graph_only_prompts_examples.py

system_message = """
      You are an AI visual assistant that can analyze a graph in a scientific paper. You are provided with the OCR-extracted text, the caption of the figure, and the first paragraph that mentioned the figure.

      Your task is to use information from all these provided sources to create a plausible question about the graph, and then provide a detailed answer.

      You should aim to ask complex questions that go beyond a simple description of the graph. The answer to such questions should require understanding the graph data, and then reasoning based on background knowledge or interpretation. You should aim to provide guides and scholarly perspectives based on the graph's data and context.

      Avoid directly revealing specific details in the question. Make the question challenging in a way that the user needs to first reason about the graph data and the context they have derived from the accompanying paper.

      Instead of referring to specific labels or points in the graph while asking a question or giving an answer, explain the graph using natural language. Include details like data trends, prominent points, and relationships between different data sets visualized in the graph.

      When using the information from the OCR-extracted text, caption and first paragraph to explain the graph, avoid stating that these are your sources. Always answer as if you are directly interpreting the graph according to your AI comprehension, understanding and reasoning. Regarding the format of the given input: the following context described the image:  "from": "human", "value": contains OCR extracted text listed, and 'Figure (integer) caption of the image'."from: gpt value: contains the first paragraph in the text that mentioned the figure. Many times the OCR is missing simply because the image does not contain text. 

"""
example_1_q = """
[{'from': 'human',
  'value': "OCR extracted text list, separated by ', ' : 0.8I-R >0.750.70.650.50.60.70.80.91Source variance (y)Mean target risk (RT), 0.8, I-R, >, 0.75, 0.7, 0.65, 0.5, 0.6, 0.7, 0.8, 0.9, 1, Source, variance, (y), Mean, target, risk, (RT) Fig. 6. Mean target risks for the synthetic problem setting, as a function of source variance γ.<image>\nAnalyze the image in a comprehensive and detailed manner"},
 {'from': 'gpt',
  'value': 'Weights are estimated by fitting a normal distribution to data from each domain, and computing the ratio of the target probability over the source probability of each source data point ŵ(xi) = p̂T (xi)/p̂S(xi). We compare the importanceweighted risk estimator (R̂ŵ) with its control variate counterpart (R̂β̂). We average their estimated risks over all data sets and specifically over the 10% of data sets with the largest weight variance (indicated with ”>” in the legend of Figures 6, 7 and 8). We also include validation on the labeled target samples (R̂T ) as the oracle solution. After risk estimation, the λ is selected that minimized risk. The classifier is then retrained using all source data and the selected λ, and evaluated using the target risk based on the true target labels as the final measure. This process is repeated for each data set and we report the final average as R̄T .'}]
"""
example_1_a = """
Question: Considering the graph structure, can you elaborate on how the lines, axes and legend provide a comprehensive perspective of the interplay between source variance and mean target risk?

Answer: The graph provides a visual representation of data, making complex data readable and meaningful. It consists of two main axes, the horizontal and the vertical, showcasing source variance and mean target risk, respectively.The horizontal (x-axis) represents source variance, which shows the degree of variability within the source data. The vertical (y-axis) corresponds to the mean target risk, which reflects the mean risk in the target data observing all source data points and their related probabilities.Lines on the graph portray the relationship between these two parameters. A line ascending from left to right may indicate a positive correlation between the source variance and the mean target risk. This means as the source variance increases, mean target risk also increases, signifying a direct proportionality between the two of them.The legend is a key aspect of the graph as it provides identification for each line or data set represented. If multiple lines or data sets appear in the graph, each would be differentiated by colors, patterns or symbols and accurately recognized via the legend. Similarly, the ">" indicator, as described in the paragraph, denotes the 10% of datasets with the largest weight variance. So, the axes, lines, and legends combine to deliver a comprehensive picture of the specific relationship under study. The graph is an intuitive way to understand this relationship and how one parameter changes relative to another. Such a representation encourages easy data interpretation and helps drive meaningful insights.

Question: How does the graph and its depiction of source variance and mean target risk factor into the process of classifier evaluation using the target risk based on the true target labels?

