest hero. Let me tell you why he's so awesome and unforgettable.Qian Xuesen was born in 1911 in Hangzhou, which is a beautiful city with lots of cool gardens and ancient buildings. Even as a young kid, he was incredibly smart and loved studying science and math. Can you imagine being a genius from such an early age? I can barely tie my shoes without help!When he grew up, Qian went to study at some of the best universities in the world, like MIT and Caltech in America. He was one of the top scientists working on rocket technology and even helped the American space program in its early days. Qian understood all about aerodynamics, which is the study of how air flows around objects like airplanes and rockets. Pretty cool, right?But then something sad happened. In the 1950s, the American government didn't trust Qian because he was Chinese. They accused him of being a communist spy, even though there was no real proof! That must have been so unfair and hurtful. The US deported him back to China in a big unpleasant controversy.At first, Qian was very upset about leaving America since he had worked there for so many years. But then he realized China really needed his brilliant mind to help develop its own rocket and space technology. Qian decided to dedicate himself fully to helping his home country advance in science.With Qian's leadership, China was able to make huge leaps in aerospace. He helped establish the philosophy that China's missile and space programs should focus on practical applications to improve people's lives, not just make powerful weapons. Qian pushed for using rockets to launch satellites that could forecast the weather, survey natural resources, and even broadcast TV signals!Qian is sometimes called the "Father of Chinese Rocketry" because of his enormous contributions. Under his guidance, China was able to successfully launch its first missile in 1960 and first satellite in 1970. He even trained an entire generation of brilliant scientists and engineers. Qian's work laid the foundation for China's later achievements like sending rovers to the Moon and Mars.Despite being forced to leave America, Qian never gave up his dream of scientific exploration to benefit humanity. He worked tirelessly until the very end, asking his students to put"Patriotism, Honesty and Diligence" above all else. To me, Qian Xuesen is the perfect example of persistence, brilliance, and service to one's country.Qian had to overcome so many challenges, like being falsely accused, deported against his will, and having to basically restart his life's work from scratch. But he never complained or gave up. He just focused on using his amazing mind to make the world a better place through science. That's why he's my biggest hero and most unforgettable person.I hope I can be as smart, hardworking and dedicated as Qian Xuesen one day. His story of overcoming injustice and pursuing knowledge for the greater good will always inspire me. China would not be the great technological power it is today without the "Father of Chinese Rocketry" helping to lead the way. Qian proved that with enough perseverance and patriotism, one person really can change the course of a nation.篇6My Unforgettable Hero: Qian XuesenHave you ever had someone you really admire and look up to? Someone who has accomplished great things and made the world a better place? For me, that person is Qian Xuesen, abrilliant scientist and one of the founders of China's aerospace industry. His life story is truly inspiring, and he will forever be my hero.I first learned about Qian Xuesen when I was in third grade. Our teacher told us about his amazing achievements and how he overcame so many challenges to become a successful scientist. I was fascinated by his story and wanted to know more about him.Qian Xuesen was born in 1911 in Hangzhou, China. He was a gifted student from a young age and excelled in mathematics and science. In 1935, he went to study in the United States and later became one of the founding engineers of the American rocket program. During World War II, he made significant contributions to the development of ballistic missiles.However, Qian Xuesen's life took a dramatic turn when he was accused of being a communist sympathizer and deported from the United States in 1955. Despite this setback, he remained dedicated to his scientific pursuits and returned to China, where he played a crucial role in establishing China's aerospace industry.One thing that really impressed me about Qian Xuesen was his perseverance and determination. Even when faced with adversity and injustice, he never gave up on his dreams andcontinued to work tirelessly to advance scientific knowledge. He once said, "Difficulties are meant to be overcome; they are not excuses for giving up."Qian Xuesen's achievements are truly remarkable. He is regarded as the "Father of Chinese Rocketry" and made groundbreaking contributions to the development of China's first satellite, the Dong Fang Hong I, which was launched in 1970. He also played a pivotal role in the establishment of China's first missile and space programs.But what I admire most about Qian Xuesen is not just his scientific accomplishments, but also his character and values. Despite facing discrimination and injustice, he never harbored resentment or bitterness. Instead, he remained humble, compassionate, and dedicated to serving his country and advancing