A Small File Merging and Prefetching Strategy Basedon Access Task in Cloud Storage

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AICE人工智能等级考试三级考前冲刺

AICE人工智能等级考试三级考前冲刺1.以下场景中，使用了自然语言处理技术的是?（） [单选题] *A:智能家居控制系统识别语音指令(正确答案)B:机器人在工厂中搬运货物C:无人机根据预设轨迹完成航拍任务D:超市购物车上的自动计费系统答案解析：自然语言处理技术是一种机器学习技术，使计算机能够解读、处理和理解人类语言。

它会将人类语言转化为计算机可理解的形式，并对其进行计算处理。

A 选项中智能家居系统，会识别语音指令并将语音信号转化为文本，再对文本进行处理以完成指令功能，其中使用的就是自然语言处理技术。

所以正确答案选A。

2.1TB 等于?（） [单选题] *A:1024BB:1024KBC:1024MD:1024GB(正确答案)答案解析：计算机存储数据单位的大小顺序是:bit<Byte(B)<KB<MB<GB<TB，除1B=8bit 外，其他单位的换算关系是:(2 的 10 次方)1TB=1024GB1GB=1024MB1MB=1024KB1KB=1024Byte所以选项 D 正确3.运行下方程序打印的结果是?（）animal='8l8phant'foriinanimal:ifi=='8':animal=animal.replace(i,'e')breakprint(animal) [单选题] *A:8l8phantB:iliphantC:elephant(正确答案)D:Elephant答案解析：这段程序的功能是将字符串中的某个字符替换为另一个字符，最终输出替换后的字符串。

程序使用 for 循环遍历字符串'8l8phant'中的每个字符，当遍历到字符'8'时，使用replace()方法将该字符串中所有字符'8'替换为字符'e',并结束程序。

最后，输出替换后的字符串为"elephant"。

2022年陕西理工大学计算机科学与技术专业《数据结构与算法》科目期末试卷A(有答案)

2022年陕西理工大学计算机科学与技术专业《数据结构与算法》科目期末试卷A（有答案）一、选择题1、从未排序序列中依次取出一个元素与已排序序列中的元素依次进行比较，然后将其放在已排序序列的合适位置，该排序方法称为（）排序法。

A.插入B.选择C.希尔D.二路归并2、设有一个10阶的对称矩阵A，采用压缩存储方式，以行序为主存储， a11为第一元素，其存储地址为1，每个元素占一个地址空间，则a85的地址为（）。

A.13B.33C.18D.403、线性表的顺序存储结构是一种（）。

A.随机存取的存储结构B.顺序存取的存储结构C.索引存取的存储结构D.Hash存取的存储结构4、最大容量为n的循环队列，队尾指针是rear，队头：front，则队空的条件是（）。

A.（rear+1）MOD n=frontB.rear=frontC.rear+1=frontD.（rear-1）MOD n=front5、在下列表述中，正确的是（）A.含有一个或多个空格字符的串称为空格串B.对n（n>0）个顶点的网，求出权最小的n-1条边便可构成其最小生成树C.选择排序算法是不稳定的D.平衡二叉树的左右子树的结点数之差的绝对值不超过l6、已知字符串S为“abaabaabacacaabaabcc”，模式串t为“abaabc”，采用KMP算法进行匹配，第一次出现“失配”（s!＝t）时，i＝j＝5，则下次开始匹配时，i和j的值分别（）。

A．i＝1，j＝0 B．i＝5，j＝0 C．i＝5，j＝2 D．i＝6，j＝27、下列叙述中，不符合m阶B树定义要求的是（）。

A．根结点最多有m棵子树 B．所有叶结点都在同一层上C．各结点内关键字均升序或降序排列 D．叶结点之间通过指针链接8、设X是树T中的一个非根结点，B是T所对应的二叉树。

