Experience-dependent representation of visual categories in parietal cortex

Categorization is a process by which the brain assigns meaning to sensory stimuli. Through experience, we learn to group stimuli into categories, such as 'chair', 'table' and 'vehicle', which are critical for rapidly and appropriately selecting behavioural responses. Although much is known about the neural representation of simple visual stimulus features (for example, orientation, direction and colour), relatively little is known about how the brain learns and encodes the meaning of stimuli. We trained monkeys to classify 360 degrees of visual motion directions into two discrete categories, and compared neuronal activity in the lateral intraparietal (LIP) and middle temporal (MT) areas, two interconnected brain regions known to be involved in visual motion processing. Here we show that neurons in LIP--an area known to be centrally involved in visuo-spatial attention, motor planning and decision-making-robustly reflect the category of motion direction as a result of learning. The activity of LIP neurons encoded directions of motion according to their category membership, and that encoding shifted after the monkeys were retrained to group the same stimuli into two new categories. In contrast, neurons in area MT were strongly direction selective but carried little, if any, explicit category information. This indicates that LIP might be an important nexus for the transformation of visual direction selectivity to more abstract representations that encode the behavioural relevance, or meaning, of stimuli.     

Semantic-Oriented Knowledge Transfer for Review Rating

With the rapid development of Web 2.0, more and more people are sharing their opinions about online products, so there is much product review data. However, it is difficult to compare products directly using ratings because many ratings are based on different scales or ratings are even missing. This paper addresses the following question: given textual reviews, how can we automatically determine the semantic orientations of reviewers and then rank different items? Due to the absence of ratings in many reviews, it is difficult to collect sufficient rating data for certain specific categories of products (e.g., movies), but it is easier to find rating data in another different but related category (e.g., books). We refer to this problem as transfer rating, and try to train a better ranking model for items in the interested category with the help of rating data from another related category. Specifically, we developed a ranking-oriented method called TRate for determining the semantic orientations and for ranking different items and formulated it in a regularized algorithm for rating knowledge transfer by bridging the two related categories via a shared latent semantic space. Tests on the Epinion dataset verified its effectiveness.     

对实用类体操走进高校的可行性研究

从理论的角度分析了实用类体操教学进入高校的可行性和重要性.研究表明:实用类体操教学进入高校是可行的.首先,具有良好的国内环境;其次,实用类体操具有较高的安全性;第三,高校各专项体育教师完全可以胜任实用类体操的教学;第四,实用类体操对场地、器材的要求简单;最后,实用类体操趣味性较强.实用类体操的重要性主要体现在改变传统体育观念、提高学生的生存技能以及学生的思想道德素质.     

社会权——社会法的基石范畴

社会权是社会法的基石范畴。一个学科的范畴通常可以分为普通范畴、基本范畴和基石范畴三个不同的层次,其中,基石范畴是把一个法的部门从法体系中相对地独立出来的核心范畴。＂私法公法化＂、＂社会问题＂、＂社会整体利益＂及＂社会安全＂等范畴都难以成为社会法的基石范畴。问题群中属于本源性的问题才能成为基本问题,功能性范畴、目的性范畴只是解决本源性范畴所导致的结果和目的,而本身不是本源性范畴或基石范畴。社会权和社会法的＂历史起点＂具有同步性,社会权的产生和发展催生和推动了社会法的产生和发展。社会权作为社会法的基石范畴,既能制约、控制和引导社会法的发展目标,也能很好体现权利本位的法的价值和理念;既有利于构建一个有内在逻辑的社会法体系,也体现了以人为本的科学发展观。     