human knowledge.In his later years, Qian Xuesen focused on promoting education and scientific research in China. He believed that education was the key to a nation's progress and worked tirelessly to inspire and mentor young scientists and engineers. He once said, "The future of a nation lies in the hands of its youth, and their education is the foundation upon which a prosperous and powerful nation is built."Qian Xuesen's life story has taught me many valuable lessons. It has shown me the importance of perseverance, dedication, and never giving up on your dreams, no matter how difficult the challenges may seem. It has also taught me the value of humility, compassion, and using one's talents and abilities to make a positive difference in the world.As I grow older, I hope to emulate Qian Xuesen's example and pursue my passions with the same level of dedication and perseverance. I want to make a positive impact on the world, just like he did, and inspire others to never give up on their dreams, no matter how daunting the obstacles may seem.Qian Xuesen's legacy will live on forever, and he will always be my hero – a shining example of what can be achieved through hard work, determination, and an unwavering commitment to the pursuit of knowledge and progress.。

6月全国大学英语四级考试全真试题

洛基英语，中国在线英语教育领导品牌Part I Writing (30 minutes)Directions: For this part, you are allowed 30 minute to write a short essay on the topic of students selecting their lectures. You should write at least 120 words following the outline given bellow:1. 越来越多的博物馆免费对外开放的目的是什么？2. 也会带来一些问题3. 你的看法？Free admission to museums (本题142 分)Part II Reading Comprehension (Skimming and Scanning) (15 minutes)Directions: In this part, you will have 15 minutes to go over the passage quickly and answer the questions on Answer Sheet 1. For questions 1-7, choose the best answer from the four choices marked A),B),C) and D). For questions 8-10, complete the sentences with the information given in the passage.How Do You See Diversity?As a manager, Tiffany is responsible for interviewing applicants for some of the positions with her company .During one interview, she noticed that the candidate never made direct eye contact. She was puzzled and somewhat disappointed because she liked the individual otherwise.He had a perfect resume and gave good responses to her questions, but the fact that he never looked her in the eye said “untrustworthy,”so she decided to offer the job to her second choice.“It wasn’t until I attended a diversity workshop that I realized the person we passed over was the perfect person,”Tiffany confesses. What she hadn’t known at the time of the interview was that the candidate’s “different”behavior was simply a cultural misunderstanding . He was an Asian-American raised in a household where respect for those in authority was shown by averting(避开) your eyes.“I was just thrown off by the lack of ye contact; not realizing it was cultural,”Tiffany says. “I missed out ,but will not miss that opportunity again.”Many of us have had similar encounters with behaviors we perceive as different. As the world becomes smaller and our workplaces more diverse, it is becoming essential to expand our under-standing of others and to reexamine some of our falseassumptions .Hire AdvantageAt a time when hiring qualified people is becoming more difficult ,employers who can eliminate invalid biases(偏爱) from the process have a distinct advantage .My company, Mindsets LLC ,helps organizations and individuals see their own blind spots . A real estate recruiter we worked with illustrates the positive difference such training can make .“During my Mindsets coaching session ,I was taught how to recruit a diversified workforce. I recruited people from different cultures and skill sets .The agents were able to utilize their full potential and experiences to build up the company .When the real estate market began to change, it was because we had a diverse agent pool that we were able to stay in the real estate market much longer than others in the same profession.”Blinded by GenderDale is an account executive who attended one of my workshops on supervising a diverse workforce . “Through one of the sessions ,I discovered my personal bias ,”he recalls . “I learned I had not been looking at a person as a whole person , and being open to differences .”In his case , the blindness was not about culture but rather gender .“I had a management position open in my department ;and the two finalists were a man and a woman . Had I not attended this workshop , I would have automatically assumed the man was the best candidate because the position required quite a bit of extensive travel . My reasoning would have been that even though both candidates were great and could have been successful in the position , I assumed the woman would have wanted to be home with her children and not travel .”Dale’s assumptions are another example of the well-intentioned but incorrect thinking that limits an organization’s ability to tap into the full potential of a diverse workforce .“I learned from the class that instead of imposing my gender biases into the situation , I needed to present the full range of duties, responsibilities and expectations to all candidates and allow them to make an informed decision .”Dale credits the workshop , “because it helped me make decisions based on fairness .”Year of the Know-It-AllDoug is another supervisor who attended one of my workshops .He recalls a major lesson learned from his own employee.