As a new research direction in contemporary cognitive science,predictive processing surpasses traditional computational representation and embodied cognition and has emerged as a new paradigm in cognitive science rese...As a new research direction in contemporary cognitive science,predictive processing surpasses traditional computational representation and embodied cognition and has emerged as a new paradigm in cognitive science research.The predictive processing theory advocates that the brain is a hierarchical predictive model based on Bayesian inference,and its purpose is to minimize the difference between the predicted world and the actual world,so as to minimize the prediction error.Predictive processing is therefore essentially a context-dependent model representation,an adaptive representational system designed to achieve its cognitive goals through the minimization of prediction error.展开更多
Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness.DR occurs due to the high blood sugar level of the patient, and it is clumsy tobe detected at an early stage as no early symptoms appea...Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness.DR occurs due to the high blood sugar level of the patient, and it is clumsy tobe detected at an early stage as no early symptoms appear at the initial level. To preventblindness, early detection and regular treatment are needed. Automated detectionbased on machine intelligence may assist the ophthalmologist in examining thepatients’ condition more accurately and efficiently. The purpose of this study is toproduce an automated screening system for recognition and grading of diabetic retinopathyusing machine learning through deep transfer and representational learning.The artificial intelligence technique used is transfer learning on the deep neural network,Inception-v4. Two configuration variants of transfer learning are applied onInception-v4: Fine-tune mode and fixed feature extractor mode. Both configurationmodes have achieved decent accuracy values, but the fine-tuning method outperformsthe fixed feature extractor configuration mode. Fine-tune configuration modehas gained 96.6% accuracy in early detection of DR and 97.7% accuracy in gradingthe disease and has outperformed the state of the art methods in the relevant literature.展开更多
Objoctive To identify differential genes between normal ovarian epithelium tissue and ovarian epithelial cancer using representational difference analysis of cDNA （cDNA-RDA）. Methods cDNA-RDA was performed to ident...Objoctive To identify differential genes between normal ovarian epithelium tissue and ovarian epithelial cancer using representational difference analysis of cDNA （cDNA-RDA）. Methods cDNA-RDA was performed to identify the differentially expressed sequences between cDNAs from cancer tissue and cDNAs from normal ovarian tissue in the same patient who was in the early stage of ovarian serous cystadenocarcinoma. These differentially expressed fragments were cloned and analyzed, then sequenced and compared with known genes. Results Three differentially cxpressed cDNA fragments were isolated using cDNA from normal ovarian tissue as tester and cDNA from cancer tissue as driver amplicon by cDNA-RDA. DP Ⅲ- 1 and DP Ⅲ-2 cDNA clone showed significant homology to the cDNA of alpha actin gene; DPⅢ-3 cDNA clone showed significant homology to the cDNA oftransgelin gene. Conclusion cDNA-RDA can bc used to sensitively identify the differentially expressed genes in ovarian serous cystadenocarcinoma. Ovarian serous cystadenocarcinoma involves alteration of multiple genes.展开更多
Objective To screen coronaryartery disease (CAD) specific expressions and clone their genes. Method Blood samples were collected from CAD and non - CAD patients at the end of coronary angiography. mRNA from samples wa...Objective To screen coronaryartery disease (CAD) specific expressions and clone their genes. Method Blood samples were collected from CAD and non - CAD patients at the end of coronary angiography. mRNA from samples was isolated and converted into cDNA. After ligated with specific linkers, the cDNA was amplified with complementary primers. PCR products from CAD samples were named as tester; the ones from non - CAD samples were named as driver. With different ratio of tester to driver (1 : 100,1: 1, 000, and 1: 10, 000), they were mixed, denatured, and renatured. Single strand cD-NA was eliminated by Mung bean nuclease. Double strand cDNA presented only in tester was amplified, ligated in vector pUC19 and pUC53, and transformed into E. coll DH5a. Strains with inserted cDNA fragments were picked up based on blue and white selection. Insertions were screened by endonuclease digestion and DNA sequencing. Results were compared with DNA sequences of GeneBank. Results: After the selection with representational differential analysis, CAD specific cDNA fragments with different sizes (about 1kb, 0. 75kb, and 0. 6kb) were cloned. Among them, two fragments from unknown genes were identified. One presented a 43. 3 % similarity with part of the rattus norvegicus lipocortin gene. Another presented a 45. 4 % similarity with part of the human polynucleotide kinase 3' - phosphatase gene. Conclusion There are at least two CAD specific - ex- pressions from unknown genes that were partially similar to lipocortin and polynucleotide kinase 3'- phos-phatase genes, respectively. Expression of these genes might affect the formation and progression of plaque within coronary artery.展开更多