推荐系统研究综述

随着互联网技术的快速发展,如何对海量网络信息进行挖掘分析,已成为热点和难点问题。推荐系统能够帮助用户在没有明确需求或者信息量巨大时解决信息过载的问题,为用户提供精准、快速的业务(如商品、项目、服务等)信息,成为近年来产业界和学术界共同的兴趣点和研究热点,但是,目前数据的种类多种多样并且应用场景广泛,在面对这种情况时,推荐系统也会遇到冷启动、稀疏矩阵等挑战。深度学习是机器学习的一个重要研究领域和分支,近年来发展迅猛。研究人员使用深度学习方法,在语音识别、图像处理、自然语言处理等领域都取得了很大的突破与成就。目前,深度学习在推荐领域也得到了许多研究人员的青睐,成为推荐领域的一个新方向。推荐方法中融合深度学习技术,可以有效解决传统推荐系统中冷启动、稀疏矩阵等问题,提高推荐系统的性能和推荐精度。文中主要对传统的推荐方法和当前深度学习技术中神经网络在推荐方法上的应用进行了归纳,其中传统推荐方法主要分为以下3类:1)基于内容推荐方法主要依据用户与项目之间的特征信息,用户之间的联系不会影响推荐结果,所以不存在冷启动和稀疏矩阵的问题,但是基于内容推荐的结果新颖程度低并且面临特征提取的问题。2)协同过滤推荐方法是目前应用最为广泛的一种方法,不需要有关用户或项目的信息,只基于用户和诸如点击、浏览和评级等项目的交互信息做出准确的推荐。虽然该方法简单有效但是会出现稀疏矩阵和冷启动的问题。3)混合推荐方法融合了前2种传统推荐方法的特点,能取得很好的推荐效果,但在处理文本、图像等多源异构辅助信息时仍面临一些挑战与困难。依据神经网络基于深度学习的推荐方法主要分为4类:基于深度神经网络(DNN)的推荐方法、基于卷积神经网络(CNN)的推荐方法、基于循环神经网络(RNN)和长短期记忆神经网络(LSTM)的推荐方法、基于图神经网络(GNN)的推荐方法、将深度学习技术融入到推荐领域,构造的模型具有以下优势:具有较强的表征能力,可以直接从内容中提取用户和项目特征;具有较强的抗噪能力,可以轻易地处理含有噪声的数据;可以对动态或者序列数据进行建模;可以更加精准地学习用户或项目特征;便于对数据进行统一处理,并且可以处理大规模数据。将深度学习技术应用到推荐领域,可以积极有效地应对传统推荐方法面临的挑战,提高推荐效果。     

现代学习理论在计算机辅助教学中的应用研究

现代学习理论认为学习要以学生为中心，要求学生由外部刺激的被动接受者转变为信息加工的主体和知识结构的主动建构者，同时要求教师从知识的传授者、灌输者转变为学生主动建构知识结构的帮助者、促进者．因此，计算机辅助教学的设计应体现学生的中心地位，应强调“情境”对知识结构建构的重要作用，强调“协作学习”，强调知识的应用与发展．     

中国传统文化对语言学习风格形成的影响

学习者的学习风格是二外习得中个体差异的一个内在的固定因素,其形成受多种外部及内部因素的影响,而学习者最基本的性格特点构成了学习风格的核心。论述了学习风格的主要分类及四种语言学习者,并探讨了中国传统文化对学习者学习风格形成的影响。     

Identifying natural images from human brain activity

A challenging goal in neuroscience is to be able to read out, or decode, mental content from brain activity. Recent functional magnetic resonance imaging (fMRI) studies have decoded orientation, position and object category from activity in visual cortex. However, these studies typically used relatively simple stimuli (for example, gratings) or images drawn from fixed categories (for example, faces, houses), and decoding was based on previous measurements of brain activity evoked by those same stimuli or categories. To overcome these limitations, here we develop a decoding method based on quantitative receptive-field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas. These models describe the tuning of individual voxels for space, orientation and spatial frequency, and are estimated directly from responses evoked by natural images. We show that these receptive-field models make it possible to identify, from a large set of completely novel natural images, which specific image was seen by an observer. Identification is not a mere consequence of the retinotopic organization of visual areas; simpler receptive-field models that describe only spatial tuning yield much poorer identification performance. Our results suggest that it may soon be possible to reconstruct a picture of a person's visual experience from measurements of brain activity alone.     

