unstructuredio/SCORE-Bench · Datasets at Hugging Face

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*Funding total may increase depending on A-Posteriori funding commitment and additional commercialization subtask. More information may be needed.
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Description
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The electric sector is undergoing rapid changes, growing in complexity, and continues to be extremely vulnerable to cyber-attacks, physical incidents, and existential threats. Last year, President Biden signed into law H.R.7535, the Quantum Computing Cybersecurity Preparedness Act,⁵ which encourages federal agencies to prepare for a quantum computing threat that could break today’s encryption keys. Adding to the complexity, new business models are emerging as larger portions of the economy, such as transportation, are electrified and intermittent resources and new energy storage solutions are developed and incorporated into the electric grid. Quantum Information Science (QIS) however does not only serve as a threat, but can also play a part in addressing both the grid’s vulnerabilities and the grid’s increasing complexity as the grid evolves to meet changing requirements and goals in the energy sector.
This qFACSS Topic Area will explore the following areas:
1. Quantum computing for optimization and contingency analysis;
2. Quantum key distribution (QKD) and post quantum cryptography (PQC) for grid
cybersecurity; and
3. Quantum sensing for grid timing and synchronization with DERs (redundancy, GPS synchronization, or GPS replacement), grid anomaly detection, positioning, navigation, and timing for mobile storage (EVs with vehicle-to-everything capability), CO₂ management (e.g. pipeline or generation plant leakage detection, hydrogen leakage detection, and CO₂ sequestration and CO₂ storage), and geothermal detection/imaging.
This Topic Area will involve a lab data call to inventory currently developed QIS technologies at the labs that could be leveraged for the grid as outlined above and described in more detail below.
In parallel, This Topic Area will facilitate a Request for Information (RFI), open to both industry and lab input, to gain a holistic understanding of the landscape of QIS technologies qualified will help inform ongoing partnerships and future RDD&D funding opportunities as well as this Lab Call Topic Area if timely. Meanwhile, relevant and discrete projects under the DOE V2X MO⁶
⁵ H.R.7535 – Quantum Computing Cybersecurity Preparedness Act \| U.S. Congress
⁶ Vehicle-to-Everything (V2X) Memorandum of Understanding (MOU) \| U.S. Department of Energy
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Notes to Consolidated Financial Statements (Continued)


Note 18. Fair value measurements
Our financial assets and liabilities are summarized below as of June 30, 2023 and December 31, 2022, with fair values shown according to the fair value hierarchy (in millions). The carrying values of cash and cash equivalents, U.S. Treasury Bills, other receivables and accounts payable, accruals and other liabilities are considered to be reasonable estimates of or otherwise approximate the fair values.


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Carrying Value Fair Value Quoted Prices (Level 1) Significant Other Observable Inputs (Level 2) Significant Unobservable Inputs (Level 3)
June 30, 2023
Investments in fixed maturity securities:
U.S. Treasury, U.S. government corporations and agencies $ 9,052 $ 9,052 $ 9,017 $ 35 $ —
Foreign governments 11,481 11,481 11,170 311 —
Corporate bonds 1,554 1,554 — 908 646
Other 266 266 — 266 —
Investments in equity securities 353,409 353,409 342,596 11 10,802
Investments in Kraft Heinz & Occidental common stock 27,060 24,732 24,732 — —
Loans and finance receivables 23,530 24,020 — 1,097 22,923
Derivative contract assets ⁽¹⁾ 355 355 74 256 25
Derivative contract liabilities ⁽¹⁾ 309 309 48 73 188
Notes payable and other borrowings:
Insurance and other 41,389 36,100 — 36,071 29
Railroad, utilities and energy 83,958 77,040 — 77,040 —
December 31, 2022
Investments in fixed maturity securities:
U.S. Treasury, U.S. government corporations and agencies $ 9,802 $ 9,802 $ 9,733 $ 69 $ —
Foreign governments 10,327 10,327 9,854 473 —
Corporate bonds 2,195 2,195 — 1,546 649
Other 2,804 2,804 — 2,804 —
Investments in equity securities 308,793 308,793 296,610 9 12,174
Investments in Kraft Heinz & Occidental common stock 24,421 25,491 25,491 — —
Loans and finance receivables 23,208 23,428 — 1,513 21,915
Derivative contract assets ⁽¹⁾ 589 589 56 474 59
Derivative contract liabilities ⁽¹⁾ 242 242 8 122 112
Notes payable and other borrowings:
Insurance and other 46,538 41,961 — 41,061 900
Railroad, utilities and energy 76,206 67,651 — 67,651 —
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⁽¹⁾ Assets are included in other assets and liabilities are included in accounts payable, accruals and other liabilities.
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Table 3: Ablation study: FID on COCO-30K validation set on 256 × 256 resolution.
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Setup FID-30K CLIP
Diffusion prior with quantization 9.86 0.287
Diffusion prior w/o quantization 9.87 0.286
Linear prior 8.03 0.261
Residual prior 8.61 0.249
No prior 25.92 0.256
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et al., 2022) with minor modifications. We trained this autoencoder on the LAION HighRes dataset (Schuhmann et al., 2022), obtaining the SotA results in image reconstruction. We released the weights and code for these models under an open source licence¹¹. The comparison of our autoencoder with competitors can be found in Table 4.