“One of my most embarrassing moments was when I had a Chinese-American employee put in a request to take time off to celebrate Chinese New Year . In myignorance , I assumed he had his dates wrong , as the first of January had just passed . When I advised him of this , I gave him a long talking-to about turning in requests early with the proper dates .“He patiently waited , then when I was done , he said he would like Chinese New Year did not begin January first , and that Chinese New Year ,which is tied to the lunar cycle ,is one of the most celebrated holidays on the Chinese calendar . Needless to say , I felt very embarrassed in assuming he had his dates mixed up . But I learned a great deal about assumptions , and that the timing of holidays varies considerably from culture to culture .“Attending the diversity workshop helped me realize how much I could learn by simply asking questions and creating dialogues with my employees , rather than making assumptions and trying to be a know-it-all ,”Doug admits . “The biggest thing I took away from the workshop is learning how to be more ‘inclusive’to differences.”A better Bottom LineAn open mind about diversity not only improves organizations internally , it is profitable as well . These comments from a customer service representative show how an inclusive attitude can improve sales .”Most of my customers speak English as a second language . One of the best things my company has done is to contract with a language service that offers translations over the phone . It wasn’t until my boss received Mindsets’training that she was able to understand how important inclusiveness was to customer service . As result , our customer base has increased .”Once we start to see people as individuals . and discard the stereotypes , we can move positively toward inclusiveness for everyone . Diversity is about coming together and taking advantage of our differences and similarities . It is about building better communities and organizations that enhance us as individuals and reinforce our shared humanity .When we begin to question our assumptions and challenge what we think we have learned from our past , from the media, peers , family , friends , etc , we begin to realize that some of our conclusions are flawed(有缺陷的) or contrary to our fundamental values . We need to train our-selves to think differently , shift our mindsets and realize that diversity opens doors for all of us ,creating opportunities in organizations and communities that benefit everyone .请根据上面给出的内容，来回答下面的单项选择题（下列每小题备选答案中，只有一个符合题意的正确答案。

2024届四川省西昌市川兴中学英语高三上期末学业水平测试试题含解析

2024届四川省西昌市川兴中学英语高三上期末学业水平测试试题请考生注意：1．请用2B铅笔将选择题答案涂填在答题纸相应位置上，请用0．5毫米及以上黑色字迹的钢笔或签字笔将主观题的答案写在答题纸相应的答题区内。

写在试题卷、草稿纸上均无效。

2．答题前，认真阅读答题纸上的《注意事项》，按规定答题。

第一部分（共20小题，每小题1.5分，满分30分）1．________ makes our school famous is ________ more than 90% of the students have been admitted to universities.A．What; that B．That; becauseC．That; what D．What; because2．If an entire country gets richer at the same time, individuals wouldn't ________ feel wealthier, since their relative positions in society haven't changed.A．necessarily B．initially C．typically D．sustainably3．—Do you ________ ready for the spring outing?—No,I still have to buy some fruit.A．everything B．anything C．something D．nothing4．Premier Li Keqiang officially ______ the plan to develop the Bay Area in his government work report delivered at the start of the National People’s Congress session in March.A．laid out B．laid offC．laid down D．laid up5．Creating an atmosphere ______ employees feel part of a team is a big challenge. A．where B．whoseC．that D．which6．One is expected to behave _________ on some special occasions.A．accurately B．precisely C．appropriately D．rightly7．It was in the remote mountain areas ______ I spent the gap year ______ I learned how to work with a group of strangers and how to look after myself.A．where; that B．when; thatC．where; when D．that; where8．Adding to their happiness, he says,______ some similarities between the two test papers.A．is B．are C．has D．have9．—Why not buy a second-hand car first if you don’t have enough mone y for a new one?—That’s a good ________.A．saying B．questionC．account D．suggestion10．You can use this room for your club activities tomorrow ______ you keep it tidy and clean.A．for fear that B．in caseC．on condition that D．even if11．Lily is a good singer. How I wish I ________ as well as her.A．sing B．will singC．had sung D．sang12．August 8th，2008 is a special day，________________，I think，that will be remembered by the Chinese forever .A．one B．whatC．which D．the one13．Every great accomplishment rests on the of what came before it; when you trace it back, you’ll see one small step that started it all.A．reputation B．expectationC．recreation D．foundation14．She must have ranked her birthday presents in order of _because the top one is her favorite．A．value B．time C．interest D．preference15．She said her ________ is enough to support her in later years and that her only hope is that her son can visit regularly. After all she had not much time left.A．pension B．benefit C．allowance D．bonus16．--You know Mr. Green has been ill for days?--Yes, I wonder if he is ______ better now.A．any B．some C．any D．no17．Personally speaking, ________the grand blueprint into reality is a long process. A．turning B．turnC．turned D．having turned18．The education system in China is very different from ________ in the USA．A．one B．itC．those D．that19．I appreciate ______ the opportunity to work in your company two years ago. A．giving B．to give C．being given D．to be given20．—What’s up? Y ou look down. —I have piles of papers ________, but I type so slowly.A．to be typed B．typed C．to type D．being typed第二部分阅读理解（满分40分）阅读下列短文，从每题所给的A、B、C、D四个选项中，选出最佳选项。

