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data/retrieval_individual-b9229914-47bc-4da8-a21b-89329fff8207.jsonl
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{"tstamp": 1722305252.6945, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722305252.201, "finish": 1722305252.6945, "ip": "", "conv_id": "2766c8817d564f3fa47a1aa8eeaa0b03", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "mass effect 1 how many skill points total?", "output": [["mass effect 1 how many skill points total?", "Gaming Stackexchange\n\nQ: How many skill points will I have at max level? What is the maximum level in Mass Effect 3?\nHow many skill points will Shepard have at max level? \nIf NPC companions earn a different number of skill points than Shepard, what's the number they'll have at max level?\n\nA: Since this question seems to have been deemed the Canonical by mods:\nThe maximum character level is 60. At this level, Shepard will have earned a total of 181 Skill Points.\nMaxing a single skill requires 21 points.\nThere are enough points in the game to max out a total of 8 skills, with a few left over.\nShepard, conveniently enough, has 8 skills, plus one slot for a Bonus Power.\nSo, you can come very close to maxing out all skills, but you can't quite make it to maxing out all 9. You can max out 8 of them, and have 13 points remaining for the 9th, which lets you get to rank 4. You'll have 2 points left over. Alternately, you could max out 7 skills, get two to rank 5, and have 3 points left over.\n"]], "corpus": "stackexchange"}
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{"tstamp": 1722305639.8104, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722305639.6393, "finish": 1722305639.8104, "ip": "", "conv_id": "bc1a7d6500334478abf4839584bbeaec", "model_name": "GritLM/GritLM-7B", "prompt": "How does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?", "output": [["How does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?", "Title: Brain Network Transformer\n\nAbstract: Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transformer-based models have been studied over different types of data, including graphs, shown to bring performance gains widely. In this work, we study Transformer-based models for brain network analysis. Driven by the unique properties of data, we model brain networks as graphs with nodes of fixed size and order, which allows us to (1) use connection profiles as node features to provide natural and low-cost positional information and (2) learn pair-wise connection strengths among ROIs with efficient attention weights across individuals that are predictive towards downstream analysis tasks. Moreover, we propose an Orthonormal Clustering Readout operation based on self-supervised soft clustering and orthonormal projection. This design accounts for the underlying functional modules that determine similar behaviors among groups of ROIs, leading to distinguishable cluster-aware node embeddings and informative graph embeddings. Finally, we re-standardize the evaluation pipeline on the only one publicly available large-scale brain network dataset of ABIDE, to enable meaningful comparison of different models. Experiment results show clear improvements of our proposed Brain Network Transformer on both the public ABIDE and our restricted ABCD datasets. The implementation is available at https://github.com/Wayfear/BrainNetworkTransformer."]], "corpus": "arxiv"}
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{"tstamp": 1722305639.8104, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722305639.6393, "finish": 1722305639.8104, "ip": "", "conv_id": "0848dc0be9b94d688857e785d82e9f51", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "How does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?", "output": [["How does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?", "Title: Noncompact, Gauge-Invariant Simulations of U(1), SU(2), and SU(3)\n\nAbstract: We have applied a new noncompact, gauge-invariant, Monte Carlo method to simulate the U(1), SU(2), and SU(3) gauge theories on 8^4 and 12^4 lattices. For U(1) the Creutz ratios of the Wilson loops agree with the exact results for beta > 0.5 after a renormalization of the charge. The SU(2) and SU(3) Creutz ratios robustly display quark confinement at beta = 0.5 and beta = 2, respectively. At much weaker coupling, the SU(2) and SU(3) Creutz ratios agree with perturbation theory after a renormalization of the coupling constant. For SU(3) without quarks, our lattice QCD parameter is Lambda_L = 130 +- 18$ MeV."]], "corpus": "arxiv"}
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{"tstamp": 1722305252.6945, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722305252.201, "finish": 1722305252.6945, "ip": "", "conv_id": "2766c8817d564f3fa47a1aa8eeaa0b03", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "mass effect 1 how many skill points total?", "output": [["mass effect 1 how many skill points total?", "Gaming Stackexchange\n\nQ: How many skill points will I have at max level? What is the maximum level in Mass Effect 3?\nHow many skill points will Shepard have at max level? \nIf NPC companions earn a different number of skill points than Shepard, what's the number they'll have at max level?\n\nA: Since this question seems to have been deemed the Canonical by mods:\nThe maximum character level is 60. At this level, Shepard will have earned a total of 181 Skill Points.\nMaxing a single skill requires 21 points.\nThere are enough points in the game to max out a total of 8 skills, with a few left over.\nShepard, conveniently enough, has 8 skills, plus one slot for a Bonus Power.\nSo, you can come very close to maxing out all skills, but you can't quite make it to maxing out all 9. You can max out 8 of them, and have 13 points remaining for the 9th, which lets you get to rank 4. You'll have 2 points left over. Alternately, you could max out 7 skills, get two to rank 5, and have 3 points left over.\n"]], "corpus": "stackexchange"}