在B中，X是其双亲的右孩子，下列结论正确的是（）。

A.在树T中，X是其双亲的第一个孩子B.在树T中，X一定无右兄弟C.在树T中，X一定是叶结点D.在树T中，X一定有左兄弟9、有关二叉树下列说法正确的是（）。

nmfe算法 rna折叠最小自由能 -回复

nmfe算法rna折叠最小自由能-回复【NMFE算法RNA折叠最小自由能】RNA（核糖核酸）是一类生物分子，其序列中的碱基决定了其结构和功能。

折叠是指RNA分子通过碱基之间的互补配对形成特定的结构，进而发挥特定的功能。

RNA分子的折叠状态对于生物体的正常生理过程起着关键作用。

RNA折叠的问题可以形式化为在给定RNA序列的条件下，如何找到能量最小的折叠构象。

这是一个计算复杂度很高的问题，需要应用数学和计算机科学的技术。

近年来，科学家们提出了多种算法用于求解RNA折叠最小自由能的问题。

其中一种重要的算法是NMFE（Nucleotide Mutational Folding Evaluation）算法。

以下是NMFE算法的一步一步介绍：第一步：选择RNA序列作为输入，我们需要选择一个特定的RNA序列作为算法的初始输入。

RNA 序列是由四种不同的核苷酸（腺嘌呤A、尿嘧啶U、鸟嘌呤G和胞嘧啶C）组成的。

第二步：建立目标函数在NMFE算法中，我们需要定义一个目标函数来刻画给定的折叠结构的自由能。

自由能是描述了系统处于某个状态的稳定程度的物理量，其数值越小，表示该状态越稳定。

第三步：构建动态规划模型NMFE算法通过动态规划的方式来求解RNA折叠最小自由能问题。

动态规划是一种将复杂问题分解为相对简单的子问题并重复利用子问题解的方法。

在NMFE算法中，我们采用了自底向上的策略来构建动态规划模型。

具体地，我们从RNA序列的末端开始，逐渐向前计算，直到获得整个序列的最优折叠结构。

第四步：计算最小自由能在动态规划模型中，我们需要定义一系列的状态和转移方程来计算每一步的最小自由能。

具体来说，我们可以定义一个二维数组，每个元素代表了从第i个碱基到第j个碱基之间的最小自由能。

通过填充这个数组，我们可以递推地计算每一个子问题的最小自由能。

第五步：回溯获得最优解在计算得到最小自由能的同时，我们还需要记录每一步的选择，以便于最后回溯获得最优解的折叠结构。

deep visual-semantic alignments for generating image descriptions

Deep Visual-Semantic Alignments for Generating Image DescriptionsAndrej Karpathy Li Fei-FeiDepartment of Computer Science,Stanford University{karpathy,feifeili}@AbstractWe present a model that generates free-form natural lan-guage descriptions of image regions.Our model leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between text and vi-sual data.Our approach is based on a novel combination of Convolutional Neural Networks over image regions,bidi-rectional Recurrent Neural Networks over sentences,and a structured objective that aligns the two modalities through a multimodal embedding.We then describe a Recurrent Neu-ral Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions.We demonstrate the effectiveness of our alignment model with ranking experiments on Flickr8K,Flickr30K and COCO datasets,where we substantially improve on the state of the art.We then show that the sentences created by our gen-erative model outperform retrieval baselines on the three aforementioned datasets and a new dataset of region-level annotations.1.IntroductionA quick glance at an image is sufﬁcient for a human to point out and describe an immense amount of details about the vi-sual scene[8].However,this remarkable ability has proven to be an elusive task for our visual recognition models.The majority of previous work in visual recognition has focused on labeling images with aﬁxed set of visual categories,and great progress has been achieved in these endeavors[36,6]. However,while closed vocabularies of visual concepts con-stitute a convenient modeling assumption,they are vastly restrictive when compared to the enormous amount of rich descriptions that a human can compose.Some pioneering approaches that address the challenge of generating image descriptions have been developed[22,7]. However,these models often rely on hard-coded visual con-cepts and sentence templates,which imposes limits on their variety.Moreover,the focus of these works has been on re-ducing complex visual scenes into a single sentence,which we consider as an unnecessaryrestriction.Figure1.Our model generates free-form natural language descrip-tions of image regions.In this work,we strive to take a step towards the goal of generating dense,free-form descriptions of images(Figure1).The primary challenge towards this goal is in the de-sign of a model that is rich enough to reason simultaneously about contents of images and their representation in the do-main of natural language.Additionally,the model should be free of assumptions about speciﬁc hard-coded templates, rules or categories and instead rely primarily on training data.The second,practical challenge is that datasets of im-age captions are available in large quantities on the internet [14,46,29],but these descriptions multiplex mentions of several entities whose locations in the images are unknown.Our core insight is that we can leverage these large image-sentence datasets by treating the sentences as weak labels, in which contiguous segments of words correspond to some particular,but unknown location in the image.Our ap-proach is to infer these alignments and use them to learna generative model of descriptions.Concretely,our contri-butions are twofold:•We develop a deep neural network model that in-fers the latent alignment between segments of sen-1tences and the region of the image that they describe.Our model associates the two modalities through a common,multimodal embedding space and a struc-tured objective.We validate the effectiveness of this approach on image-sentence retrieval experiments in which we surpass the state-of-the-art.•We introduce a multimodal Recurrent Neural Network architecture that takes an input image and generates its description in text.Our experiments show that the generated sentences signiﬁcantly outperform retrieval-based baselines,and produce sensible qualitative pre-dictions.We then train the model on the inferred cor-respondences and evaluate its performance on a new dataset of region-level annotations.We make our code,data and annotations publicly available.2.Related WorkDense image annotations.Our work shares the high-level goal of densely annotating the contents of images with many works before us.Barnard et al.[1]and Socher et al.[38]studied the multimodal correspondence between words and images to annotate segments of images.Several works[26,12,9]studied the problem of holistic scene un-derstanding in which the scene type,objects and their spa-tial support in the image is inferred.However,the focus of these works is on correctly labeling scenes,objects and re-gions with aﬁxed set of categories,while our focus is on richer and higher-level descriptions of regions. Generating textual descriptions.Multiple works have ex-plored the goal of annotating images with textual descrip-tions on the scene level.A number of approaches pose the task as a retrieval problem,where the most compatible annotation in the training set is transferred to a test image [14,39,7,34,17],or where training annotations are broken up and stitched together[23,27,24].However,these meth-ods rely on a large amount of training data to capture the variety in possible outputs,and are often expensive at test time due to their non-parametric nature.Several approaches have been explored for generating image captions based on ﬁxed templates that areﬁlled based on the content of the im-age[13,22,7,43,44,4].This approach still imposes limits on the variety of outputs,but the advantage is that theﬁnal results are more likely to be syntactically correct.Instead of using aﬁxed template,some approaches that use a gen-erative grammar have also been developed[33,45].More closely related to our approach is the work of Srivastava et al.[40]who use a Deep Boltzmann Machine to learn a joint distribution over a images and tags.However,they do not generate extended phrases.More recently,Kiros et al.[19] developed a log-bilinear model that can generate full sen-tence descriptions.However,their model uses aﬁxed win-dow context,while our Recurrent Neural Network model can condition the probability distribution over the next word in the sentence on all previously generated words. Grounding natural language in images.A number of ap-proaches have been developed for grounding textual data in the visual domain.Kong et al.[20]develop a Markov Ran-dom Field that infers correspondences from parts of sen-tences to objects to improve visual scene parsing in RGBD images.Matuszek et al.[30]learn joint language and per-ception model for grounded attribute learning in a robotic setting.Zitnick et al.[48]reason about sentences and their grounding in cartoon scenes.Lin et al.[28]retrieve videos from a sentence description using an intermediate graph representation.The basic form of our model is in-spired by Frome et al.[10]who associate words and images through a semantic embedding.More closely related is the work of Karpathy et al.[18],who decompose images and sentences into fragments and infer their inter-modal align-ment using a ranking objective.In contrast to their model which is based on grounding dependency tree relations,our model aligns contiguous segments of sentences which are more meaningful,interpretable,and notﬁxed in length. Neural networks in visual and language domains.Mul-tiple approaches have been developed for representing im-ages and words in higher-level representations.On the im-age side,Convolutional Neural Networks(CNNs)[25,21] have recently emerged as a powerful class of models for image classiﬁcation and object detection[36].On the sen-tence side,our work takes advantage of pretrained word vectors[32,15,2]to obtain low-dimensional representa-tions of words.Finally,Recurrent Neural Networks have been previously used in language modeling[31,41],but we additionally condition these models on images.3.Our ModelOverview.The ultimate goal of our model is to generate descriptions of image regions.During training,the input to our model is a set of images and their corresponding sen-tence descriptions(Figure2).Weﬁrst present a model that aligns segments of sentences to the visual regions that they describe through a multimodal embedding.We then treat these correspondences as training data for our multimodal Recurrent Neural Network model which learns to generate the descriptions.3.1.Learning to align visual and language data Our alignment model assumes an input dataset of images and their sentence descriptions.The key challenge to in-ferring the association between visual and textual data is that sentences written by people make multiple references to some particular,but unknown locations in the image.For example,in Figure2,the words“Tabby cat is leaning”referFigure 2.Overview of our approach.A dataset of images and their sentence descriptions is the input to our model (left).Our model ﬁrst infers the correspondences (middle)and then learns to generate novel descriptions (right).to the cat,the words “wooden table”refer to the table,etc.We would like to infer these latent correspondences,with the goal of later learning to generate these snippets from image regions.We build on the basic approach of Karpa-thy et al.[18],who learn to ground dependency tree re-lations in sentences to image regions as part of a ranking objective.Our contribution is in the use of bidirectional recurrent neural network to compute word representations in the sentence,dispensing of the need to compute depen-dency trees and allowing unbounded interactions of words and their context in the sentence.We also substantially sim-plify their objective and show that both modiﬁcations im-prove ranking performance.We ﬁrst describe neural networks that map words and image regions into a common,multimodal embedding.Then we introduce our novel objective,which learns the embedding representations so that semantically similar concepts across the two modalities occupy nearby regions of the space.3.1.1Representing imagesFollowing prior work [22,18],we observe that sentencedescriptions make frequent references to objects and their attributes.Thus,we follow the method of Girshick et al.[11]to detect objects in every image with a Region Convo-lutional Neural Network (RCNN).The CNN is pre-trained on ImageNet [3]and ﬁnetuned on the 200classes of the ImageNet Detection Challenge [36].To establish fair com-parisons to Karpathy et al.[18],we use the top 19detected locations and the whole image and compute the represen-tations based on the pixels I b inside each bounding box as follows:v =W m [CNN θc (I b )]+b m ,(1)where CNN (I b )transforms the pixels inside bounding box I b into 4096-dimensional activations of the fully connected layer immediately before the classiﬁer.The CNN parame-ters θc contain approximately 60million parameters and the architecture closely follows the network of Krizhevsky et al [21].The matrix W m has dimensions h ×4096,where h is the size of the multimodal embedding space (h ranges from 1000-1600in our experiments).Every image is thus repre-sented as a set of h -dimensional vectors {v i |i =1...20}.3.1.2Representing sentencesTo establish the inter-modal relationships,we would like to represent the words in the sentence in the same h -dimensional embedding space that the image regions oc-cupy.The simplest approach might be to project every in-dividual word directly into this embedding.However,this approach does not consider any ordering and word context information in the sentence.An extension to this idea is to use word bigrams,or dependency tree relations as pre-viously proposed [18].However,this still imposes an ar-bitrary maximum size of the context window and requires the use of Dependency Tree Parsers that might be trained on unrelated text corpora.To address these concerns,we propose to use a bidirectional recurrent neural network (BRNN)[37]to compute the word representations.In our setting,the BRNN takes a sequence of N words (encoded in a 1-of-k representation)and trans-forms each one into an h -dimensional vector.However,the representation of each word is enriched by a variably-sized context around that ing the index t =1...N to denote the position of a word in a sentence,the precise form of the BRNN we use is as follows:x t =W w I t(2)e t =f (W e x t +b e )(3)h f t =f (e t +W f h ft −1+b f )(4)h b t =f (e t +W b h b t +1+b b )(5)s t =f (W d (h f t +h b t )+b d ).(6)Here,I t is an indicator column vector that is all zeros except for a single one at the index of the t -th word in a word vo-cabulary.The weights W w specify a word embedding ma-trix that we initialize with 300-dimensional word2vec [32]weights and keep ﬁxed in our experiments due to overﬁtting concerns.Note that the BRNN consists of two independent streams of processing,one moving left to right (h f t )and theother right to left (h bt )(see Figure 3for diagram).The ﬁ-nal h -dimensional representation s t for the t -th word is a function of both the word at that location and also its sur-rounding context in the sentence.Technically,every s t is a function of all words in the entire sentence,but our empir-Figure3.Diagram for evaluating the image-sentence score S kl. Object regions are embedded with a CNN(left).Words(enriched by their context)are embedded in the same multimodal space with a BRNN(right).Pairwise similarities are computed with inner products(magnitudes shown in grayscale)andﬁnally reduced to image-sentence score with Equation8.icalﬁnding is that theﬁnal word representations(s t)align most strongly to the visual concept of the word at that lo-cation(I t).Our hypothesis is that the strength of inﬂuence diminishes with each step of processing since s t is a more direct function of I t than of the other words in the sentence. We learn the parameters W e,W f,W b,W d and the respec-tive biases b e,b f,b b,b d.A typical size of the hidden rep-resentation in our experiments ranges between300-600di-mensions.We set the activation function f to the rectiﬁed linear unit(ReLU),which computes f:x→max(0,x).3.1.3Alignment objectiveWe have described the transformations that map every im-age and sentence into a set of vectors in a common h-dimensional space.Since our labels are at the level of en-tire images and sentences,our strategy is to formulate an image-sentence score as a function of the individual scores that measure how well a word aligns to a region of an im-age.Intuitively,a sentence-image pair should have a high matching score if its words have a conﬁdent support in the image.In Karpathy et al.