介尺度中的复杂性——人工智能发展中的共性挑战

ResearchArtiﬁcial Intelligence—PerspectiveComplexity at Mesoscales:A Common Challenge in Developing ArtiﬁcialIntelligenceLi Guo a ,b ,Jun Wu c ,Jinghai Li a ,b ,⇑aState Key Laboratory of Multiphase Complex Systems,Institute of Process Engineering,Chinese Academy of Sciences,Beijing 100190,China bSchool of Chemical Engineering,University of Chinese Academy of Sciences,Beijing 100049,China cSchool of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,Chinaa r t i c l e i n f o Article history:Received 15July 2018Revised 29July 2019Accepted 12August 2019Available online 21August 2019Keywords:Artiﬁcial intelligence Deep learning Mesoscience MesoscaleComplex systema b s t r a c tExploring the physical mechanisms of complex systems and making effective use of them are the keys to dealing with the complexity of the world.The emergence of big data and the enhancement of computing power,in conjunction with the improvement of optimization algorithms,are leading to the development of artiﬁcial intelligence (AI)driven by deep learning.However,deep learning fails to reveal the underlying logic and physical connotations of the problems being solved.Mesoscience provides a concept to under-stand the mechanism of the spatiotemporal multiscale structure of complex systems,and its capability for analyzing complex problems has been validated in different ﬁelds.This paper proposes a research paradigm for AI,which introduces the analytical principles of mesoscience into the design of deep learn-ing models.This is done to address the fundamental problem of deep learning models detaching the physical prototype from the problem being solved;the purpose is to promote the sustainable develop-ment of AI.Ó2019THE AUTHORS.Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.This is an open access article under the CC BY-NC-ND license(/licenses/by-nc-nd/4.0/).1.AI has achieved signiﬁcant development and increasingly become a common multidisciplinary techniqueIn recent years,various scientiﬁc and technological break-throughs have become a reality.AlphaGo,developed by Google,defeated top Go players such as Lee Sedol and Ke Jie;driverless cars have traveled safely for millions of kilometers and obtained legal driving rights in more than ten states in the United States;and image-and speech-recognition techniques have gradually matured and been widely used in consumer products such as cameras and smartphones,bringing great convenience.As a result,the old term ‘‘artiﬁcial intelligence”(AI)has reappeared in the public sight,trig-gering a new round of technological revolution.Today,AI is included in the major development strategies of many countries,and is regarded as a core capability [1–4].As AI is closely related to many ﬁelds,it is expected to become a model framework for interdisciplinary research and to further promote the coordinated development of various ﬁelds.AI has existed for more than 60years,since its birth at the Dartmouth conference in 1956.The development of its main-stream techniques has gone through three key periods:the reason-ing period,knowledge period,and learning period.From 1956to the early 1970s,AI research was in the reasoning period,and mainly concentrated on rule-based symbolic representation and reasoning;the representative achievements in this period were various automatic theorem-proving procedures [5].However,with the increasing difﬁculty of problems,it was challenging for machi-nes to be intelligent purely based on logical reasoning.Hence,some researchers turned from the exploration of general thinking law to the application of specialized knowledge,and AI research entered the knowledge period.From the early 1970s to the end of the 1980s,a large number of expert systems [6]were estab-lished and achieved remarkable results in speciﬁc application areas.However,with the expansion of the application scale,it was very difﬁcult to summarize knowledge and then teach it to computers.Therefore,some researchers advocated having comput-ers learn knowledge from data automatically,and AI research entered the learning period.Since the early 1990s,AI research has been devoted to machine learning theory and algorithm research [7].Machine learning has not only made breakthroughs⇑Corresponding author.E-mail address:jhli@ (J.Li).in traditional AI tasks such as image recognition [8–10]and speech recognition [11,12],but also played an important role in many novel applications,such as predicting the activity of poten-tial drug molecules [13],analyzing particle accelerator procedure [14],reconstructing brain circuits [15],identifying exoplanets [16],diagnosing skin cancer [17],and predicting the effects of mutations in non-coding DNA on gene expression and disease [18,19].The revival of AI can be attributed to three main factors.First,the signiﬁcant progress of data acquisition,storage,and transmis-sion techniques has resulted in big data,which is an essential resource for AI research.Moreover,the maturity of