Understanding the neural substrates of human cognition is a central goal of neuroscience research.Modern imaging techniques,such as functional magnetic resonance imaging(fMRI),provide an opportunity to map cognitive f...Understanding the neural substrates of human cognition is a central goal of neuroscience research.Modern imaging techniques,such as functional magnetic resonance imaging(fMRI),provide an opportunity to map cognitive function in vivo.To date,modeling shared information in task-evoked neural dynamics across individuals remains challenging,largely due to pronounced inter-subject variability in brain anatomy,function,and behaviors[1],[2].An emerging topic,known as hyperalignment or functional alignment,has been proposed recently[3],to map subject-specific neural responses onto a common representational space using either linear transformations of task-evoked neural activity[4]or resting-state connectivity profiles[5].However,these approaches often assume uniform neural responses across individuals,struggling to capture group heterogeneity and model functional interactions between brain areas[6].展开更多
This paper presents an empirical study of the acquisition of English ambiguous verb-locative prepositional phrase constructions （VLPPs） by adult Mandarin and Spanish speakers. This study assumes that the semantic pr...This paper presents an empirical study of the acquisition of English ambiguous verb-locative prepositional phrase constructions （VLPPs） by adult Mandarin and Spanish speakers. This study assumes that the semantic properties of the target VLPPs that relate to change-of-location in sentences such as The boat floated under the bridge arise from an uninterpretable syntactic feature selected by English but unselected by Mandarin Chinese and Spanish. Results obtained from an animated cartoon selection task indicate that neither the Mandarin nor the Spanish speakers at any level of English proficiency possess native-like interpretative knowledge. Tense/ Aspect effects on the interpretation of the target constructions by Spanish speakers were also found. These results are interpreted as consistent with the Representational Deficit Hypothesis view （Hawkins, 2003, 2005） of adult second language acquisition.展开更多
Objective To search differentially expressed sequences correlated with pathogenesis of human nasopharyngeal carcinoma (NPC), including the candidates of tumor suppressor genes Methods Representational difference a...Objective To search differentially expressed sequences correlated with pathogenesis of human nasopharyngeal carcinoma (NPC), including the candidates of tumor suppressor genes Methods Representational difference analysis (RDA) was performed to isolate differentially expressed sequences between cDNA from normal human primary cultures of nasopharyngeal epithelial cells and cDNA from NPC cell line HNE1 The source of differentially expressed products were proved by Southern blot, Northern blot and in situ hybridization The fragments were cloned with pGEM T easy kit and sequenced by the chain termination reaction Results Four differentially expressed cDNA fragments were isolated in the fourth subtractive hybridization using cDNA from normal human nasopharyngeal epithelial cells as tester amplicon and cDNA from NPC cell line HNE1 as driver amplicon by cDNA RDA These differential cDNA fragments revealed that they really came from the tester amplicon and were not expressed or down regulated in the NPC HNE1 cells Some of the genes were expressed only in human nasopharyngeal epithelial cells but deleted or down regulated in the biopsies of NPC Of these obtained clones, some were the sequences of the human known genes including house keeping genes, the others represented novel gene sequences Conclusion The differentially expressed products including the candidates of tumor suppressor genes may be associated with the initiation of the NPC展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Class...Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Classifier(GPT2-ICC),which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins.GPT2-ICC integrates representation learning with a large language model(LLM)-based classifier,enabling highly accurate identification of potential ion channels.Several potential ion channels were predicated from the unannotated human proteome,further demonstrating GPT2-ICC’s generalization ability.This study marks a significant advancement in artificial-intelligence-driven ion channel research,highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data.Moreover,it provides a valuable computational tool for uncovering previously uncharacterized ion channels.展开更多
A personalized outfit recommendation has emerged as a hot research topic in the fashion domain.However,existing recommendations do not fully exploit user style preferences.Typically,users prefer particular styles such...A personalized outfit recommendation has emerged as a hot research topic in the fashion domain.However,existing recommendations do not fully exploit user style preferences.Typically,users prefer particular styles such as casual and athletic styles,and consider attributes like color and texture when selecting outfits.To achieve personalized outfit recommendations in line with user style preferences,this paper proposes a personal style guided outfit recommendation with multi-modal fashion compatibility modeling,termed as PSGNet.Firstly,a style classifier is designed to categorize fashion images of various clothing types and attributes into distinct style categories.Secondly,a personal style prediction module extracts user style preferences by analyzing historical data.Then,to address the limitations of single-modal representations and enhance fashion compatibility,both fashion images and text data are leveraged to extract multi-modal features.Finally,PSGNet integrates these components through Bayesian personalized ranking(BPR)to unify the personal style and fashion compatibility,where the former is used as personal style features and guides the output of the personalized outfit recommendation tailored to the target user.Extensive experiments on large-scale datasets demonstrate that the proposed model is efficient on the personalized outfit recommendation.展开更多
Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays ...Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable,thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making.This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations.The research methodology begins with the compilation of small molecule databases,followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms,capturing patterns and salient features across extensive chemical spaces.The study then examines various drug discovery downstream tasks,including drug-target interaction(DTI)prediction,drug-target affinity(DTA)prediction,drug property(DP)prediction,and drug generation,all based on learned representations.The analysis concludes by highlighting challenges and opportunities associated with machine learning(ML)methods for molecular representation and improving downstream task performance.Additionally,the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine(TCM)medicinal substances and facilitating TCM target discovery.展开更多
Let F_(1)be the virtual field consisting of one element and(Q,I)a string pair.In this paper,we study the representations of string pairs over the virtual field F_(1).It is proved that an indecomposable F_(1)-represent...Let F_(1)be the virtual field consisting of one element and(Q,I)a string pair.In this paper,we study the representations of string pairs over the virtual field F_(1).It is proved that an indecomposable F_(1)-representation is either a string representation or a band representation by using the coefficient quivers.It is worth noting that for a given band and a positive integer,there exists a unique band representation up to isomorphism.展开更多
The modifiedλ-differential Lie-Yamaguti algebras are considered,in which a modifiedλ-differential Lie-Yamaguti algebra consisting of a Lie-Yamaguti algebra and a modifiedλ-differential operator.First we introduce t...The modifiedλ-differential Lie-Yamaguti algebras are considered,in which a modifiedλ-differential Lie-Yamaguti algebra consisting of a Lie-Yamaguti algebra and a modifiedλ-differential operator.First we introduce the representation of modifiedλ-differential Lie-Yamaguti algebras.Furthermore,we establish the cohomology of a modifiedλ-differential Lie-Yamaguti algebra with coefficients in a representation.Finally,we investigate the one-parameter formal deformations and Abelian extensions of modifiedλ-differential Lie-Yamaguti algebras using the second cohomology group.展开更多
Binary Code Similarity Detection(BCSD)is vital for vulnerability discovery,malware detection,and software security,especially when source code is unavailable.Yet,it faces challenges from semantic loss,recompilation va...Binary Code Similarity Detection(BCSD)is vital for vulnerability discovery,malware detection,and software security,especially when source code is unavailable.Yet,it faces challenges from semantic loss,recompilation variations,and obfuscation.Recent advances in artificial intelligence—particularly natural language processing(NLP),graph representation learning(GRL),and large language models(LLMs)—have markedly improved accuracy,enabling better recognition of code variants and deeper semantic understanding.This paper presents a comprehensive review of 82 studies published between 1975 and 2025,systematically tracing the historical evolution of BCSD and analyzing the progressive incorporation of artificial intelligence(AI)techniques.Particular emphasis is placed on the role of LLMs,which have recently emerged as transformative tools in advancing semantic representation and enhancing detection performance.The review is organized around five central research questions:(1)the chronological development and milestones of BCSD;(2)the construction of AI-driven technical roadmaps that chart methodological transitions;(3)the design and implementation of general analytical workflows for binary code analysis;(4)the applicability,strengths,and limitations of LLMs in capturing semantic and structural features of binary code;and(5)the persistent challenges and promising directions for future investigation.By synthesizing insights across these dimensions,the study demonstrates how LLMs reshape the landscape of binary code analysis,offering unprecedented opportunities to improve accuracy,scalability,and adaptability in real-world scenarios.This review not only bridges a critical gap in the existing literature but also provides a forward-looking perspective,serving as a valuable reference for researchers and practitioners aiming to advance AI-powered BCSD methodologies and applications.展开更多
When the G20 was created in 1999 in the wake of the Asian financial crisis,few imagined it would one day become the nerve centre of global governance.Twenty-six years later,the G20 members,which represent 85 percent o...When the G20 was created in 1999 in the wake of the Asian financial crisis,few imagined it would one day become the nerve centre of global governance.Twenty-six years later,the G20 members,which represent 85 percent of the global GDP and two-thirds of the world population,are once again navigating a turbulent era marked by geopolitical rivalry,economic fragmentation and widening inequality.展开更多
This article revisits the concept of epistemological rupture by questioning the stark division between scientific and non-scientific thought. Drawing on the theory of representation, it contends that both forms of kno...This article revisits the concept of epistemological rupture by questioning the stark division between scientific and non-scientific thought. Drawing on the theory of representation, it contends that both forms of knowledge are socially constructed, moulded by communication, norms and group dynamics. Rather than labelling non-scientific thought as flawed or regressive, the discussion shows how decontextualization and recontextualization processes apply equally to everyday ‘natural' knowledge and formal science,exposing the social and historical contingencies shaping concepts. Consequently, rupture appears less a sudden break than a gradual threshold reached through dialectical transformations in cognition and society. Rather than conferring total superiority on science, ruptures highlight how certain discourses gain legitimacy while others become ‘non-knowledge'. The article concludes that science's dominance reflects broader power relationships and evolving modes of production and validation. By situating epistemological rupture within these processes, it illuminates how different knowledge forms coexist, evolve and sometimes conflict in stratified social fields—ultimately challenging a simplistic binary between scientific progress and supposedly primitive or natural thought. This viewpoint opens new possibilities for examining the shifting boundaries between rational explanations and the shared beliefs shaping collective reality and daily life.展开更多