[18],they interpreted the dot product v T i s t between an image fragment i and a sentence fragment t as a measure of similarity and used these to de-ﬁne the score between image k and sentence l as:S kl=t∈g li∈g kmax(0,v T i s t).(7)Here,g k is the set of image fragments in image k and g l is the set of sentence fragments in sentence l.The indices k,l range over the images and sentences in the training set. Together with their additional Multiple Instance Learning objective,this score carries the interpretation that a sentence fragment aligns to a subset of the image regions whenever the dot product is positive.We found that the following reformulation simpliﬁes the model and alleviates the need for additional objectives and their hyperparameters:S kl=t∈g lmax i∈gkv T i s t.(8)Here,every word s t aligns to the single best image region. As we show in the experiments,this simpliﬁed model also leads to improvements in theﬁnal ranking performance. Assuming that k=l denotes a corresponding image and sentence pair,theﬁnal max-margin,structured loss remains:C(θ)=klmax(0,S kl−S kk+1)rank images(9)+lmax(0,S lk−S kk+1)rank sentences.This objective encourages aligned image-sentences pairs to have a higher score than misaligned pairs,by a margin.3.1.4Decoding text segment alignments to images Consider an image from the training set and its correspond-ing sentence.We can interpret the quantity v T i s t as the un-normalized log probability of the t−th word describing any of the bounding boxes in the image.However,since we are ultimately interested in generating snippets of text instead of single words,we would like to align extended,contigu-ous sequences of words to a single bounding box.Note that the na¨ıve solution that assigns each word independently to the highest-scoring region is insufﬁcient because it leads to words getting scattered inconsistently to different regions. To address this issue,we treat the true alignments as latent variables in a Markov Random Field(MRF)where the bi-nary interactions between neighboring words encourage an alignment to the same region.Concretely,given a sentence with N words and an image with M bounding boxes,we introduce the latent alignment variables a j∈{1..M}for j=1...N and formulate an MRF in a chain structure along the sentence as follows:E(a)=j=1...NψU j(a j)+j=1...N−1ψB j(a j,a j+1)(10)ψU j(a j=t)=v T i s t(11)ψB j(a j,a j+1)=β1[a j=a j+1].(12) Here,βis a hyperparameter that controls the afﬁnity to-wards longer word phrases.This parameter allows us to interpolate between single-word alignments(β=0)andFigure4.Diagram of our multimodal Recurrent Neural Network generative model.The RNN takes an image,a word,the context from previous time steps and deﬁnes a distribution over the next word.START and END are special tokens.aligning the entire sentence to a single,maximally scoring region whenβis large.We minimize the energy toﬁnd the best alignments a using dynamic programming.The output of this process is a set of image regions annotated with seg-ments of text.We now describe an approach for generating novel phrases based on these correspondences.3.2.Multimodal Recurrent Neural Network forgenerating descriptionsIn this section we assume an input set of images and their textual descriptions.These could be full images and their sentence descriptions,or regions and text snippets as dis-cussed in previous sections.The key challenge is in the de-sign of a model that can predict a variable-sized sequence of outputs.In previously developed language models based on Recurrent Neural Networks(RNNs)[31,41,5],this is achieved by deﬁning a probability distribution of the next word in a sequence,given the current word and context from previous time steps.We explore a simple but effective ex-tension that additionally conditions the generative process on the content of an input image.More formally,the RNN takes the image pixels I and a sequence of input vectors (x1,...,x T).It then computes a sequence of hidden states (h1,...,h t)and a sequence of outputs(y1,...,y t)by iter-ating the following recurrence relation for t=1to T:b v=W hi[CNNθc(I)](13)h t=f(W hx x t+W hh h t−1+b h+b v)(14)y t=softmax(W oh h t+b o).(15) In the equations above,W hi,W hx,W hh,W oh and b h,b o are a set of learnable weights and biases.The output vector y t has the size of the word dictionary and one additional di-mension for a special END token that terminates the gener-ative process.Note that we provide the image context vector b v to the RNN at every iteration so that it does not have to remember the image content while generating words. RNN training.The RNN is trained to combine a word(x t), the previous context(h t−1)and the image information(b v) to predict the next word(y t).Concretely,the training pro-ceeds as follows(refer to Figure4):We set h0= 0,x1to a special START vector,and the desired label y1as theﬁrst word in the sequence.In particular,we use the word em-bedding for“the”as the START vector x1.Analogously, we set x2to the word vector of theﬁrst word and expect the network to predict the second word,etc.Finally,on the last step when x T represents the last word,the target label is set to a special END token.The cost function is to maximize the log probability assigned to the target labels.RNN at test time.The RNN predicts a sentence as follows: We compute the representation of the image b v,set h0=0, x1to the embedding of the word“the”,and compute the distribution over theﬁrst word y1.We sample from the dis-tribution(or pick the argmax),set its embedding vector as x2,and repeat this process until the END token is generated.3.3.OptimizationWe use Stochastic Gradient Descent with mini-batches of 100image-sentence pairs and momentum of0.9to optimize the alignment model.We cross-validate the learning rate and the weight decay.We also use dropout regularization in all layers except in the recurrent layers[47].The generative RNN is more difﬁcult to optimize,party due to the word frequency disparity between rare words,and very common words(such as the END token).We achieved the best re-sults using RMSprop[42],which is an adaptive step size method that scales the gradient of each weight by a running average of its gradient magnitudes.4.ExperimentsDatasets.We use the Flickr8K[14],Flickr30K[46]and COCO[29]datasets in our experiments.These datasets contain8,000,31,000and123,000images respectively and each is annotated with5sentences using Amazon Mechanical Turk.For Flickr8K and Flickr30K,we use 1,000images for validation,1,000for testing and the rest for training(consistent with[14,18]).For COCO we use 5,000images for both validation and testing.Data Preprocessing.We convert all sentences to lower-case,discard non-alphanumeric characters,andﬁlter out the articles“an”,“a”,and“the”for efﬁciency.Our word vocabulary contains20,000words.4.1.Image-Sentence Alignment EvaluationWeﬁrst investigate the quality of the inferred text and im-age alignments.As a proxy for this evaluation we perform ranking experiments where we consider a withheld set of images and sentences and then retrieve items in one modal-ity given a query from the other.We use the image-sentence score S kl(Section3.1.3)to evaluate a compatibility score between all pairs of test images and sentences.We then re-port the median rank of the closest ground truth result in theImage Annotation Image SearchModel R@1R@5R@10Med r R@1R@5R@10Med rDeViSE(Frome et al.[10]) 4.518.129.226 6.721.932.725SDT-RNN(Socher et al.[39])9.629.841.1168.929.841.116DeFrag(Karpathy et al.[18])12.632.944.0149.729.642.515Our implementation of DeFrag[18]13.835.848.210.49.528.240.315.6Our model:DepTree edges14.837.950.09.411.631.443.813.2Our model:BRNN16.540.654.27.611.832.144.712.4Flickr30KDeViSE(Frome et al.[10]) 4.518.129.226 6.721.932.725SDT-RNN(Socher et al.[39])9.629.841.1168.929.841.116DeFrag(Karpathy et al.[18])14.237.751.31010.230.844.214Our implementation of DeFrag[18]19.244.558.0 6.012.935.447.510.8Our model:DepTree edges20.046.659.4 5.415.036.548.210.4Our model:BRNN22.248.261.4 4.815.237.750.59.2COCOOur model:1K test images29.462.075.9 2.520.952.869.2 4.0Our model:5K test images11.832.545.412.28.924.936.319.5Table1.Image-Sentence ranking experiment results.R@K is Recall@K(high is good).Med r is the median rank(low is good).In the results for our models,we take the top5validation set models,evaluate each independently on the test set and then report the average performance.The standard deviations on the recall values range from approximately0.5to1.0.list and Recall@K,which measures the fraction of times a correct item was found among the top K results.The results of these experiments can be found in Table1,and exam-ple retrievals in Figure5.We now highlight some of the takeaways.Our full model outperforms previous work.We compare our full model(“Our model:BRNN”)to the following base-lines:DeViSE[10]is a model that learns a score between words and images.As the simplest extension to the setting of multiple image regions and multiple words,Karpathy et al.[18]averaged the word and image region representa-tions to obtain a single vector for each modality.Socher et al.[39]is trained with a similar objective,but instead of averaging the word representations,they merge word vec-tors into a single sentence vector with a Recursive Neural Network.DeFrag are the results reported by Karpathy et al.[18].Since we use different word vectors,dropout for regularization and different cross-validation ranges(includ-ing larger embedding sizes),we re-implemented their cost function for a fair comparison(“Our implementation of De-Frag”).In all of these cases,our full model(“Our model: BRNN”)provides consistent improvements.Our simpler cost function improves performance.We now try to understand the sources of these improvements. First,we removed the BRNN and used dependency tree re-lations exactly as described in Karpathy et al.[18](“Our model:DepTree edges”).The only difference between this model and“Our reimplementation of DeFrag”is the new, simpler cost function introduced in Section3.1.3.We see that our formulation shows consistent improvements.BRNN outperforms dependency tree relations.Further-more,when we replace the dependency tree relations with the BRNN,we observe additional performance improve-ments.Since the dependency relations were shown to work better than single words and bigrams[18],this suggests that the BRNN is taking advantage of contexts longer than two words.Furthermore,our method does not rely on extracting a Dependency Tree and instead uses the raw words directly. COCO results for future comparisons.The COCO dataset has only recently been released,and we are not aware of other published ranking results.Therefore,we re-port results on a subset of1,000images and the full set of 5,000test images for future comparisons.Qualitative.As can be seen from example groundings in Figure5,the model discovers interpretable visual-semantic correspondences,even for small or relatively rare objects such as“seagulls”and“accordion”.These details would be missed by models that only reason about full images. 4.2.Evaluation of Generated DescriptionsWe have demonstrated that our alignment model produces state of the art ranking results and qualitative experiments suggest that the model effectively infers the alignment be-tween words and image regions.Our task is now to synthe-size these sentence snippets given new image regions.We evaluate these predictions with the BLEU[35]score,which despite multiple problems[14,22]is still considered to be the standard metric of evaluation in this setting.The BLEU score evaluates a candidate sentence by measuring the frac-tion of n-grams that appear in a set of references.Figure5.Example alignments predicted by our model.For every test image above,we retrieve the most compatible test sentence and visualize the highest-scoring region for each word(before MRF smoothing described in Section3.1.4)and the associated scores(v T i s t). We hide the alignments of low-scoring words to reduce clutter.We assign each region an arbitrary color.Flickr8K Flickr30K COCOMethod of generating text B-1B-2B-3B-1B-2B-3B-1B-2B-3Human agreement0.590.350.160.640.360.160.570.310.13Ranking:Nearest Neighbor0.290.110.030.270.080.020.320.110.03Generating:RNN0.420.190.060.450.200.060.500.250.12Table2.BLEU score evaluation of full image predictions on1,000images.B-n is BLEU score that uses up to n-grams(high is good).Our multimodal RNN outperforms retrieval baseline. Weﬁrst verify that our multimodal RNN is rich enough to support sentence generation for full images.In this experi-ment,we trained the RNN to generate sentences on full im-ages from Flickr8K,Flickr30K,and COCO datasets.Then at test time,we use theﬁrst four out ofﬁve sentences as references and theﬁfth one to evaluate human agreement. We also compare to a ranking baseline which uses the best model from the previous section(Section4.1)to annotate each test image with the highest-scoring sentence from the training set.The quantitative results of this experiment are in Table2.Note that the RNN model conﬁdently outper-forms the retrieval method.This result is especially interest-ing in COCO dataset,since its training set consists of more than600,000sentences that cover a large variety of de-scriptions.Additionally,compared to the retrieval baseline which compares each image to all sentences in the training set,the RNN takes a fraction of a second to evaluate.We show example fullframe predictions in Figure6.Our generative model(shown in blue)produces sensible de-scriptions,even in the last two images that we consider to be failure cases.Additionally,we veriﬁed that none of these sentences appear in the training set.This suggests that the model is not simply memorizing the training data.How-ever,there are20occurrences of“man in black shirt”and 60occurrences of“is paying guitar”,which the model may have composed to describe theﬁrst image.Region-level evaluation.Finally,we evaluate our region RNN which was trained on the inferred,intermodal corre-spondences.To support this evaluation,we collected a new dataset of region-level annotations.Concretely,we asked8 people to label a subset of COCO test images with region-level text descriptions.The labeling interface consisted of a single test image,and the ability to draw a bounding box and annotate it with text.We provided minimal constraints and instructions,except to“describe the content of each box”and we encouraged the annotators to describe a large variety of objects,actions,stuff,and high-level concepts. Theﬁnal dataset consists of1469annotations in237im-ages.There are on average6.2annotations per image,and each one is on average4.13words long.We compare three models on this dataset:The region RNN model,a fullframe RNN model that was trained on full im-ages and sentences,and a ranking baseline.To predict de-scriptions with the ranking baseline,we take the number of words in the shortest reference annotation and search the training set sentences for the highest scoring segment of text。