high-performance computing techniques and the emergence of power-ful computing hardware (e.g.,graphics processing units (GPUs)and central processing unit (CPU)clusters)have laid a solid foun-dation for the study of st but not least,researchers have accu-mulated abundant experience and skills in modeling large-scale complex problems,leading to the rapid development of machine learning methods represented by deep neural networks [20],which provide an effective approach to study AI.Today,AI is a mul-tidisciplinary technique,which can be used in any area that requires data analysis.2.Deep learning is prevailing,but its physical mechanism remains unclearA deep learning model is actually a multilayer artiﬁcial neural network.To avoid confusion,it is hereinafter referred to as a deep neural network,the structure of which is shown in Fig.1.The model consists of three main parts:the input layer,hidden layer (s),and output layer.Each node of the input layer corresponds to one dimension of the input data (e.g.,a pixel of image),each node of the output layer corresponds to a decision variable (e.g.,a semantic category),and the hidden layers are made up of many ‘‘neurons.”In terms of biological mechanism,a neuron receives a potential signal transmitted by other neurons and will be activated and out-put a signal when the accumulated signal is higher than its ownpotential.This process can be formalized as y ¼f w T x þb ÀÁ,where x ¼x 1ÁÁÁx n ½ T denotes a multidimensional input signal,y denotes aone-dimensional output signal,w ¼w 1ÁÁÁw n ½ T denotes the weight of the input signal,b denotes a bias,and f is an activation function.It can be seen that a deep neural network is essentially a mathe-matical model produced by nesting a simple function hierarchi-cally.Although the deep neural network is inspired by neurophysiology,its working principle is far from the brain simu-lation depicted by the media.In fact,the working principle of the human brain has not yet been fully explored.When many neurons are hierarchically organized as a deep neural network,this deep model is equivalent to a nested composite function,and each layer of the network corresponds to a nonlinear mapping (the output signal of the previous layer is used as the input signal for the next layer).Signal transmission throughout the network can be formally described as y ¼f W L ÁÁÁf W 2f W 1x þb 1ðÞþb 2½ ÁÁÁþb L f g ,where W l and b l l ¼ð1;2;:::;L Þrespectively denote the weight matrix and the bias vec-tor at the l th layer (i.e.,the model parameters to be solved).Here,the model parameters are packed into matrices and vectors,since each layer of the network contains multiple neurons.Given the application task,a loss function (used to measure the difference between the actual and expected outputs of a deep neural net-work)should be designed ﬁrst,and then the model parameters can be solved by optimizing the loss function with a backpropaga-tion algorithm [21],so that a multilevel abstract representation collectively hidden in a dataset can be learned.Illustrated by statistical learning theory [22],the more numer-ous the parameters are,the higher the model complexity will be,and hence the stronger the learning ability will be.We can increase the model complexity of a deep neural network by ‘‘widening”or ‘‘deepening”it.The latter works better in practice.While ‘‘widen-ing”only increases the number of basic functions,‘‘deepening”increases not only the number of functions,but also the layers of function nesting,making it more powerful in view of functional expression.Therefore,‘‘deepening”is more helpful in improving model complexity and learning ability.Taking the ImageNet com-petition in the computer vision ﬁeld as an example,neural archi-tecture is getting deeper and deeper,from the seven-layer AlexNet [8]to the 16-layer VggNet [9],and then to the 152-layer ResNet [10].At present,the deepest neural network has reached thousands of layers,with the number of model parameters reach-ing as many as severalbillions.Fig.1.An example of a deep neural network.Eight images on the bottom right of Fig.1was adapted from ImageNet ( ).L.Guo et al./Engineering 5(2019)924–929925It is worth noting that one crucial aspect of a deep neural net-work is how to design the neural architecture reasonably.Existing neural architectures have mostly been developed manually by human experts,which is a time-consuming and error-prone pro-cess.Therefore,a great deal of effort is currently being put into automatic machine learning(AutoML),with a particular focus on methods of automated neural architecture search(NAS)[23].The concept behind NAS is to explore a search space—which deﬁnes all of the architectures that can be represented in principle—by Bayesian optimization[24],reinforcement learning[25],or neuro-evolutionary learning[26],with the goal ofﬁnding the architec-tures that achieve high predictive performance on unseen data.Although deep learning has achieved many successes,the inter-pretability of such a model remains unclear.When researchers apply a deep neural network to model their problems,they regard it as a‘‘black box”and only focus on the input and output.Most employed neural architectures are designed entirely based on the experience and intuition of the researchers,which fail to link the problem to be solved with its physical background.Although the calculation process of a deep neural network can be