商务英语词义的假性透明

引介＂词义透明＂这一概念,反证商务语境中英语词义存在的＂假性透明＂语言现象,并将＂假性透明＂术语运用于论述一般用途英语词汇对商务英语词汇习得的负迁移。以财富期刊网2 000多个商务英语语词为语料,依据商务英语的特点和学生常犯的错误,将商务英语中容易造成词义＂假性透明＂的语词分为假朋友、多义聚合和词化表达3类。认为＂假朋友＂是两种语言或同一语言不同变体中,形式相同,但意义完全不同的词或词组;指出词化表达凸显了商务英语简洁高效的语言特点,但其整体义不是单个词义的累加,应作为商务英语词汇习得显性教学的重点。     

可再生资源型企业的绿色特征与绿色战略类型体系研究——以林业企业为例

研究可再生资源型企业独特的绿色特征,据此构建其完整的绿色战略类型体系,为企业绿色发展战略的制定提供多种备选方案。首先,运用系统工程理论中关于系统目标、结构、功能和环境的分析方法,以林业企业为例,研究可再生资源型企业的绿色特征;然后,根据战略管理理论和绿色经济理论,将绿色特征归并为3类,分别形成3个研究视角——绿色链条视角(闭环性视角)、绿色拓展视角和绿色产业视角,再从这3个视角探讨可再生资源型企业绿色战略的类型及其相互关联。从功能目标、自然关联、绿色范围、资源供应、供应链5个特征类,得出林业企业与一般工业企业的绿色特征在16个特征项的对比关系。基于绿色特征,构建了可再生资源型企业绿色战略的类型体系。可再生资源型企业具有显著的绿色特征和多样化的绿色战略类型,应充分发挥其绿色战略优势。超循环绿色战略是绿色战略的成熟阶段,未来的应用前景十分广阔,企业在战略实施中应注重其中资源链、生态链和价值链的相互促进。本研究成果对一般企业的绿色战略管理也有普遍意义。     

实体化思维与知性范畴？--对康德先验范畴体系的一些修正

康德把“时间”和“空间”当作感性直观，成为知性范畴的基础。笔者则把“时间”和“空间”从感性直观提升为知性范畴，并对康德范畴的其他内容进行调整或补充，进而对其整个范畴体系进行一定的修改或完善。在新的范畴体系中，除了引入被康德忽略的三组范畴即时空范畴、自然数范畴和界限范畴，还对原有的关系范畴作出重大改动，即除“因果性”之外新增两个范畴即“随机性”和“统计性”，这对解决“休谟问题”是至关重要的。     

Cortical remodelling induced by activity of ventral tegmental dopamine neurons.

Representations of sensory stimuli in the cerebral cortex can undergo progressive remodelling according to the behavioural importance of the stimuli. The cortex receives widespread projections from dopamine neurons in the ventral tegmental area (VTA), which are activated by new stimuli or unpredicted rewards, and are believed to provide a reinforcement signal for such learning-related cortical reorganization. In the primary auditory cortex (AI) dopamine release has been observed during auditory learning that remodels the sound-frequency representations. Furthermore, dopamine modulates long-term potentiation, a putative cellular mechanism underlying plasticity. Here we show that stimulating the VTA together with an auditory stimulus of a particular tone increases the cortical area and selectivity of the neural responses to that sound stimulus in AI. Conversely, the AI representations of nearby sound frequencies are selectively decreased. Strong, sharply tuned responses to the paired tones also emerge in a second cortical area, whereas the same stimuli evoke only poor or non-selective responses in this second cortical field in naive animals. In addition, we found that strong long-range coherence of neuronal discharge emerges between AI and this secondary auditory cortical area.     

分组训练卷积字典的图像去噪算法

卷积稀疏编码(convolutional sparse coding,CSC)这一全局模型因字典的特殊结构而受到广泛关注,其中卷积字典学习算法(slice-based dictionary learning,S-BCSC)是最为有效的CSC模型优化算法.虽然S-BCSC算法非常有效,但算法在应用中对整幅图像只使用一个固定大小的字典,然而这并不利于图像信息的准确描述.为克服这一缺陷,讨论如何根据图像大小确定卷积字典大小,结合稀疏表示字典学习算法,提出分组训练卷积字典的图像去噪算法.新算法首先将过冗余图像块按照平滑、纹理、边缘分为三类;然后为每一类分别确定所要训练的卷积字典大小;最后依据S-BCSC算法完成字典学习以及图像去噪过程.从实验结果可以看出,所提算法在图像质量、清晰度上相比原S-BCSC算法都有所提升.     