十二生肖在中国流传千年,那这些生肖是怎么选出来的呢?People in China have 12 zodiac animals.Each animal represents one year in the Chinese calendar.These animals are Rat,Ox,Tiger,Rabbit,Dragon,Snake,Horse,Goat,Monkey,Roo...十二生肖在中国流传千年,那这些生肖是怎么选出来的呢?People in China have 12 zodiac animals.Each animal represents one year in the Chinese calendar.These animals are Rat,Ox,Tiger,Rabbit,Dragon,Snake,Horse,Goat,Monkey,Rooster,Dog and Pig.展开更多
This paper considers the notions of common sense and interobjectivity to articulate an understanding of how different cultural realities give rise to different construals of scientific phenomena across distinct cultur...This paper considers the notions of common sense and interobjectivity to articulate an understanding of how different cultural realities give rise to different construals of scientific phenomena across distinct cultures. Our main focus in this paper is on the social sciences. We propose a quadrant of different cultural–scientific stances from which the study of social phenomena is possible, based on the emic–etic dimension pertaining to the study of culture from contrasting perspectives. Although the emic–etic distinction is normal y applied in fields within the science of culture, it is proposed here that the distinction is in some ways germane to scientific practice in general, making it amenable for use in a culture of science(CoS) programme. The four perspectives that emerge from the quadrant are illustrated using exemplars. Different aspects of CoS—that is, scientific practice, scientific conventions and representations of science—are then discussed in further detail, including in two tables illustrating points of convergence and divergence between the East and West when it comes to different aspects of CoS.展开更多
The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability cla...The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability classification.To address this challenge,we propose Vulnerability2Vec,a graph-embedding-based framework designed to enhance the automated classification of security vulnerabilities that threaten energy system resilience.Vulnerability2Vec converts Common Vulnerabilities and Exposures(CVE)text explanations to semantic graphs,where nodes represent CVE IDs and key terms(nouns,verbs,and adjectives),and edges capture co-occurrence relationships.Then,it embeds the semantic graphs to a low-dimensional vector space with random-walk sampling and skip-gram with negative sampling.It is possible to identify the latent relationships and structural patterns that traditional sparse vector methods fail to capture.Experimental results demonstrate a classification accuracy of up to 80%,significantly outperforming baseline methods.This approach offers a theoretical basis for classifying vulnerability types as structured semantic patterns in complex software systems.The proposed method models the semantic structure of vulnerabilities,providing a theoretical foundation for their classification.展开更多
The event-based vision sensor(EVS),which can generate efficient spiking data streams by exclusively detecting motion,exemplifies neuromorphic vision methodologies.Generally,its inherent lack of texture features limits...The event-based vision sensor(EVS),which can generate efficient spiking data streams by exclusively detecting motion,exemplifies neuromorphic vision methodologies.Generally,its inherent lack of texture features limits effectiveness in complex vision processing tasks,necessitating supplementary visual information.However,to date,no event-based hybrid vision solution has been developed that preserves the characteristics of complete spike data streams to support synchronous computation architectures based on spiking neural network(SNN).In this paper,we present a novel spike-based sensor with digitized pixels,which integrates the event detection structure with the pulse frequency modulation(PFM)circuit.This design enables the simultaneous output of spiking data that encodes both temporal changes and texture information.Fabricated in 180 nm process,the proposed sensor achieves a resolution of 128×128,a maximum event rate of 960 Meps,a grayscale frame rate of 117.1 kfps,and a measured power consumption of 60.1 mW,which is suited for high-speed,low-latency,edge SNNbased vision computing systems.展开更多
基金supported by the National Social Science Fund of China’s project‘Philosophical Research on the Challenge of Artificial Cognition to Natural Cognition’(grant number 21&ZD061)
文摘As a new research direction in contemporary cognitive science,predictive processing surpasses traditional computational representation and embodied cognition and has emerged as a new paradigm in cognitive science research.The predictive processing theory advocates that the brain is a hierarchical predictive model based on Bayesian inference,and its purpose is to minimize the difference between the predicted world and the actual world,so as to minimize the prediction error.Predictive processing is therefore essentially a context-dependent model representation,an adaptive representational system designed to achieve its cognitive goals through the minimization of prediction error.