Mellanox Ethernet 网络设备用户手册说明书

SOLUTION BRIEFKEY BUSINESS BENEFITSEXECUTIVE SUMMARYAnalytic tools such as Spark, Presto and Hive are transforming how enterprises interact with and derive value from their data. Designed to be in memory, these computing and analytical frameworks process volumes of data 100x faster than Hadoop Map/Reduce and HDFS - transforming batch processing tasks into real-time analysis. These advancements have created new business models while accelerating the process of digital transformation for existing enterprises.A critical component in this revolution is the performance of the networking and storage infrastructure that is deployed in support of these modern computing applications. Considering the volumes of data that must be ingested, stored, and analyzed, it quickly becomes evident that the storage architecture must be both highly performant and massively scalable.This solution brief outlines how the promise of in-memory computing can be delivered using high-speed Mellanox Ethernet infrastructure and MinIO’s ultra-high performance object storage solution.IN MEMORY COMPUTINGWith data constantly flowing from multiple sources - logfiles, time series data, vehicles,sensors, and instruments – the compute infrastructure must constantly improve to analyze data in real time. In-memory computing applications, which load data into the memory of a cluster of servers thereby enabling parallel processing, are achieving speeds up to 100x faster than traditional Hadoop clusters that use MapReduce to analyze and HDFS to store data.Although Hadoop was critical to helping enterprises understand the art of the possible in big data analytics, other applications such as Spark, Presto, Hive, H2O.ai, and Kafka have proven to be more effective and efficient tools for analyzing data. The reality of running large Hadoop clusters is one of immense complexity, requiring expensive administrators and a highly inefficient aggregation of compute and storage. This has driven the adoption of tools like SparkDelivering In-memory Computing Using Mellanox Ethernet Infrastructure and MinIO’s Object Storage SolutionMinIO and Mellanox: Better TogetherHigh performance object storage requires the right server and networking components. With industryleading performance combined with the best innovation to accelerate data infrastructure Mellanox provides the networking foundation needed to connect in-memory computing applications with MinIO high performance object storage. Together, they allow in-memory compute applications to access and process large amounts of data to provide high speed business insights.Simple to Deploy, Simpler to ManageMinIO can be installed and configured within minutes simply by downloading a single binary and executing it. The amount of configuration options and variations has been kept to a minimum resulting in near-zero system administration tasks and few paths to failures. Upgrading MinIO is done with a single command which is non-disruptive and incurs zero downtime.MinIO is distributed under the terms of the Apache* License Version 2.0 and is actively developed on Github. MinIO’s development community starts with the MinIO engineering team and includes all of the 4,500 members of MinIO’s Slack Workspace. Since 2015 MinIO has gathered over 16K stars on Github making it one of the top 25 Golang* projects based on a number of stars.which are simpler to use and take advantage of the massive benefits afforded by disaggregating storage and compute. These solutions, based on low cost, memory dense compute nodes allow developers to move analytic workloads into memory where they execute faster, thereby enabling a new class of real time, analytical use cases.These modern applications are built using cloud-native technologies and,in turn, use cloud-native storage. The emerging standard for both the public and private cloud, object storage is prized for its near infinite scalability and simplicity - storing data in its native format while offering many of the same features as block or file. By pairing object storage with high speed, high bandwidth networking and robust compute enterprises can achieve remarkable price/performance results.DISAGGREGATE COMPUTE AND STORAGE Designed in an era of slow 1GbE networks, Hadoop (MapReduce and HDFS) achieved its performance by moving compute tasks closer to the data. A Hadoop cluster often consists of many 100s or 1000s of server nodes that combine both compute and storage.The YARN scheduler first identifies where the data resides, then distributes the jobs to the specific HDFS nodes. This architecture can deliver performance, but at a high price - measured in low compute utilization, costs to manage, and costs associated with its complexity at scale. Also, in practice, enterprises don’t experience high levels of data locality with the results being suboptimal performance.Due to improvements in storage and interconnect technologies speeds it has become possible to send and receive data remotely at high speeds with little (less than 1 microsecond) to no latency difference than if the storage were local to the compute.As a result, it is now possible to separate storage from the compute with no performance penalty. Data analysis is still possible in near real time because the interconnect between the storage and the compute is fast enough to support such demands.By combining dense compute nodes, large amounts of RAM, ultra-highspeed networks and fast object storage, enterprises are able to disaggregate storage from compute creating the flexibility to upgrade, replace, or add individual resources independently. This also allows for better planning for future growth as compute and storage can be added independently and when necessary, improving utilization and budget control.Multiple processing clusters can now share high performance object storage so that different types of processing, such as advanced queries, AI model training, and streaming data analysis, can run on their own independent clusters while sharing the same data stored on the object storage. The result is superior performance and vastly improved economics.HIGH PERFORMANCE OBJECT STORAGEWith in-memory computing, it is now possible to process volumes of data much faster than with Hadoop Map/Reduce and HDFS. Supporting these applications requires a modern data infrastructure with a storage foundation that is able to provide both the performance required by these applications and the scalability to handle the immense volume of data created by the modern enterprise.Building large clusters of storage is best done by combining simple building blocks together, an approach proven out by the hyper-scalers. By joining one cluster with many other clusters, MinIO can grow to provide a single, planet-wide global namespace. MinIO’s object storage server has a wide rangeof optimized, enterprise-grade features including erasure code and bitrot protection for data integrity, identity management, access management, WORM and encryption for data security and continuous replication and lamba compute for dynamic, distributed data.MinIO object storage is the only solution that provides throughput rates over 100GB/sec and scales easily to store 1000s of Petabytes of data under a single namespace. MinIO runs Spark queries faster, captures streaming data more effectively, and shortens the time needed to test, train and deploy AI algorithms.LATENCY AND THROUGHPUTIndustry-leading performance and IT efficiency combined with the best of open innovation assist in accelerating big data analytics workloads which require intensive processing. The Mellanox ConnectX® adapters reduce the CPU overhead through advanced hardware-based stateless offloads and flow steering engines. This allows big data applications utilizing TCP or UDP over IP transport to achieve the highest throughput, allowing completion of heavier analytic workloads in less time for big data clusters so organizations can unlock and efficiently scale data-driven insights while increasing application densities for their business.Mellanox Spectrum® Open Ethernet switches feature consistently low latency and can support a variety of non-blocking, lossless fabric designs while delivering data at line-rate speeds. Spectrum switches can be deployed in a modern spine-leaf topology to efficiently and easily scalefor future needs. Spectrum also delivers packet processing without buffer fairness concerns. The single shared buffer in Mellanox switches eliminates the need to manage port mapping and greatly simplifies deployment. In an© Copyright 2019. Mellanox, Mellanox logo, and ConnectX are registered trademarks of Mellanox Technologies, Ltd. Mellanox Onyx is a trademark of Mellanox Technologies, Ltd. All other trade-marks are property of their respective owners350 Oakmead Parkway, Suite 100 Sunnyvale, CA 94085Tel: 408-970-3400 • Fax: MLNX-423558315-99349object storage environment, fluid resource pools will greatly benefit from fair load balancing. As a result, Mellanox switches are able to deliver optimal and predictable network performance for data analytics workloads.The Mellanox 25, 50 or 100G Ethernet adapters along with Spectrum switches results in an industry leading end-to-end, high bandwidth, low latency Ethernet fabric. The combination of in-memory processing for applications and high-performance object storage from MinIO along with reduced latency and throughput improvements made possible by Mellanox interconnects creates a modern data center infrastructure that provides a simple yet highly performant and scalable foundation for AI, ML, and Big Data workloads.CONCLUSIONAdvanced applications that use in-memory computing, such as Spark, Presto and Hive, are revealing business opportunities to act in real-time on information pulled from large volumes of data. These applications are cloud native, which means they are designed to run on the computing resources in the cloud, a place where Hadoop HDFS is being replaced in favor of using data infrastructures that disaggregates storage from compute. These applications now use object storage as the primary storage vehicle whether running in the cloud or on- premises.Employing Mellanox networking and MinIO object storage allows enterprises to disaggregate compute from storage achieving both performance and scalability. By connecting dense processing nodes to MinIO object storage nodes with high performance Mellanox networking enterprises can deploy object storage solutions that can provide throughput rates over 100GB/sec and scales easily to store 1000s of Petabytes of data under a singlenamespace. The joint solution allows queries to run faster, capture streaming data more effectively, and shortens the time needed to test, train and deploy AI algorithms, effectively replacing existing Hadoop clusters with a data infrastructure solution, based on in-memory computing, that consumes a smaller data center footprint yet provides significantly more performance.WANT TO LEARN MORE?Click the link below to learn more about object storage from MinIO VAST: https://min.io/Follow the link below to learn more about Mellanox end-to-end Ethernet storage fabric:/ethernet-storage-fabric/。