基于项目属性分类的协同过滤算法研究

用户对项目的评分数据是传统协同过滤算法进行项目或用户推荐的唯一依据,项目或用户本身的属性特征并未进行过多考虑。为此,在计算项目之间的相似度时融合了项目标签属性,提高了项目推荐的准确率。具体方法是首先通过创建项目属性分类表,得到项目属性之间的差异度,然后将项目属性差异度融入pearson 相关系数公式中,计算项目之间的相似度。通过实验验证,改进后的方法比传统的基于项目的协同过滤算法的推荐结果平均偏差小,命中率高,推荐结果更加准确。     

Selective deficit of visual search in moving displays after extrastriate damage

A visual cue that is often associated with significant stimuli, such as those provided by prey and predators, is movement relative to the observer. An efficient visual system should be able to direct attention to those parts of the visual field that contain such stimuli. What is needed is a system that can filter by movement difference. This could direct attention to a moving item among stationary items, or an item moving in one direction against a background moving in a different direction. Visual search experiments have shown that people are indeed able to filter by movement; that is, they can attend to just the moving items in arrays of moving and stationary stimuli. Single-cell recordings from monkey visual cortex show that the medial temporal cortical area (MT) has some of the properties required to filter by movement. We have now linked these two observations by showing that a patient with bilateral lesions to the presumed human homologue of MT cannot restrict visual attention to the moving items in arrays of both moving and stationary items. This suggests that MT is the site of a movement filter used in normal visual processing.     

维吾尔语的几种特殊空位

维吾尔语中有几种与名词的形态变化有关的特殊空位，即语言的经济性原则引起的语义空位、中心名词移位引起的句法空位和靠上下文解释的语用空位等，它们与名词构形成分的不规则重叠、其他词类带名词的构形成分等现象有着潜在的关系。     

Symmetry perception in an insect

Symmetrical visual patterns have a salient status in human perception, as evinced by their prevalent occurrence in art, and also in animal perception, where they may be an indicator of phenotypic and genotypic quality. Symmetry perception has been demonstrated in humans, birds, dolphins and apes. Here we show that bees trained to discriminate bilaterally symmetrical from non-symmetrical patterns learn the task and transfer it appropriately to novel stimuli, thus demonstrating a capacity to detect and generalize symmetry or asymmetry. We conclude that bees, and possibly flower-visiting insects in general, can acquire a generalized preference towards symmetrical or, alternatively, asymmetrical patterns depending on experience, and that symmetry detection is preformed or can be learned as perceptual category by insects, because it can be extracted as an independent visual pattern feature. Bees show a predisposition for learning and generalized symmetry because, if trained to it, they choose it more frequently, come closer to and hover longer in front of the novel symmetrical stimuli than the bees trained for asymmetry do for the novel asymmetrical stimuli. Thus, even organisms with comparatively small nervous systems can generalize about symmetry, and favour symmetrical over asymmetrical patterns.     

语言知识技能化理论框架下的补缺假设

Anderson的语言知识技能化理论把知识划分成陈述性知识和程序性知识,语言习得过程是陈述性知识向程序性知识转化的过程,这个过程涉及到语境问题。"补缺假设"理论在语言知识技能化理论框架下具有现实意义,陈述性知识向程序性知识转化过程实际上是语言与语境适配过程,这个过程对外语教学的启发很大。     

基于汉字字频向量的中文文本自动分类系统

提出了一种根据汉字统计特性和基于实例映射的中文文本自动分类方法。该方法采用汉字字频向量作为文本的表示方法。它的显著特点是引入线性最小二乘方估计（ＬｉｎｅａｒＬｅａｓｔＳｑｕａｒｅＦｉｌ，ＬＬＳＦ）技术建立文本分类器模型，通过对训练集语料的手工分类标引以及对文本和类别间的相关性判定的学习，实现了基于全局最小错误率的汉字———类别两个向量空间的映射函数，并用该函数对测试文本进行分类     

从语法化角度看语言共性

从语法化角度探讨语言共性,发现发生在不同类型语言、不同语法范畴、相同语法范畴内不同语法单位中的历时演变过程具有普遍性,而真正促成这一普遍现象的则是历时演变机制.因此应进一步扩大历时因素的作用,将语言的历时演变机制作为更加基本和稳固的共性形式纳入语言共性研究理论.