基金the National Research Foundation(NRF)of Korea under the auspices of the Ministry of Science and ICT,Republic of Korea(Grant No.NRF-2020R1G1A1012741)received by M.R.Bhutta.https://nrf.kird.re.kr/main.do.
文摘Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness.DR occurs due to the high blood sugar level of the patient, and it is clumsy tobe detected at an early stage as no early symptoms appear at the initial level. To preventblindness, early detection and regular treatment are needed. Automated detectionbased on machine intelligence may assist the ophthalmologist in examining thepatients’ condition more accurately and efficiently. The purpose of this study is toproduce an automated screening system for recognition and grading of diabetic retinopathyusing machine learning through deep transfer and representational learning.The artificial intelligence technique used is transfer learning on the deep neural network,Inception-v4. Two configuration variants of transfer learning are applied onInception-v4: Fine-tune mode and fixed feature extractor mode. Both configurationmodes have achieved decent accuracy values, but the fine-tuning method outperformsthe fixed feature extractor configuration mode. Fine-tune configuration modehas gained 96.6% accuracy in early detection of DR and 97.7% accuracy in gradingthe disease and has outperformed the state of the art methods in the relevant literature.
文摘Objoctive To identify differential genes between normal ovarian epithelium tissue and ovarian epithelial cancer using representational difference analysis of cDNA （cDNA-RDA）. Methods cDNA-RDA was performed to identify the differentially expressed sequences between cDNAs from cancer tissue and cDNAs from normal ovarian tissue in the same patient who was in the early stage of ovarian serous cystadenocarcinoma. These differentially expressed fragments were cloned and analyzed, then sequenced and compared with known genes. Results Three differentially cxpressed cDNA fragments were isolated using cDNA from normal ovarian tissue as tester and cDNA from cancer tissue as driver amplicon by cDNA-RDA. DP Ⅲ- 1 and DP Ⅲ-2 cDNA clone showed significant homology to the cDNA of alpha actin gene; DPⅢ-3 cDNA clone showed significant homology to the cDNA oftransgelin gene. Conclusion cDNA-RDA can bc used to sensitively identify the differentially expressed genes in ovarian serous cystadenocarcinoma. Ovarian serous cystadenocarcinoma involves alteration of multiple genes.
文摘Objective To screen coronaryartery disease (CAD) specific expressions and clone their genes. Method Blood samples were collected from CAD and non - CAD patients at the end of coronary angiography. mRNA from samples was isolated and converted into cDNA. After ligated with specific linkers, the cDNA was amplified with complementary primers. PCR products from CAD samples were named as tester; the ones from non - CAD samples were named as driver. With different ratio of tester to driver (1 : 100,1: 1, 000, and 1: 10, 000), they were mixed, denatured, and renatured. Single strand cD-NA was eliminated by Mung bean nuclease. Double strand cDNA presented only in tester was amplified, ligated in vector pUC19 and pUC53, and transformed into E. coll DH5a. Strains with inserted cDNA fragments were picked up based on blue and white selection. Insertions were screened by endonuclease digestion and DNA sequencing. Results were compared with DNA sequences of GeneBank. Results: After the selection with representational differential analysis, CAD specific cDNA fragments with different sizes (about 1kb, 0. 75kb, and 0. 6kb) were cloned. Among them, two fragments from unknown genes were identified. One presented a 43. 3 % similarity with part of the rattus norvegicus lipocortin gene. Another presented a 45. 4 % similarity with part of the human polynucleotide kinase 3' - phosphatase gene. Conclusion There are at least two CAD specific - ex- pressions from unknown genes that were partially similar to lipocortin and polynucleotide kinase 3'- phos-phatase genes, respectively. Expression of these genes might affect the formation and progression of plaque within coronary artery.