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*Funding total may increase depending on A-Posteriori funding commitment and additional commercialization subtask. More information may be needed.

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Description

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The electric sector is undergoing rapid changes, growing in complexity, and continues to be extremely vulnerable to cyber-attacks, physical incidents, and existential threats. Last year, President Biden signed into law H.R.7535, the Quantum Computing Cybersecurity Preparedness Act,⁵ which encourages federal agencies to prepare for a quantum computing threat that could break today’s encryption keys. Adding to the complexity, new business models are emerging as larger portions of the economy, such as transportation, are electrified and intermittent resources and new energy storage solutions are developed and incorporated into the electric grid. Quantum Information Science (QIS) however does not only serve as a threat, but can also play a part in addressing both the grid’s vulnerabilities and the grid’s increasing complexity as the grid evolves to meet changing requirements and goals in the energy sector.

This qFACSS Topic Area will explore the following areas:

1. Quantum computing for optimization and contingency analysis;

2. Quantum key distribution (QKD) and post quantum cryptography (PQC) for grid

cybersecurity; and

3. Quantum sensing for grid timing and synchronization with DERs (redundancy, GPS synchronization, or GPS replacement), grid anomaly detection, positioning, navigation, and timing for mobile storage (EVs with vehicle-to-everything capability), CO₂ management (e.g. pipeline or generation plant leakage detection, hydrogen leakage detection, and CO₂ sequestration and CO₂ storage), and geothermal detection/imaging.

This Topic Area will involve a lab data call to inventory currently developed QIS technologies at the labs that could be leveraged for the grid as outlined above and described in more detail below.

In parallel, This Topic Area will facilitate a Request for Information (RFI), open to both industry and lab input, to gain a holistic understanding of the landscape of QIS technologies qualified will help inform ongoing partnerships and future RDD&D funding opportunities as well as this Lab Call Topic Area if timely. Meanwhile, relevant and discrete projects under the DOE V2X MO⁶

⁵ H.R.7535 – Quantum Computing Cybersecurity Preparedness Act | U.S. Congress

⁶ Vehicle-to-Everything (V2X) Memorandum of Understanding (MOU) | U.S. Department of Energy

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Notes to Consolidated Financial Statements (Continued)

Note 18. Fair value measurements

Our financial assets and liabilities are summarized below as of June 30, 2023 and December 31, 2022, with fair values shown according to the fair value hierarchy (in millions). The carrying values of cash and cash equivalents, U.S. Treasury Bills, other receivables and accounts payable, accruals and other liabilities are considered to be reasonable estimates of or otherwise approximate the fair values.

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Carrying Value Fair Value Quoted Prices (Level 1) Significant Other Observable Inputs (Level 2) Significant Unobservable Inputs (Level 3)

June 30, 2023

Investments in fixed maturity securities:

U.S. Treasury, U.S. government corporations and agencies $ 9,052 $ 9,052 $ 9,017 $ 35 $ —

Foreign governments 11,481 11,481 11,170 311 —

Corporate bonds 1,554 1,554 — 908 646

Other 266 266 — 266 —

Investments in equity securities 353,409 353,409 342,596 11 10,802

Investments in Kraft Heinz & Occidental common stock 27,060 24,732 24,732 — —

Loans and finance receivables 23,530 24,020 — 1,097 22,923

Derivative contract assets ⁽¹⁾ 355 355 74 256 25

Derivative contract liabilities ⁽¹⁾ 309 309 48 73 188

Notes payable and other borrowings:

Insurance and other 41,389 36,100 — 36,071 29

Railroad, utilities and energy 83,958 77,040 — 77,040 —

December 31, 2022

Investments in fixed maturity securities:

U.S. Treasury, U.S. government corporations and agencies $ 9,802 $ 9,802 $ 9,733 $ 69 $ —

Foreign governments 10,327 10,327 9,854 473 —

Corporate bonds 2,195 2,195 — 1,546 649

Other 2,804 2,804 — 2,804 —

Investments in equity securities 308,793 308,793 296,610 9 12,174

Investments in Kraft Heinz & Occidental common stock 24,421 25,491 25,491 — —

Loans and finance receivables 23,208 23,428 — 1,513 21,915

Derivative contract assets ⁽¹⁾ 589 589 56 474 59

Derivative contract liabilities ⁽¹⁾ 242 242 8 122 112

Notes payable and other borrowings:

Insurance and other 46,538 41,961 — 41,061 900

Railroad, utilities and energy 76,206 67,651 — 67,651 —

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⁽¹⁾ Assets are included in other assets and liabilities are included in accounts payable, accruals and other liabilities.

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23

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Table 3: Ablation study: FID on COCO-30K validation set on 256 × 256 resolution.

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Setup FID-30K CLIP

Diffusion prior with quantization 9.86 0.287

Diffusion prior w/o quantization 9.87 0.286

Linear prior 8.03 0.261

Residual prior 8.61 0.249

No prior 25.92 0.256

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et al., 2022) with minor modifications. We trained this autoencoder on the LAION HighRes dataset (Schuhmann et al., 2022), obtaining the SotA results in image reconstruction. We released the weights and code for these models under an open source licence¹¹. The comparison of our autoencoder with competitors can be found in Table 4.