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... | This technical diagram illustrates a hierarchical neural network architecture that processes an input $S$ of size $H \times W \times 3$ through a series of sequential stages. The flow begins with a gray-boxed initial stage containing "Patch Partition," "Linear Embedding," and "Swin Transformer Block," followed by three... | vfig_diagrams |
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text { font-f... | This diagram illustrates a sequential machine learning process involving two parallel model paths, labeled "Pre-trained model (teacher)" on the left and "Pre-trained model (student)" on the right. The workflow is divided into three vertical stages: "Initial task," "Knowledge distillation," and "Subsequent tasks." Solid... | vfig_diagrams |
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<marker id="arrowRed" markerWidth="10" mark... | This diagram illustrates a machine learning pipeline, labeled "Training/Validation Phase," which processes input shapes through a series of 2D-Convolution and Max-pooling layers. The data is then passed through a "Flatten" layer into a Feedforward Neural Network (FNN) to produce an "Output." A separate "Testing/Predict... | vfig_diagrams |
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<marker id="arrow-fill-black... | This technical diagram illustrates a multi-level system architecture divided into four horizontal layers labeled "Lvl1: MS Req," "Lvl2: Swap Type," "Lvl3: MP Bitmap," and "Lvl4: Req State." The top layer features an LRU block, request queues, and a tree structure connected to a list of CPU nodes (cpu0 through cpu15). T... | vfig_diagrams |
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<marker id="arrowhead-start" markerWidth="... | This diagram illustrates a blockchain transaction workflow involving four main components: Client/SDK, Membership, Peer, and Orderer. The process begins with "0. Enroll" from the Membership service, followed by "1. Proposal" and "2. Submit Transaction" from the Client/SDK to the Peer. The Peer then performs "3. Relay" ... | vfig_diagrams |
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<mark... | This diagram illustrates a framework for time series analysis using an LLM, featuring three types of hints: (i) Symbolic Numerical Hint, (ii) Context-Integrated Step-aligned hint, and (iii) Task-prior Hint. These hints are tokenized and fed into an LLM alongside an "LLM Input Template" and an "Arbitrary Question" to ge... | vfig_diagrams |
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<marker id="arrow-red" m... | This image compares two approaches for calculating similarity: "A. Coarse-level similarity" and "B. Token-level similarity (ours)." In both diagrams, input tokens $Q_{2d}k$ and $Q_{3d}k$ are mapped to a grid of nodes representing labels like "solubility," "high," "1-C," and "[cls]." While the coarse-level approach uses... | vfig_diagrams |
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font-fam... | This flowchart illustrates a technical process for evaluating scene configurations using a classifier. The diagram begins with a horizontal flow from "Current Scene" to "Scene Image" and then to "Candidates' Configurations." From "Candidates' Configurations," the process moves vertically downward to "Candidates' Images... | vfig_diagrams |
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... | This BPMN process diagram begins with a start event leading to a "Loan Requested" task, which then splits via an exclusive gateway into two parallel paths, each with a 50% probability. The top path leads to a "Manual Review" task (50 mins), while the bottom path leads to an "Auto Review" task (30 mins). Both paths conv... | vfig_diagrams |
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text { fo... | This diagram illustrates three different strategies for tensor contraction, labeled (a) Reconstruction contraction, (b) MAC-optimal contraction, and (c) Latency-optimal contraction. A legend at the top defines the node types: blue circles represent "Input/Output Tensor" (labeled X and Y), green circles represent "Weigh... | vfig_diagrams |
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... | This diagram displays a horizontal flow chart consisting of three rounded rectangular nodes connected by two directional arrows. The first node on the left is labeled "Unreliable Environment," which points to the central node labeled "Parameter Perturbations" via an arrow containing the text "System Design." The centra... | vfig_diagrams |
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... | This diagram illustrates a convolutional neural network operation where input feature maps (labeled $T_n$) are processed through a series of multipliers ($w$) and adders. The input maps are connected to a processing block containing four multipliers and three adders arranged in a tree structure. The final output consis... | vfig_diagrams |