基金supported by the STI2030-Major Projects(2021ZD0200201,2022ZD0211500)the National Natural Science Foundation of China(62201519,52307259,62327805,and 82151307).
文摘Understanding the neural substrates of human cognition is a central goal of neuroscience research.Modern imaging techniques,such as functional magnetic resonance imaging(fMRI),provide an opportunity to map cognitive function in vivo.To date,modeling shared information in task-evoked neural dynamics across individuals remains challenging,largely due to pronounced inter-subject variability in brain anatomy,function,and behaviors[1],[2].An emerging topic,known as hyperalignment or functional alignment,has been proposed recently[3],to map subject-specific neural responses onto a common representational space using either linear transformations of task-evoked neural activity[4]or resting-state connectivity profiles[5].However,these approaches often assume uniform neural responses across individuals,struggling to capture group heterogeneity and model functional interactions between brain areas[6].
文摘This paper presents an empirical study of the acquisition of English ambiguous verb-locative prepositional phrase constructions （VLPPs） by adult Mandarin and Spanish speakers. This study assumes that the semantic properties of the target VLPPs that relate to change-of-location in sentences such as The boat floated under the bridge arise from an uninterpretable syntactic feature selected by English but unselected by Mandarin Chinese and Spanish. Results obtained from an animated cartoon selection task indicate that neither the Mandarin nor the Spanish speakers at any level of English proficiency possess native-like interpretative knowledge. Tense/ Aspect effects on the interpretation of the target constructions by Spanish speakers were also found. These results are interpreted as consistent with the Representational Deficit Hypothesis view （Hawkins, 2003, 2005） of adult second language acquisition.
文摘Objective To search differentially expressed sequences correlated with pathogenesis of human nasopharyngeal carcinoma (NPC), including the candidates of tumor suppressor genes Methods Representational difference analysis (RDA) was performed to isolate differentially expressed sequences between cDNA from normal human primary cultures of nasopharyngeal epithelial cells and cDNA from NPC cell line HNE1 The source of differentially expressed products were proved by Southern blot, Northern blot and in situ hybridization The fragments were cloned with pGEM T easy kit and sequenced by the chain termination reaction Results Four differentially expressed cDNA fragments were isolated in the fourth subtractive hybridization using cDNA from normal human nasopharyngeal epithelial cells as tester amplicon and cDNA from NPC cell line HNE1 as driver amplicon by cDNA RDA These differential cDNA fragments revealed that they really came from the tester amplicon and were not expressed or down regulated in the NPC HNE1 cells Some of the genes were expressed only in human nasopharyngeal epithelial cells but deleted or down regulated in the biopsies of NPC Of these obtained clones, some were the sequences of the human known genes including house keeping genes, the others represented novel gene sequences Conclusion The differentially expressed products including the candidates of tumor suppressor genes may be associated with the initiation of the NPC
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
基金funded by grants from the National Key Research and Development Program of China(Grant Nos.:2022YFE0205600 and 2022YFC3400504)the National Natural Science Foundation of China(Grant Nos.:82373792 and 82273857)the Fundamental Research Funds for the Central Universities,China,and the East China Normal University Medicine and Health Joint Fund,China(Grant No.:2022JKXYD07001).
文摘Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Classifier(GPT2-ICC),which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins.GPT2-ICC integrates representation learning with a large language model(LLM)-based classifier,enabling highly accurate identification of potential ion channels.Several potential ion channels were predicated from the unannotated human proteome,further demonstrating GPT2-ICC’s generalization ability.This study marks a significant advancement in artificial-intelligence-driven ion channel research,highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data.Moreover,it provides a valuable computational tool for uncovering previously uncharacterized ion channels.
基金Shanghai Frontier Science Research Center for Modern Textiles,Donghua University,ChinaOpen Project of Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment,Zhengzhou University of Light Industry,China(No.IM202303)National Key Research and Development Program of China(No.2019YFB1706300)。
文摘A personalized outfit recommendation has emerged as a hot research topic in the fashion domain.However,existing recommendations do not fully exploit user style preferences.Typically,users prefer particular styles such as casual and athletic styles,and consider attributes like color and texture when selecting outfits.To achieve personalized outfit recommendations in line with user style preferences,this paper proposes a personal style guided outfit recommendation with multi-modal fashion compatibility modeling,termed as PSGNet.Firstly,a style classifier is designed to categorize fashion images of various clothing types and attributes into distinct style categories.Secondly,a personal style prediction module extracts user style preferences by analyzing historical data.Then,to address the limitations of single-modal representations and enhance fashion compatibility,both fashion images and text data are leveraged to extract multi-modal features.Finally,PSGNet integrates these components through Bayesian personalized ranking(BPR)to unify the personal style and fashion compatibility,where the former is used as personal style features and guides the output of the personalized outfit recommendation tailored to the target user.Extensive experiments on large-scale datasets demonstrate that the proposed model is efficient on the personalized outfit recommendation.