lipofectamineRNAiMAX英文说明书

Lipofectamine™ RNAiMAXCat. No. 13778-075 Size: 0.75 mlCat. No. 13778-150 Size: 1.5 mlStore at +4°C (do not freeze) DescriptionLipofectamine™ RNAiMAX is a proprietary formulation specifically developed for the transfection of siRNA and Stealth™ RNAi duplexes into eukaryotic cells. Lipofectamine™ RNAiMAX provides the following advantages:• High transfection efficiencies in many cell types to minimize background expression from untransfected cells and maximize knockdown.• Minimal cytotoxicity to reduce non-specific effects and cellular stress.• Generally requires low concentrations of RNAi duplexes to obtain high knockdown levels, further minimizing non-specific effects.• A broad peak of optimal transfection activity with minimal cytotoxicity, allowing achievement of high knockdown levels despite differences in cell density, minor pipetting inaccuracies, and other variations.Important Guidelines for Transfection• Reverse transfection (page 2) and forward transfection (page 3) protocols can be used for most cell lines tested. Cell-type specific transfection protocols are available at /RNAi or through Technical Service.• We recommend Opti-MEM® I Reduced Serum Medium (Cat. No. 31985-062) to dilute RNAi duplexes and Lipofectamine™ RNAiMAX before complexing.• Do not add antibiotics to media during transfection as this causes cell death.• Test serum-free media for compatibility with Lipofectamine™ RNAiMAX. • To assess transfection efficiency, we recommend using a KIF11 Stealth™Select RNAi, as described in Assessing Transfection Efficiency (page 2). • Use 10 nM RNAi duplex and indicated procedure as a starting point;optimize transfections as described in Optimizing Transfections (page 3). Quality ControlLipofectamine™ RNAiMAX is tested for absence of microbial contamination with blood agar plates, Sabaraud dextrose agar plates, and fluid thioglycolate medium, for absence of RNAse activity, and functionally by transfection of Stealth™ RNAi and appropriate controls into a reporter cell line.Part No.: 13778.PPS Rev. Date: 11 Jan 2006For research use only. Not intended for any animal or human therapeutic or diagnostic use.For technical support, contact tech_service@.Page 2 Reverse TransfectionUse this procedure to reverse transfect Stealth™ RNAi or siRNA into mammalian cells in a 24-well format (for other formats, see Scaling Up or Down Transfections, page 4). In reverse transfections, the complexes are prepared inside the wells, after which cells and medium are added. Reverse transfections are faster to perform than forward transfections, and are the method of choice for high-throughput transfection. Optimize transfections as described in Optimizing Transfections (page 3), especially if transfecting a mammalian cell line for the first time. All amounts and volumes are given on a per well basis.1. For each well to be transfected, prepare RNAi duplex-Lipofectamine™RNAiMAX complexes as follows.a. Dilute 6 pmol RNAi duplex in 100 µl Opti-MEM® I Medium without serumin the well of the tissue culture plate. Mix gently.b. Mix Lipofectamine™ RNAiMAX gently before use, then add 1 µlLipofectamine™ RNAiMAX to each well containing the diluted RNAimolecules. Mix gently and incubate for 10-20 minutes at roomtemperature.2. Dilute cells in complete growth medium without antibiotics so that 500 µlcontains the appropriate number of cells to give 30-50% confluence 24 hours after plating. Use 20,000-50,000 cells/well for suspension cells.3. To each well with RNAi duplex - Lipofectamine™ RNAiMAX complexes, add500 µl of the diluted cells. This gives a final volume of 600 µl and a final RNA concentration of 10 nM. Mix gently by rocking the plate back and forth.4. Incubate the cells 24-72 hours at 37°C in a CO2 incubator until you are readyto assay for gene knockdown.Assessing Transfection EfficiencyTo qualitatively assess transfection efficiency, we recommend using a KIF11 Stealth™ Select RNAi (available through /rnaiexpress; for human cells, oligo HSS105842 is a good choice). Adherent cells in whichKIF11/Eg5 is knocked down exhibit a “rounded-up” phenotype after 24 hours due to a mitotic arrest (Weil, D. et al., Biotechniques (2002), 33: 1244-1248); slow growing cells may take up to 72 hours to display the rounded phenotype. Alternatively, growth inhibition can be assayed after 48-72 hours.Note: The BLOCK-iT™ Fluorescent Oligo (Cat. No. 2013) is optimized for use with Lipofectamine™ 2000, and is not recommended for Lipofectamine™ RNAiMAX.Page 3 Forward TransfectionUse this procedure to forward transfect Stealth™ RNAi or siRNA into mammalian cells in a 24-well format (for other formats, see Scaling Up or Down Transfections, page 4). In forward transfections, cells are plated in the wells, and the transfection mix is generally prepared and added the next day. Optimize transfections as described in Optimizing Transfections (page 3), especially if transfecting a mammalian cell line for the first time. All amounts and volumes are given on a per well basis.Note: For some cell lines (e.g. MCF-7 or HepG2), we recommend reverse transfections.1. One day before transfection, plate cells in 500 µl of growth medium withoutantibiotics such that they will be 30-50% confluent at the time of transfection.2. For each well to be transfected, prepare RNAi duplex-Lipofectamine™RNAiMAX complexes as follows:a. Dilute 6 pmol RNAi duplex in 50 µl Opti-MEM®I Reduced Serum Mediumwithout serum. Mix gently.b. Mix Lipofectamine™ RNAiMAX gently before use, then dilute 1 µl in 50 µlOpti-MEM® I Reduced Serum Medium. Mix gently.c. Combine the diluted RNAi duplex with the diluted Lipofectamine™RNAiMAX. Mix gently and incubate for 10-20 minutes at roomtemperature.3. Add the RNAi duplex-Lipofectamine™ RNAiMAX complexes to each wellcontaining cells. This gives a final volume of 600 µl and a final RNAconcentration of 10 nM. Mix gently by rocking the plate back and forth.4. Incubate the cells 24-48 hours at 37°C in a CO2 incubator until you areready to assay for gene knockdown. Medium may be changed after 4-6hours.Optimizing TransfectionsTo obtain the highest transfection efficiency and low non-specific effects, optimize transfection conditions by varying RNAi duplex and Lipofectamine™RNAiMAX concentrations. Test 0.6-30 pmol RNAi duplex (final concentration 1-50 nM) and 0.5-1.5 µl Lipofectamine™ RNAiMAX for 24-well format. For extended time course experiments (> 72 hours), consider a cell density that is 10-20% confluent 24 hours after plating.Note: The concentration of RNAi duplex required will vary depending on the efficacy of the duplex.Page 4 Scaling Up or Down TransfectionsTo transfect cells in different tissue culture formats, vary the amounts of Lipofectamine™ RNAiMAX, RNAi duplex, cells, and medium used in proportion to the relative surface area, as shown in the table.Culture vessel Rel.surf.area1Vol. ofplatingmediumDilutionmediumreversetransfectionDilutionmediumforwardtransfectionRNAi(pmol)RNAi(nM)Lipofect-amine™RNAiMAX296-well 0.2 100 µl 20 µl 2 x 10 µl 0.12-6 1-50 0.1-0.3 µl48-well 0.4 200 µl 40 µl 2 x 20 µl 0.24-12 1-50 0.2-0.6 µl24-well 1 500 µl 100 µl 2 x 50 µl 0.6-30 1-50 0.5-1.5 µl6-well 5 2.5 ml 500 µl 2 x 250 µl 3-150 1-50 2.5-7.5 µl60 mm 10 5 ml 1 ml 2 x 500 µl 6-300 1-50 5-15 µl100 mm 30 10 ml 2 ml 2 x 1 ml 12-600 1-50 15-35 µl1 Surface areas may vary depending on the manufacturer.2If the volume of Lipofectamine™ RNAiMAX is too small to dispense accurately, and you cannot pool dilutions, predilute Lipofectamine™ RNAiMAX 10-fold in Opti-MEM®I Reduced Serum Medium, and dispense a 10-fold higher amount (should be at least1.0 µl per well). Discard any unused diluted Lipofectamine™ RNAiMAX. Cotransfecting DNA and RNA using Lipofectamine™ RNAiMAX For cotransfections of plasmid DNA and Stealth™ RNAi or siRNA into mammalian cells, we recommend using Lipofectamine™ 2000 (Catalog no. 11668-027), which is superior for plasmid transfections. If you want to use Lipofectamine™ RNAiMAX for your cotransfections, perform a reverse transfection as described on page 2 with the following modifications:1a: Add 20 ng (for 24-well format) of plasmid DNA to the diluted RNAi duplex. 2: Add cells such that they will be 80-100% confluent 24 hours after plating.Purchaser NotificationThis product is covered by one or more Limited Use Label Licenses (see the Invitrogen catalog or our web-site, ). By the use of this product you accept the terms and conditions of all applicable Limited Use Label Licenses.Limited Use Label License No. 5Limited Use Label License No. 27©2006 Invitrogen Corporation. All rights reserved.。