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{"tstamp": 1722305639.8104, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722305639.6393, "finish": 1722305639.8104, "ip": "", "conv_id": "bc1a7d6500334478abf4839584bbeaec", "model_name": "GritLM/GritLM-7B", "prompt": "How does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?", "output": [["How does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?", "Title: Brain Network Transformer\n\nAbstract: Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transformer-based models have been studied over different types of data, including graphs, shown to bring performance gains widely. In this work, we study Transformer-based models for brain network analysis. Driven by the unique properties of data, we model brain networks as graphs with nodes of fixed size and order, which allows us to (1) use connection profiles as node features to provide natural and low-cost positional information and (2) learn pair-wise connection strengths among ROIs with efficient attention weights across individuals that are predictive towards downstream analysis tasks. Moreover, we propose an Orthonormal Clustering Readout operation based on self-supervised soft clustering and orthonormal projection. This design accounts for the underlying functional modules that determine similar behaviors among groups of ROIs, leading to distinguishable cluster-aware node embeddings and informative graph embeddings. Finally, we re-standardize the evaluation pipeline on the only one publicly available large-scale brain network dataset of ABIDE, to enable meaningful comparison of different models. Experiment results show clear improvements of our proposed Brain Network Transformer on both the public ABIDE and our restricted ABCD datasets. The implementation is available at https://github.com/Wayfear/BrainNetworkTransformer."]], "corpus": "arxiv"}
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{"tstamp": 1722305639.8104, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722305639.6393, "finish": 1722305639.8104, "ip": "", "conv_id": "0848dc0be9b94d688857e785d82e9f51", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "How does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?", "output": [["How does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?", "Title: Noncompact, Gauge-Invariant Simulations of U(1), SU(2), and SU(3)\n\nAbstract: We have applied a new noncompact, gauge-invariant, Monte Carlo method to simulate the U(1), SU(2), and SU(3) gauge theories on 8^4 and 12^4 lattices. For U(1) the Creutz ratios of the Wilson loops agree with the exact results for beta > 0.5 after a renormalization of the charge. The SU(2) and SU(3) Creutz ratios robustly display quark confinement at beta = 0.5 and beta = 2, respectively. At much weaker coupling, the SU(2) and SU(3) Creutz ratios agree with perturbation theory after a renormalization of the coupling constant. For SU(3) without quarks, our lattice QCD parameter is Lambda_L = 130 +- 18$ MeV."]], "corpus": "arxiv"}
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{"tstamp": 1722305896.3763, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722305894.1942, "finish": 1722305896.3763, "ip": "", "conv_id": "ebb67aaa6367404fada798faa16238be", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "can gravity waves travel faster than light?", "output": [["can gravity waves travel faster than light?", "Physics Stackexchange\n\nQ: Why can't the speed of gravitational waves be greater than the speed of light if the universe can expand faster than the speed of light? Since the expansion speed of the universe can be greater than the speed of light, why can't gravitational waves, which also uses space as the medium, travel faster than the speed of light?\n\nA: Gravitational waves are solutions to the linearized field equations\n$\\Box h_{\\mu \\nu} = 0,$\nwhere $\\Box$ is the d'Alembertian differential operator.\nThey are of the form\n\\begin{align}\nh_{\\mu \\nu}= \\text{Re}[H_{\\mu \\nu} e^{i k_{\\rho} x^{\\rho}}] , \n\\end{align}\nwhere $H_{\\mu \\nu}$ is just a complex symmetric matrix setting the polarization of the wave and $k_{\\rho}$ is its wave-vector.\nFor the above ansatz to satisfy the equations, we plug it into the equations to get the condition $k_{\\mu} k^{\\mu} =0$. This is just the statement that the wave-vector must be null, meaning the wave propagates at the speed of light.\n"]], "corpus": "stackexchange"}
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{"tstamp": 1722305896.3763, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722305894.1942, "finish": 1722305896.3763, "ip": "", "conv_id": "0594d3384acf4283a4656c03b325081a", "model_name": "BM25", "prompt": "can gravity waves travel faster than light?", "output": [["can gravity waves travel faster than light?", "Physics Stackexchange\n\nQ: Why can't fermions be affected by effective gravity in non-linear quantum electrodynamics? Quantum electrodynamics based upon Euler-Heisenberg or Born-Infeld Lagrangians predict photons\nto move according to an effective metric which is dependent on the background electromagnetic\nfield. In other words, photon trajectories are curved in presence of electromagnetic fields,\nmeaning that an effective gravity is acting upon. If part of fermion masses is allegedly of\nelectromagnetic origin, the question why their trajectories are not affected by this\neffective gravity naturally comes to mind.\n\nA: In the presence of a background electromagnetic field, electromagnetic fields travel along a deformed light cone which is smaller than the \"relativistic light cone\". However, charged fermions can still travel faster than electromagnetic waves as long as they are still slower than the \"relativistic speed of light\". They emit Cherenkov radiation while doing so. \n"]], "corpus": "stackexchange"}
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