基金supported by the Shenzhen Key Laboratory of Intelligent Bioinformatics(No.ZDSYS20220422103800001)the Shenzhen Science and Technology Program(No.JCYJ20230807140709020)+2 种基金National Natural Science Foundation of China(Nos.62402489,U22A2041,and 62373172)the China Postdoctoral Science Foundation(No.2023M743688)Guangdong Basic and Applied Basic Research Foundation(Nos.2024A1515011960 and 2023A1515110570)。
文摘Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable,thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making.This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations.The research methodology begins with the compilation of small molecule databases,followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms,capturing patterns and salient features across extensive chemical spaces.The study then examines various drug discovery downstream tasks,including drug-target interaction(DTI)prediction,drug-target affinity(DTA)prediction,drug property(DP)prediction,and drug generation,all based on learned representations.The analysis concludes by highlighting challenges and opportunities associated with machine learning(ML)methods for molecular representation and improving downstream task performance.Additionally,the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine(TCM)medicinal substances and facilitating TCM target discovery.
文摘Let F_(1)be the virtual field consisting of one element and(Q,I)a string pair.In this paper,we study the representations of string pairs over the virtual field F_(1).It is proved that an indecomposable F_(1)-representation is either a string representation or a band representation by using the coefficient quivers.It is worth noting that for a given band and a positive integer,there exists a unique band representation up to isomorphism.
基金National Natural Science Foundation of China(12161013)Research Projects of Guizhou University of Commerce in 2024。
文摘The modifiedλ-differential Lie-Yamaguti algebras are considered,in which a modifiedλ-differential Lie-Yamaguti algebra consisting of a Lie-Yamaguti algebra and a modifiedλ-differential operator.First we introduce the representation of modifiedλ-differential Lie-Yamaguti algebras.Furthermore,we establish the cohomology of a modifiedλ-differential Lie-Yamaguti algebra with coefficients in a representation.Finally,we investigate the one-parameter formal deformations and Abelian extensions of modifiedλ-differential Lie-Yamaguti algebras using the second cohomology group.
文摘Binary Code Similarity Detection(BCSD)is vital for vulnerability discovery,malware detection,and software security,especially when source code is unavailable.Yet,it faces challenges from semantic loss,recompilation variations,and obfuscation.Recent advances in artificial intelligence—particularly natural language processing(NLP),graph representation learning(GRL),and large language models(LLMs)—have markedly improved accuracy,enabling better recognition of code variants and deeper semantic understanding.This paper presents a comprehensive review of 82 studies published between 1975 and 2025,systematically tracing the historical evolution of BCSD and analyzing the progressive incorporation of artificial intelligence(AI)techniques.Particular emphasis is placed on the role of LLMs,which have recently emerged as transformative tools in advancing semantic representation and enhancing detection performance.The review is organized around five central research questions:(1)the chronological development and milestones of BCSD;(2)the construction of AI-driven technical roadmaps that chart methodological transitions;(3)the design and implementation of general analytical workflows for binary code analysis;(4)the applicability,strengths,and limitations of LLMs in capturing semantic and structural features of binary code;and(5)the persistent challenges and promising directions for future investigation.By synthesizing insights across these dimensions,the study demonstrates how LLMs reshape the landscape of binary code analysis,offering unprecedented opportunities to improve accuracy,scalability,and adaptability in real-world scenarios.This review not only bridges a critical gap in the existing literature but also provides a forward-looking perspective,serving as a valuable reference for researchers and practitioners aiming to advance AI-powered BCSD methodologies and applications.
文摘When the G20 was created in 1999 in the wake of the Asian financial crisis,few imagined it would one day become the nerve centre of global governance.Twenty-six years later,the G20 members,which represent 85 percent of the global GDP and two-thirds of the world population,are once again navigating a turbulent era marked by geopolitical rivalry,economic fragmentation and widening inequality.