2022年安徽理工大学计算机科学与技术专业《数据结构与算法》科目期末试卷A(有答案)

2022年安徽理工大学计算机科学与技术专业《数据结构与算法》科目期末试卷A（有答案）一、选择题1、有一个100*90的稀疏矩阵，非0元素有10个，设每个整型数占2字节，则用三元组表示该矩阵时，所需的字节数是（）。

A.60B.66C.18000D.332、用有向无环图描述表达式(A+B)*((A+B)／/A)，至少需要顶点的数目为（）。

A.5B.6C.8D.93、链表不具有的特点是（）。

A.插入、删除不需要移动元素B.可随机访问任一元素C.不必事先估计存储空间D.所需空间与线性长度成正比4、下列关于AOE网的叙述中，不正确的是（）。

A.关键活动不按期完成就会影响整个工程的完成时间B.任何一个关键活动提前完成，那么整个工程将会提前完成C.所有的关键活动提前完成，那么整个工程将会提前完成D.某些关键活动若提前完成，那么整个工程将会提前完成5、用不带头结点的单链表存储队列，其队头指针指向队头结点，队尾指针指向队尾结点，则在进行出队操作时（）。

A.仅修改队头指针B.仅修改队尾指针C.队头、队尾指针都可能要修改D.队头、队尾指针都要修改6、排序过程中，对尚未确定最终位置的所有元素进行一遍处理称为一趟排序。

下列排序方法中，每一趟排序结束时都至少能够确定一个元素最终位置的方法是（）。

Ⅰ．简单选择排序Ⅱ．希尔排序Ⅲ．快速排序Ⅳ．堆排Ⅴ．二路归并排序A．仅Ⅰ、Ⅲ、Ⅳ B．仅Ⅰ、Ⅱ、Ⅲ C．仅Ⅱ、Ⅲ、Ⅳ D．仅Ⅲ、Ⅳ、Ⅴ7、若元素a，b，c，d，e，f依次进栈，允许进栈、退栈操作交替进行，但不允许连续三次进行退栈操作，则不可能得到的出栈序列是（）。

8、已知一棵二叉树的前序遍历结果为ABCDEF，中序遍历结果为CBAEDF，则后序遍历结果为（）。

A.CBEFDAB.FEDCBAC.CBEDFAD.不定9、有关二叉树下列说法正确的是（）。

A.二叉树的度为2B.一棵二叉树的度可以小于2C.二叉树中至少有一个结点的度为2D.二叉树中任何一个结点的度都为210、下面给出的四种排序方法中，排序过程中的比较次数与排序方法无关的是（）。

甘阳

科研成果
an overview article). J. Shen, D. Zhang, Y. Wang, Y. Gan, AFM and SEM Study on Crystallographic and Topographical Evolution of Wet-Etched Patterned Sapphire Substrates (PSS): I. Cone-Shaped PSS Etched in Sulfuric Acid and Phosphoric Acid Mixture (3:1) at 230°C, ECS nol., 6 (2017) R24. 所有文章（All Publications）
谢谢观看
科研成果
） Y. Yuan, D. Zhang, F. Zhang, , Y. Gan, Crystallographic Orientation Dependence of Nanopattern Morphology and Size in Electropolished Polycrystalline and Monocrystalline Aluminum: An EBSD and SEM Study, ., 167 (2020) . （多晶和单晶铝，一定条件下电解抛光后，表面会产生纳米图案（如平行条纹或短条或无序结构），这不奇怪。但是，结合系统的EBSD、SEM表征和深入系统的数据分析，发现了意想不到的结果——纳米图案的类型和周期具有明显的晶粒取向和晶面取向依赖性。在机理方面也提出了一个改进的框架。后续还有更多结果。。。
目录
01 研究方向
02 主要贡献
研究方向
研究方向
主要研究方向：新能源材料与器件、电池、石墨烯纳米材料等的表面和界面物理化学。无机氧化物和陶瓷材料的表面微观结构与表面化学性质的关系，纳米材料制备、表征和应用。

分子生物学常用参考书目

二十一世纪是分子生物学发展的世纪，生命科学将进入一个新的时代——后基因组时代postgenomics
二十一世纪分子生物学发展的趋势：
1.功能基因组学 functional genomics 依附于对DNA序列的了解，应用基因组学的知识和工具
去了解影响发育和整个生物体的特征序列表达谱。酿酒酵母16条染色体的全部序列于1996年完成。
…
1997
Wilmut成功获得克隆羊—Dolly诞生；
1998
Renard 克隆牛诞生（体细胞→个体）；
…
2000 ,6.26 中、美、日、德、法、英6国，宣布人类基因组草图发表。
2000 ,10月科学家宣布将于2001年3月完成河豚鱼的基因组测序。
2000,12月14日英美等国科学家宣布绘出拟南芥基因组的完整图谱。
2003年4月14日六国科学家完成了人类基因组序列图的绘制，实现了人类基因组计划的所有目标。
二十世纪是以核酸为研究核心，带动分子生物学向纵深发展：
•
50年代双螺旋结构
•
60年代操纵子学说
•
70年代DNA重组
•
80年代PCR技术
•
90年代DNA测序
生命科学从宏观→微观→宏观；由分析→综合的时代。
分子生物学常用参考书目
2024/2/1
第一章绪论
一、什么是分子生物学？
Instant Notes in Molecular Biology
---Turner et al.
Molecular biology seeks to explain the relationships between the structure and function of biological molecules and how these relationships contribute to the operation and control of biochemical processes.