Answer: The graph represents the relationship between source variance and mean target risk and is instrumental in understanding the process of classifier evaluation and adjustment.Once a classifier is trained using all source data and a selected λ, it's evaluated using the target risk based on the true target labels. This risk can directly be linked to what is shown in the graph.As the graph suggests, an increase in source variance could potentially lead to an increase in mean target risk. If a classifier is trained using source data with a high variance, the target risk evaluated by the true labels might be high, indicating that the classifier might not be as accurate or reliable. In contrast, if the source variance is low implying less spread in the source data, the mean target risk could be low, which could suggest better performance of the classifier. Low mean target risk denotes that the classifier correctly predicts the label for target data most of the time. Through constant evaluation, adjustments on the λ and subsequent retraining, we aim to minimize the target risk and improve the performance of the classifier. The depicted relationship in the graph, therefore, greatly assists in this iterative process of classifier improvement.
"""
example_2_q = """
[ { "from": "human", "value": "OCR extracted text list, separated by ', ' : 109令GG487中一ロー G日o00d4Total Power constrainte - individual power constraint, SDR approachA individual power constraint, SOCP approch3individual power constraint, suboptimal25101520P,(dB)secrecy rate ((bits/symbol), 10, 9, 令, GG4, 8, 7, 中, 一, ロ, ー, G, 日, o00d, 4, Total, Power, constraint, e, -, individual, power, constraint,, SDR, approach, A, individual, power, constraint,, SOCP, approch, 3, individual, power, constraint,, suboptimal, 2, 5, 10, 15, 20, P,(dB), s, ecrecy, rate, (, (bits/symbol) Fig. 3. DF Second-hop secrecy rate vs. the total relay transmit powerPT for different cases. Eavesdropper has a stronger channel.Examine the image closely and share its details\n<image>" }, { "from": "gpt", "value": "In Figures 2 and 3, we plot the second-hop secrecy rate, which is the maximum secrecy rate that our collaborative relay beamforming system can support under both total and individual relay power constraints. For the case of individual relay power constraints, we assume that the relays have the same power budgets: pi = PT M . Specifically, in Fig. 2, we have σh = 3, σz = 1, N0 = 1 and M = 5. In this case, the legitimate user has a stronger channel. In Fig. 3, the only changes are σh = 1 and σz = 2, which imply that the eavesdropper has a stronger channel. Our CRB system can achieve secure transmission even when the eavesdropper has more favorable channel conditions. As can be seen from the figures, the highest secrecy rate is achieved, as expected, under a total transmit power constraint. On the other hand, we observe that only a relatively small rate loss is experienced under individual relay power constraints. Moreover, we note that our two different optimization approaches give nearly the same result. It also can be seen that under individual power constraint, the simple suboptimal method suffers a constant loss as compared to SDR or SOCP based optimal value." } ]"""

example_2_a = """
Question: Can the collaborative relay beamforming system still support secure transmission if an eavesdropper has a stronger channel? What does the graph imply about this aspect?
Answer: Yes, the graph implies that the collaborative relay beamforming (CRB) system can still support secure transmission even when the eavesdropper has a stronger channel. This can be seen from the data trends, where even with an increase in total relay transmit power (PT), the system is able to maintain a reasonable level of secrecy rate. This demonstrates the robustness of the CRB system in maintaining secure communications even under unfavorable channel conditions.

Question: How does the total transmit power constraint compare to individual power constraints in terms of secrecy rate? What conclusions can be drawn from the graph regarding this?
Answer: The graph illustrates that the highest secrecy rate is achieved under a total transmit power constraint as compared to individual power constraints. This shows that when the total power that can be transmitted is constrained, the system is better able to maintain a high level of secrecy rate. However, even under individual relay power constraints, only a relatively small rate loss is experienced. Thus, while total power constraints result in optimal secrecy rates, individual power constraints are still effective and yield respectable secrecy rates.

Question: What observations can be made about the performance of different optimization approaches in maintaining the secrecy rate, as indicated by the graph?
Answer: From the graph, it appears that two different optimization approaches - the SDR and SOCP - yield nearly the same results in terms of the secrecy rate. This suggests that both methods are comparably effective in optimizing the performance of the system. Interestingly, a simpler, suboptimal method also appears in the graph. Although this method shows a constant loss as compared to the SDR and SOCP based optimal value, it still sustains a consistent secrecy rate, indicating that even sub-optimal methods can provide a certain level of security.

Question: According to trends depicted on the graph, what happens to the secrecy rate as the total relay transmit power increases? What might this imply about the relationship between these two parameters?
Answer: The graph implies that as the total relay transmit power (PT) increases, the secrecy rate generally increases. This suggests a positive correlation between these two parameters, which means that increasing the total transmit power of a relay tends to result in a higher level of security. This might be due to the fact that with greater transmit power, the system is better able to overcome the strength of the eavesdropper's channel, thereby ensuring secure transmission."""

examples = [example_1_q, example_1_a, example_2_q, example_2_a]