explicitly rep-resented by a mathematical formula,it is difﬁcult to explain it at the level of physical meaning.The model lacks a physical connota-tion that can reﬂect the essence of the problem.Even if AutoML is helpful toﬁnd‘‘better”neural architectures in terms of predictive performance,it still cannot be physically explained.A few researchers have tried to explain a deep neural network in terms of some speciﬁc application tasks.Taking image recognition as an example,researchers performed a de-convolution operation on a deep convolutional neural network(CNN)to visualize the visual features learned by the layers,hoping to explain the microscopic process of image recognition[27].However,such a heuristic expla-nation is not universal and can hardly be extensively applied to other cases;furthermore,it fails to reveal the physical mechanism of the model.Therefore,the interpretability of the deep neural net-work is a bottleneck in its further development.3.Mesoscience is expected to be a possible solution to reveal the physical mechanism of deep learning and further promote the development of AIAssociating the design of a deep neural network with the physi-cal mechanism of the problem to be solved is a prerequisite for realizing breakthrough progress in AI,and the universality of the physical mechanism determines the scope of AI applications, which is a fundamental problem for AI in the future.Mesoscience is based on the idea that complexity originates from the compromise in the competition between two or more dominant mechanisms in a system,resulting in a complex spa-tiotemporal dynamic structure[28].Almost all systems studied in AI research are complex systems.Introducing mesoscience prin-ciples and methods into AI research(mainly in respect to deep neural networks)might be a promising way to address the afore-mentioned problems.Starting with the study of gas–solidﬂuidization in chemical engineering[29],mesoscience has been consecutively applied to studies in gas–liquidﬂuidization[30],turbulenceﬂow[31,32],pro-tein structure[33],catalysis[34],and so forth.The universal law has now been gradually summarized.The main spirit of meso-science can be summarized as follows[35,36]:In general,a com-plex problem has multiple levels,each of which has multiscale characteristics,and different levels are related to each other.A complex system consists of countless elements,and there is likely a spatiotemporal multiscale structure between the system and ele-ment scale due to the collective effect of elements.There are three types of regimes in such a structure(taking the case of a system controlled by two dominant mechanisms as an example),with completely different properties when the boundary and external conditions are changed:A–B regime:This regime is jointly controlled by physical mechanisms A and B,and is known as the mesoregime.The structure of the mesoregime shows the alternation of the two states,which is controlled by the compromise in competition between mechanism A and mechanism B.In this regime,the system structure conforms to the following:minAB!A-dominated regime:As the external conditions change, mechanism B disappears and mechanism A alone dominates the system.In this case,the system structure characteristics are simple and in line with the following:A¼minB-dominated regime:As the external conditions change in the opposite direction,mechanism A disappears and mechanism B dominates the system;the system structure now conforms to the following:B¼minMost importantly,the transition between the A,A–B,and B regimes is often accompanied by sudden changes in the system’s characteristics and function.The problem handled by deep learning can often be regarded as a complex system.The correlation between the input and output of such a system is usually modeled as a nonlinear nested ing mesoscience theory to examine existing deep learning models,this paper proposes the following research and application mode of mesoscience-based AI:Supposing that there is a huge training dataset,we want to establish a model to express the inherent laws of the dataset through deep learning.According to the concept and logic of meso-science,the following steps should be taken:(1)Analyze these data to determine how many levels they involve.(2)For each level,analyze the existence of three regimes.(3)If it belongs to regime A or regime B,the structure is simple and can be solved by the existing deep learning technique.(4)If it is in the A–B mesoregime,the system has a signiﬁcant spatiotemporal dynamic structure.It is necessary to analyze its physical dominant mechanismsﬁrst;next,use a multi-objective variational model for the two or more mechanisms with the classi-cal gradient descent to help the model training.