文摘This article revisits the concept of epistemological rupture by questioning the stark division between scientific and non-scientific thought. Drawing on the theory of representation, it contends that both forms of knowledge are socially constructed, moulded by communication, norms and group dynamics. Rather than labelling non-scientific thought as flawed or regressive, the discussion shows how decontextualization and recontextualization processes apply equally to everyday ‘natural' knowledge and formal science,exposing the social and historical contingencies shaping concepts. Consequently, rupture appears less a sudden break than a gradual threshold reached through dialectical transformations in cognition and society. Rather than conferring total superiority on science, ruptures highlight how certain discourses gain legitimacy while others become ‘non-knowledge'. The article concludes that science's dominance reflects broader power relationships and evolving modes of production and validation. By situating epistemological rupture within these processes, it illuminates how different knowledge forms coexist, evolve and sometimes conflict in stratified social fields—ultimately challenging a simplistic binary between scientific progress and supposedly primitive or natural thought. This viewpoint opens new possibilities for examining the shifting boundaries between rational explanations and the shared beliefs shaping collective reality and daily life.
文摘十二生肖在中国流传千年,那这些生肖是怎么选出来的呢?People in China have 12 zodiac animals.Each animal represents one year in the Chinese calendar.These animals are Rat,Ox,Tiger,Rabbit,Dragon,Snake,Horse,Goat,Monkey,Rooster,Dog and Pig.
文摘This paper considers the notions of common sense and interobjectivity to articulate an understanding of how different cultural realities give rise to different construals of scientific phenomena across distinct cultures. Our main focus in this paper is on the social sciences. We propose a quadrant of different cultural–scientific stances from which the study of social phenomena is possible, based on the emic–etic dimension pertaining to the study of culture from contrasting perspectives. Although the emic–etic distinction is normal y applied in fields within the science of culture, it is proposed here that the distinction is in some ways germane to scientific practice in general, making it amenable for use in a culture of science(CoS) programme. The four perspectives that emerge from the quadrant are illustrated using exemplars. Different aspects of CoS—that is, scientific practice, scientific conventions and representations of science—are then discussed in further detail, including in two tables illustrating points of convergence and divergence between the East and West when it comes to different aspects of CoS.
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the Convergence Security Core Talent Training Business Support Program(IITP-2025-RS-2023-00266605,50%)in part by the Institute of Information&Communications Technology Planning&Evaluation(lITP)grant funded by the Korea government(MSIT)(RS-2025-02305436,Development of Digital Innovative Element Technologies for Rapid Prediction of Potential Complex Disasters and Continuous Disaster Prevention,30%)supported by the Chung-Ang University Graduate Research Scholar-ship in 2023(20%).
文摘The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability classification.To address this challenge,we propose Vulnerability2Vec,a graph-embedding-based framework designed to enhance the automated classification of security vulnerabilities that threaten energy system resilience.Vulnerability2Vec converts Common Vulnerabilities and Exposures(CVE)text explanations to semantic graphs,where nodes represent CVE IDs and key terms(nouns,verbs,and adjectives),and edges capture co-occurrence relationships.Then,it embeds the semantic graphs to a low-dimensional vector space with random-walk sampling and skip-gram with negative sampling.It is possible to identify the latent relationships and structural patterns that traditional sparse vector methods fail to capture.Experimental results demonstrate a classification accuracy of up to 80%,significantly outperforming baseline methods.This approach offers a theoretical basis for classifying vulnerability types as structured semantic patterns in complex software systems.The proposed method models the semantic structure of vulnerabilities,providing a theoretical foundation for their classification.
基金supported in part by the National Key Research and Development Program of China(Grant No.2022YFB2804401)the National Natural Science Foundation of China(Grant Nos.62334008,62134004,62404218)+1 种基金the Beijing Natural Science Foundation(Grant No.Z220005)Chinese Academy of Sciences(Grant No.ZDBS-LY-JSC008).
文摘The event-based vision sensor(EVS),which can generate efficient spiking data streams by exclusively detecting motion,exemplifies neuromorphic vision methodologies.Generally,its inherent lack of texture features limits effectiveness in complex vision processing tasks,necessitating supplementary visual information.However,to date,no event-based hybrid vision solution has been developed that preserves the characteristics of complete spike data streams to support synchronous computation architectures based on spiking neural network(SNN).In this paper,we present a novel spike-based sensor with digitized pixels,which integrates the event detection structure with the pulse frequency modulation(PFM)circuit.This design enables the simultaneous output of spiking data that encodes both temporal changes and texture information.Fabricated in 180 nm process,the proposed sensor achieves a resolution of 128×128,a maximum event rate of 960 Meps,a grayscale frame rate of 117.1 kfps,and a measured power consumption of 60.1 mW,which is suited for high-speed,low-latency,edge SNNbased vision computing systems.