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... | The image illustrates a data sequence divided into two distinct segments: "Context Length" and "Forecast Length." The "Context Length" is represented by a blue curve with discrete data points, which is further subdivided into smaller intervals labeled as "Patch Length." To the right, a pink curve extends from a moon an... | vfig_diagrams |
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<feTu... | This diagram, titled "The Futures Cone" by Joseph Voros (2000), illustrates a range of future possibilities radiating from a single point labeled "Now." The cone expands over time, with nested regions representing different levels of certainty, labeled "Probable," "Preferable," "Plausible," and "Possible." The visual s... | vfig_diagrams |
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<marker id="arrow-start" markerWidth="10... | This technical diagram illustrates a machine learning architecture divided into an "Ingredient Representation Component" on the left and a "Pairing Score Prediction Component" on the right. The left component processes "Gin" and "Tonic Water" through identical MLP blocks that share weights, resulting in two sets of out... | vfig_diagrams |
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.node-box { fill: white; stroke: black;... | This diagram is a hierarchical flow chart illustrating the division of a dataset starting with a total of N=88. The top node splits into two primary branches labeled "Train" (N=58) and "Test" (N=30). Each of these branches further subdivides into two sub-categories labeled "LVO" and "NL+SM," resulting in four distinct ... | vfig_diagrams |
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<marker id="arrow-gr... | This image presents three architectural diagrams labeled a, b, and c. Diagram (a) shows a recurrent process where $x_t$ and $s_{t-1}$ interact with weight matrices $W^O$ and $W^R$ to produce $s_t$ and $y_t = s_t$. Diagram (b) illustrates a dynamic update mechanism where $x_t$ is transformed by $W^K, W^V,$ and $W^Q$ to ... | vfig_diagrams |
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font... | This diagram illustrates a neural network architecture designed to output a "Quality" score based on input data. On the far left, a boxed section displays two U-shaped curves labeled "Reference" and "Model," which feed into a "Concat." block. The network then processes this concatenated input through a sequential serie... | vfig_diagrams |
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... | The diagram illustrates a hierarchical tuning process, beginning with a small tree structure at the top left that maps "Root N0" to configurations N1 through N4. A large, dashed-border box on the left contains detailed metadata for "Configuration N0," including fields for "Resource," "Digest," and "Workload." Arrows co... | vfig_diagrams |
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<marker id="arrow-start" markerWidth... | This diagram illustrates a premixing system consisting of a Combustor Plenum, a Premixing Tube, and a Combustion Chamber. An Injection Lance equipped with Vortex Generators and Injection Holes ($N_H$) is positioned within the Premixing Tube, which has a length $L_M$ and diameter $D_M$. Key geometric parameters are labe... | vfig_diagrams |
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text { font-family: Aria... | This diagram illustrates a data flow process involving a patient, a smartphone, a server, and a doctor. On the left, a human figure with red dots representing "Sensor" locations sends information to a "Smart phone" displaying a bar graph. An arrow labeled "Sensory Data" connects the smartphone to a large cloud shape la... | vfig_diagrams |
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</patte... | This diagram illustrates a workflow involving several interconnected nodes. An "Array Training" node connects to an "Encoding VM" via a thick orange arrow and to a "Tabular Index" node, which leads to "Staging." A "Mean Iterate" node is linked to a "Detector" node via a purple dashed arrow, while a blue dashed line cur... | vfig_shapes |
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<marker id="arrow-thick... | This technical diagram illustrates a dual-stream architecture designed for multi-modal semantic alignment. The left and right branches each process inputs through "Text Embedder" and "Image Embedder" modules, which feed into a series of "DiT Block" layers. A central "Pretrained Multi-Modal Encoder" processes "Caption" ... | vfig_diagrams |
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