(5)After analyzing each level,carry out association and integra-tion among different levels.Examining the above steps,we found that,for problems belong-ing to regime A or regime B,the extreme conditions are relatively simple from the mesoscience perspective.Thus,the existing deep learning techniques can be used to quickly iterate to the solution to establish the mathematical model.However,for problems in the A–B mesoregime,the correlation between the input and output is controlled by multiple—at least two—physical mechanisms. Therefore,a time-consuming parameter-learning procedure is inevitable,if the conventional deep learning method is used.Alter-natively,if the physical mechanism is decomposedﬁrst,according to the concept of mesoscience,and the multi-objective variational method is adopted to analyze different control mechanisms,cou-pled with the classical backpropagation algorithm,the deep learn-ing solution satisfying the error condition can be obtained more quickly.926L.Guo et al./Engineering5(2019)924–929For complex problems dealt with by deep learning,if the con-trol mechanism is analyzedﬁrst at the physical level,the model can be established following the above steps.This is helpful to speed up the model training and facilitate a deeper understanding of the physical nature of the system.A general method to address the intrinsic problems in AI may be to introduce the analytical and processing means of mesoscience into deep learning,such as region decomposition,dominant mecha-nisms identiﬁcation,and the multi-objective variational method.4.The problem-solving paradigm of AI can be improved by mesoscienceA generalﬂowchart of AI theoretical and application research based on deep learning is shown in Fig.2.The main steps can be summarized as follows:(1)Collecting training data:Gather(sufﬁcient)data from application scenarios(often complex systems),and label the data-set if supervised learning is involved.(2)Constructing a deep neural network:Choose a suitable neural architecture and optimization algorithms to train a statisti-cal model that can capture the potential patterns hidden in the dataset.(3)Applying the model:Predict results for new data using the well-trained model.The essential step in such aﬂowchart is to construct the deep neural network.At present,researchers entirely rely on their own experience and intuition to complete this step,due to the ‘‘black box”issue inherent in artiﬁcial neural networks.To this end,we suggest that mesoscience principles and methods could be considered for the construction of a deep neural network.The resulting improvedﬂowchart is shown in Fig.3.Solving the problem of complex systems is a critical goal of AI, as illustrated by Figs.2and3;complex systems provide both appli-cation scenarios and massive datasets for AI.The human brain is also a complex system.On the one hand,brain science[37]—that is,studying the material basis and mechanism of human think-ing—is an important support for the future development of AI. On the other hand,improving our understanding of complex sys-tems will also help brain science research.The recent success of the deep learning technique can be regarded as a mathematical success.If the research results of brain science can be integrated into AI in the future,this will inevitably and signiﬁcantly promote the research and application of AI.Brain scientists have been trying to reveal the secrets of the human brain,not only from the perspective of biology and anat-omy,but also in terms of the development of the cognitive mech-anism.By combining this knowledge of how intelligence occurs with advanced computer hardware and software technology,it is possible to build an‘‘artiﬁcial brain”that may be comparable to the human counterpart.However,different people use their differ-ing abilities to solve problems with their brain after receiving cor-responding education and training,even though different peoples’brains have identical structure and functions.The key is that when a problem arises,people make use of their understanding of the problem’s physical nature together with their brain’s reasoning and induction abilities to obtain the correct solution within a lim-ited time.Therefore,the ability of AI to solve practical problems should depend on the progress of brain science,the development of information technology(IT),the understanding of the physical nature of the problems,and the effective integration and coupling among them.Although mesoscience originates from theﬁeld of chemical engineering,its basic principles are pervasively applicable to other complex systems.The core concept of mesoscience is toﬁnd the multilevel correlations and multiscale associations in the system, as well as to identify the mesoregimes and their physical dominant mechanisms in different levels.Next,the multi-objective varia-tional method is used to seek the law of compromise in competi-tion of the dominant mechanisms in order to solve the problem. In the new paradigm illustrated by Fig.3,mesoscience plays an important role in improving the model architecture and learning algorithm,in addition to improving related computing hardware and computational methods.From the mesoscience concept to AI applications,many issues remain to be explored.For example,Google’s AlphaGo Fan[38] adopted deep reinforcement learning—which integrates the per-ception ability of deep learning with the decision-making ability of reinforcement learning—to beat the human Go world champion. By combining the deep reinforcement learning technique with the Monte Carlo search strategy,AlphaGo assesses the current board situation through the value network to reduce the search depth, and uses the policy network to reduce the search width,in order to improve the search efﬁciency.AlphaGo is a successful applica-tion example of deep reinforcement learning.From the perspective of system structural analysis,deep reinforcement learning can be divided into three levels:①tens of thousands of perceptrons,②several deep learning networks,and③deep reinforcement learning