2022年南京理工大学计算机科学与技术专业《数据结构与算法》科目期末试卷A(有答案)

2022年南京理工大学计算机科学与技术专业《数据结构与算法》科目期末试卷A（有答案）一、选择题1、有一个100*90的稀疏矩阵，非0元素有10个，设每个整型数占2字节，则用三元组表示该矩阵时，所需的字节数是（）。

A.60B.66C.18000D.332、下列排序算法中，占用辅助空间最多的是（）。

A.归并排序B.快速排序C.希尔排序D.堆排序3、以下数据结构中，（）是非线性数据结构。

A.树B.字符串C.队D.栈4、动态存储管理系统中，通常可有（）种不同的分配策略。

A.1B.2C.3D.45、在用邻接表表示图时，拓扑排序算法时间复杂度为（）。

A.O(n)B.O(n+e)C.O(n*n)D.O(n*n*n)6、下列选项中，不能构成折半查找中关键字比较序列的是（）。

A．500，200，450，180 B．500，450，200，180C．180，500，200，450 D．180，200，500，4507、已知关键字序列5，8，12，19，28，20，15，22是小根堆（最小堆），插入关键字3，调整后的小根堆是（）。

A．3，5，12，8，28，20，15，22，19B．3，5，12，19，20，15，22，8，28C．3，8，12，5，20，15，22，28，19D．3，12，5，8，28，20，15，22，198、已知一棵二叉树的前序遍历结果为ABCDEF，中序遍历结果为CBAEDF，则后序遍历结果为（）。

A.CBEFDAB.FEDCBAC.CBEDFAD.不定9、一棵非空的二叉树的前序序列和后序序列正好相反，则该二叉树一定满足（）。

A.其中任意一个结点均无左孩子B.其中任意一个结点均无右孩子C.其中只有一个叶结点D.其中度为2的结点最多为一个10、下列二叉排序树中查找效率最高的是（）。

A.平衡二叉树B.二叉查找树C.没有左子树的二叉排序树D.没有右子树的二叉排序树二、填空题11、对单链表中元素按插入方法排序的C语言描述算法如下，其中L为链表头结点指针。

IBM Cognos Transformer V11.0 用户指南说明书

Dimensional Modeling Workflow................................................................................................................. 1 Analyzing Your Requirements and Source Data.................................................................................... 1 Preprocessing Your ...................................................................................................................... 2 Building a Prototype............................................................................................................................... 4 Refining Your Model............................................................................................................................... 5 Diagnose and Resolve Any Design Problems........................................................................................ 6

bibm的short paper -回复

bibm的short paper -回复[bibm的short paper]1. Introduction:The field of Bioinformatics and Computational Biology (BIBM) has emerged as a crucial area of research, combining biology and computer science to analyze biological data. In this short paper, we will explore the significance of BIBM and its impact on various aspects of scientific research and advancements.2. Understanding BIBM:Bioinformatics involves the development and application of computational tools and methods to study biological data. This field encompasses various sub-disciplines including genomics, proteomics, and transcriptomics, among others. Computational Biology, on the other hand, focuses on using computational techniques to model, simulate, and analyze biological systems and processes.3. Significance of BIBM:(a) Data analysis: BIBM plays a vital role in analyzing vast amounts of biological data, such as genomic and proteomic data, enablingscientists to draw meaningful conclusions and gain insights into complex biological phenomena.(b) Drug discovery and development: BIBM facilitates the identification of potential drug targets and aids in the development of novel therapeutic interventions by analyzing molecular interactions, predicting drug efficacy, and understanding drug resistance mechanisms.(c) Personalized medicine: The integration of genomic and clinical data allows for the development of personalized medicine tailored to individual patients, improving treatment outcomes and reducing adverse effects.(d) Disease diagnosis and prognosis: BIBM tools and algorithms are used to identify biomarkers and patterns in biological data, aiding in the early detection, accurate diagnosis, and prognosis of various diseases.(e) Evolutionary analysis: BIBM helps in studying the evolutionary relationships between different species, reconstructing phylogenetic trees, and understanding the genetic basis of speciesdivergence.4. Methods and Techniques in BIBM:(a) Sequence alignment: This technique helps in comparing DNA, RNA, and protein sequences to identify similarities and functional regions. It is essential for tasks like genome assembly and searching for homologous genes.(b) Machine learning: BIBM utilizes machine learning algorithms to classify diseases, predict protein structures, and analyze gene expression patterns. Machine learning allows for the extraction of hidden patterns and relationships from complex biological datasets.(c) Network analysis: BIBM employs network analysis to understand molecular interactions, regulatory networks, and protein-protein interactions, providing insights into the functional aspects of biological systems.(d) Structural biology methods: BIBM utilizes techniques such as molecular docking and molecular dynamics simulations to study protein-ligand interactions and protein folding, aiding in drugdesign and understanding protein functions.5. Challenges and Future Directions:Despite significant advancements in BIBM, several challenges remain. These include the need for improved computational algorithms, data integration, ethical considerations, and data privacy.In the future, BIBM is likely to continue playing a crucial role in various scientific disciplines. It will facilitate advances in precision medicine, systems biology, and the development of novel therapeutic interventions. Moreover, the integration of BIBM with other emerging technologies, such as artificial intelligence and big data analytics, will further enhance its impact and potential.6. Conclusion:Bioinformatics and Computational Biology (BIBM) have revolutionized biological research by providing powerful tools and methods for the analysis of complex biological data. From drug discovery to personalized medicine, BIBM has made significant contributions to various scientific domains. With ongoingadvancements and interdisciplinary collaborations, BIBM is set to drive further breakthroughs in the future, enabling a deeper understanding of life's intricacies and contributing to improved healthcare outcomes.。

c4t3p1阅读原文

c4t3p1阅读原文第一段说ambergris这个东西很久以前就有了，然后说ambergris的用途有for medicine，spice，用来制作perfume 什么的等等（有题，matching）然后说但是人们不知道它是从哪里来的，再就是说在古代it worth in weight in gold，当然是贵了。

第二段说以前人们一直把ambergris和amber当作一种东西。

但是有个叫Dick的作者写了一本书讲了这两个东西的区别（有题，matching）说ambergris 通常发现在海面或者shore，但是仍然不知道是从哪里来的。

Amber是一种什么东西，与松树pine有关，然后说了amber的一些特性hard，transparent，等等，用来做装饰品，头饰什么的，同样very costly。

（有题，matching）第三段说ambergris是与sperm whale的intestine肠子里的消化digest 某种东西有关。

以为intestine会有题，结果没有，提到了马可波罗，好像与这个发现有关（没题，当笑话好了）第四段就是具体describe ambergris的产生过程了。

（summary 题）大意是，sperm whale吃一种东西叫beaks of squalid，肠子就有助消化，但是不能完全消化，就转化成了另一种东西，应该是体内的垃圾。

这种垃圾是soft的，会被sperm whale 呕吐出来be vomited up。

然后这种东西遇到空气就会变硬harden，于是就形成了ambergris了，也解释了为什么ambergris总在海面和shore被发现。

第五段说人们为了获得ambergris而捕杀sperm whale 导致了濒临灭绝。

给了一个数据说in 20th century，90% ambergris was made in the processing of killing sperm whale。

基于深度学习的垃圾邮件检测

Computer Science and Application 计算机科学与应用, 2023, 13(4), 764-772 Published Online April 2023 in Hans. https:///journal/csa https:///10.12677/csa.2023.134075基于深度学习的垃圾邮件检测俞荧妹，禹素萍，许武军，范红东华大学信息科学与技术学院，上海收稿日期：2023年3月17日；录用日期：2023年4月14日；发布日期：2023年4月21日摘要邮件是日常生活中的一种通讯工具，但垃圾邮件对用户造成严重困扰，因此改进垃圾邮件识别技术、提升其准确率和效率具有重要现实意义。