strategies.These levels coincide with the three scalesin Fig.2.Schematic of existing AI research and applications.L.Guo et al./Engineering5(2019)924–929927mesoscience:①element scale,②mesoscale,and ③system scale.It is worth investigating whether it is possible to directly apply analytical methods to deep reinforcement learning.Notably,DeepMind has developed four main versions of AlphaGo:Fan,Lee,Master,and Zero.The earlier versions of AlphaGo such as Fan and Lee [38]are trained by both supervised learning and reinforcement learning,while the latest version AlphaGo Zero [39]is solely trained by self-play reinforcement learning without any human knowledge,and use a single deep neural network rather than separate policy and value networks.Here,we take only AlphaGo Fan as an example,for two reasons:First,AlphaGo Fan [38]is the most complicated version,and this paper focuses on ana-lyzing complex systems;thus,AlphaGo Fan is the most typical case among the four versions.Second,no matter whether policy and value networks are separated (e.g.,AlphaGo Fan and Lee [38])or merged (e.g.,AlphaGo Zero [39]),they still correspond to the mesoscale in accordance with the mesoscience concept.Another example is the generative adversarial network (GAN)[40],which is one of the most popular and successful deep learning models.GAN performs learning tasks by means of a mutual game between the generative model and the discriminative model.GAN’s goal is to generate pseudo data consistent with the distribu-tion of real data by using a generative model with the help of a dis-criminative model.These two models have their own goals as well;the generative model attempts to generate data that can deceive the discriminative model,while the discriminative model strives to distinguish the generated data from the real data.In the process of establishing the GAN,the two models are mutually restrained,and each tries to lead in the direction of its own advantage.Finally,under the constraint of the GAN objective function,the two models reach equilibrium and compromise with each other.If the behavior of the two models is regarded as being equivalent to the two dominant mechanisms,A and B ,in the mesoregime,then the GAN’s training is the process of compromise in the competition of two dominant mechanisms in the mesoregime.In this way,the spirit of mesoscience may be beneﬁcial when training a GAN model and further boosting its applications.Through an analysis of the progress of AI and big data during the past years,two conclusions can be drawn,as shown in Fig.3:①With the continuous development of brain science,the working principle of the human brain is gradually being revealed,and a breakthrough could be realized in AI using such achievements.②Big data has its own complexity.In order to tackle the complex-ity behind big data and build a physical model conforming to the objective law,it is necessary to identify the physical mechanismbehind the complexity.The above two aspects are logically consis-tent;that is,exploring the physical mechanism of complex systems and making effective use of them are the keys to dealing with com-plexity.Reﬂecting this logic,this paper advocates applying the principle and methods of mesoscience to AI.5.ConclusionThe emergence of big data,along with the advancement of com-puting hardware,has prompted great development in AI,leading to its applications in many ﬁelds.However,due to certain problems inherent in deep learning,the interpretability of deep learning is limited.Although mesoscience originates from chemical engineer-ing,its analytical methods—which include multilevel and multi-scale analysis,as well as the idea of compromise in the competition of dominant mechanisms in the mesoregime—can also be applied to other complex systems.In recent years,mesoscience has achieved successful applications in different ﬁelds,and is expected to provide a novel concept to improve the interpretability of deep learning.At present,the proposed mesoscience-based AI is a preliminary research idea,and its veriﬁcation and expansion require the joint effort of researchers from various disciplines.In particular,explo-ration on its speciﬁc applications is required in future.AcknowledgementsWe would like to thank Dr.Wenlai Huang,Dr.Jianhua Chen,and Dr.Lin Zhang for the valuable discussion.We thank the editors and reviewers for their valuable comments about this article.We gratefully acknowledge the support from the National Natural Science Foundation of China (91834303).Compliance with ethics guidelinesLi Guo,Jun Wu,and Jinghai Li declare that they have no conﬂict of interest or ﬁnancial conﬂicts to disclose.References[1]The State Council of China.Development Plans of New Generation ArtiﬁcialIntelligence;2017.[2]European Commission.Artiﬁcial Intelligence for Europe;2018.[3]UK Government Ofﬁce for Science.Artiﬁcial intelligence:opportunities andimplications for the future of decision making;2016.Fig.3.Schematic of mesoscience-based AI research and applications.928L.Guo et al./Engineering 5(2019)924–929[4]National Science and Technology Council,Networking and InformationTechnology Research and Development Subcommittee.The National Artiﬁcial Intelligence Research and Development Strategic Plan.Washington: Executive Ofﬁce of the President of the United State;2016Oct.[5]Chang CL,Lee RC.Symbolic logic and mechanical theorem 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