在文本分类领域，深度学习有很好的应用效果。

故文章提出了一种基于CNN 的BiGRU-Attention 模型，旨在充分利用CNN 的特征提取能力和BiGRU 的全局特征提取能力。

引入注意力机制能够突出显示重要文本，前后共经过两层双向门控循环单元，从而更全面地提取邮件文本特征。

实验数据选取Trec06c 数据集，并与其他分类模型对比，结果表明，检测准确率达到91.56%。

关键词垃圾邮件，文本分类，深度学习，双向门控循环单元，注意力机制Spam Detection Based on Deep LearningYingmei Yu, Suping Yu, Wujun Xu, Hong FanCollege of Information Science and Technology, Donghua University, ShanghaiReceived: Mar. 17th , 2023; accepted: Apr. 14th , 2023; published: Apr. 21st, 2023AbstractEmail is a communication tool in daily life, but spam has caused serious problems for users, As a re-sult, it is crucial to improve spam identification technology and improve its accuracy and efficien-cy. In the field of text classification, deep learning has a good application effect. In order to fully util-ize CNN’s feature extraction capabilities and BiGRU’s global feature extraction capabilities, this ar-ticle suggests a CNN-based BiGRU-Attention model. The introduction of the attention mechanism can highlight important text, which passes through two layers of two-way gated loop units before and after, so as to extract more comprehensive features of email text. The experimental data is selected from Trec06c dataset and compared with other classification models. The results show that the de-tection accuracy reaches 91.56%.俞荧妹等KeywordsSpam, Text Classification, Deep Learning, BiGRU, Attention MechanismCopyright © 2023 by author(s) and Hans Publishers Inc.This work is licensed under the Creative Commons Attribution International License (CC BY 4.0)./licenses/by/4.0/1. 引言当今社会，互联网的快速发展使得电子邮件在人们的日常生活中发挥了很大的功能，既可以提高工作效率、节约成本，又可以促进人们之间的交流和沟通。

环保智能垃圾桶作文英语

Environmental consciousness has become a significant aspect of modern society, and the development of smart technology has paved the way for innovative solutions to waste management.An environmentally friendly smart trash can is one such solution that integrates technology with the goal of reducing pollution and promoting sustainability.Design and FunctionalitySmart trash cans are designed with features that make them stand out from traditional bins.They often have a sleek and modern appearance,which can be an aesthetic addition to any urban or residential setting.The primary functionality of these bins includes:1.Automatic Opening and Closing:Motion sensors or foot pedals allow the lid to open automatically,reducing the need for physical contact and improving hygiene.2.Solar Power:Many smart trash cans are powered by solar panels,making them energyefficient and reducing reliance on nonrenewable energy sources.pactors:Some models have builtin compactors that compress the trash,allowing for more waste to be stored in a smaller space and reducing the frequency of emptying the bin.4.Overflow Sensors:These sensors alert when the bin is full,ensuring timely collection and preventing littering around the bin.Environmental BenefitsThe integration of smart technology in waste management offers several environmental advantages:1.Waste Reduction:By compacting trash,smart bins can hold more waste,reducing the number of times a bin needs to be emptied and the amount of waste that ends up in landfills.2.Energy Efficiency:The use of solar power for operation reduces the carbon footprint associated with waste management.3.Hygiene:Automatic opening mechanisms minimize human contact with waste, reducing the spread of germs and improving public health.4.Data Collection:Some smart bins are equipped with sensors that can collect data on waste levels and types,providing valuable insights for waste management strategies.Community EngagementSmart trash cans can also play a role in community engagement and education about environmental issues:1.Interactive Displays:Some models feature screens that provide information on recycling,waste reduction tips,and local environmental initiatives.2.Reward Systems:Integration with mobile apps can reward users for proper waste disposal,encouraging more responsible behavior.munity Alerts:In case of emergencies or special waste collection events,smart bins can serve as communication hubs to inform the public.Challenges and ConsiderationsDespite the benefits,there are challenges associated with the implementation of smart trash cans:1.Cost:The initial investment for smart bins can be high compared to traditional bins, which may be a barrier for some municipalities or organizations.2.Maintenance:The technology within smart bins requires regular maintenance and updates,which can add to the operational costs.3.Privacy Concerns:The use of sensors and data collection raises questions about privacy,especially if the technology includes video surveillance.ConclusionThe advent of environmentally friendly smart trash cans represents a step forward in integrating technology with environmental stewardship.While there are challenges to overcome,the potential benefits in terms of waste reduction,energy efficiency,and community engagement make them a promising addition to the urban landscape.As technology continues to advance,it is likely that we will see further innovations in this area,making waste management smarter and more sustainable.。

南京理工大学网络空间安全学院预推免题

南京理工大学网络空间安全学院预推免题
考生注意：所有答案（包括填空题）按试题顺序写在答题纸上，写在试卷上不给分。

1.操作系统的特征主要有：
2.操作系统的功能主要有：
3.进程的三种基本状态是：
4.进程同步机制应遵循的准则是：
5.优先权调度算法的类型有：
6.产生死锁的必要条件有：
7.程序的链接方法有以下三种：
8.操作系统在设备分配时，考虑的因素主要有：
9.文件系统主要有：
10.对文件的操作主要有：
11.主目录在树形目录结构中，作为树的节点，称为
12.影响文件安全性的主要因素有：
13.在为文件分配外空间时，所要考虑的问题主要有：
15.在UNIX系统中，管道可分为：。

2022年西安交通大学计算机科学与技术专业《操作系统》科目期末试卷B(有答案)

2022年西安交通大学计算机科学与技术专业《操作系统》科目期末试卷B（有答案）一、选择题1、采用直接存取法来读写磁盘上的物理记求时，效率最高的是（）A.连续结构的文件B.索引结构的文件C.链接结构文件D.其他结构文件2、若文件f1的硬链接为f2，两个进程分别打开fl和f2，获得对应的文件描述符为fd1和fd2，则下列叙述中，止确的是（）I.fl和f2的读写指针位置保持相同II.fl和f2共享同个内存索引节点III.fdl 和fd2分别指向各自的用户打开文件表中的一项，A.仅IIB. 仅II、IIIC.仪I、IID. I、II和II3、下列关于线程的叙述中，正确的是（）。

I.在采用轮转调度算法时，一进程拥有10个用户级线程，则在系统调度执行时间上占用10个时间片II.属于同·个进程的各个线程共享栈空间III.同一进程中的线程可以并发执行，但不同进程内的线程不可以并发执行IV.线程的切换，不会引起进程的切换A. 仅I、II、IIIB. 仅II、IVC.仅II、IIID.全错4、若系统S1采用死锁避免方法，S2采用死锁检测方法。

下列叙述中，正确的是（）。

I.S1会限制用户申请资源的顺序，而S2不会II.S1需要进程运行所需资源总最信息，而S2不需要III.SI不会给可能导致死锁的进程分配资源，而S2会A.仅I、IIB.仅II、IIIC. 仅I、IID. I、II、III5、某系统中有11台打印机，N个进程共享打印机资源，每个进程要求3台打印机。

当N的取值不超过（）时，系统不会发生死锁。

A.4B.5C.6D.76、设有一页式存储管理系统，向用户提供的逻辑地址空间最大为16页，每页2048B，内存总共有8个存储块，试问逻辑地址至少为多少位？内存空间有多大（）？A.逻辑地址至少为12位，内存空间有32KBB.逻辑地址至少为12位，内存空间有16KBC.逻辑地址至少为15位，内存空间有32KBD.逻辑地址至少为15位，内存空间有16KB7、在下述存储管理方案中，（）管理方式要求作业占用连续的存储空间。

上海交通大学试卷

上海交通大学试卷操作系统年月日姓名学号班级得分一、选择题：每题只选一个用字母表示的答案1．根据作业在本次分配到的内存起始地址，将可执行目标代码装到指定的内存地址中，并修改有关地址部分的值的方法称为B方式。

A)固定定位B) 静态重定位C) 动态重定位D) 单一重定位2．有9条磁带机供4个进程使用，如每个进程最多同时分配C条磁带机，就没有死锁的危险。

A) 1 B) 2 C) 3 D) 4二、填充题1．在进程主要状态转换图中，①表示____就绪__________状态。

2．写出正则表达式([^(+)]*) 对应字符串((first)(a+b)(second(a-b))) 的匹配部分：___(first)、(a-b) _________________三、简答题1．写出进程（不支持线程）的定义。

进程是程序处于一个执行环境中在一个数据集上的运行过程，它是系统进行资源分配和调度的一个可并发执行的独立单位。

2．简述可变分区存储管理算法中的首次适应算法（包括分配和释放算法，注：可拆成2题）。

（一）分配算法采用首次适应法为作业分配大小为size的内存空间时，总是从表的始端的低地址部分开始查找，当第一次找到大于或等于申请大小的空闲区时，就按所需大小分配给作业。

如果分配后原空闲区还有剩余空间，就修改原存储区表项的m_size和m_addr，使它记录余下的“零头”。

如果作业所需空间正好等于该空闲区大小，那么该空闲区表项的m_size就成为0，接下来要删除表中这个“空洞”。

（二）回收算法释放区与原空闲区相邻情况可归纳为四种情况。

（1）仅与前空闲区相连：合并前空闲区和释放区，该空闲区的m_addr仍为原前空闲区的首地址，修改表项的长度域m_size为原m_size与释放区长度之和。

（2）与前空闲区和后空闲区都相连：将三块空闲区合并成一块空闲区。

修改空闲区表中前空闲区表项，其始地址为原前空闲区始址，其大小m_size等于三个空闲区长度之和，这块大的空闲区由前空闲区表项登记。