Natural language processing models are vulnerable to adversarial examples. Previous textual adversarial attacks adopt gradients or confidence scores to calculate word importance ranking and generate adversarial examples. However, this information is unavailable in the real world. Therefore, we focus on a more realistic and challenging setting, named hard-label attack, in which the attacker can only query the model and obtain a discrete prediction label. Existing hard-label attack algorithms tend to initialize adversarial examples by random substitution and then utilize complex heuristic algorithms to optimize the adversarial perturbation. These methods require a lot of model queries and the attack success rate is restricted by adversary initialization. In this paper, we propose a novel hard-label attack algorithm named LimeAttack, which leverages a local explainable method to approximate word importance ranking, and then adopts beam search to find the optimal solution. Extensive experiments show that LimeAttack achieves the better attacking performance compared with existing hard-label attack under the same query budget. In addition, we evaluate the effectiveness of LimeAttack on large language models, and results indicate that adversarial examples remain a significant threat to large language models. The adversarial examples crafted by LimeAttack are highly transferable and effectively improve model robustness in adversarial training.

Short-term forecasting of residential and commercial building energy consumption is widely used in power systems and continues to grow in importance. Data-driven short-term load forecasting (STLF), although promising, has suffered from a lack of open, large-scale datasets with high building diversity. This has hindered exploring the pretrain-then-fine-tune paradigm for STLF. To help address this, we present BuildingsBench, which consists of: 1) Buildings-900K, a large-scale dataset of 900K simulated buildings representing the U.S. building stock; and 2) an evaluation platform with over 1,900 real residential and commercial buildings from 7 open datasets. BuildingsBench benchmarks two under-explored tasks: zero-shot STLF, where a pretrained model is evaluated on unseen buildings without fine-tuning, and transfer learning, where a pretrained model is fine-tuned on a target building. The main finding of our benchmark analysis is that synthetically pretrained models generalize surprisingly well to real commercial buildings. An exploration of the effect of increasing dataset size and diversity on zero-shot commercial building performance reveals a power-law with diminishing returns. We also show that fine-tuning pretrained models on real commercial and residential buildings improves performance for a majority of target buildings. We hope that BuildingsBench encourages and facilitates future research on generalizable STLF. All datasets and code can be accessed from https://github.com/NREL/BuildingsBench.

We present a new Subset Simulation approach using Hamiltonian neural network-based Monte Carlo sampling for reliability analysis. The proposed strategy combines the superior sampling of the Hamiltonian Monte Carlo method with computationally efficient gradient evaluations using Hamiltonian neural networks. This combination is especially advantageous because the neural network architecture conserves the Hamiltonian, which defines the acceptance criteria of the Hamiltonian Monte Carlo sampler. Hence, this strategy achieves high acceptance rates at low computational cost. Our approach estimates small failure probabilities using Subset Simulations. However, in low-probability sample regions, the gradient evaluation is particularly challenging. The remarkable accuracy of the proposed strategy is demonstrated on different reliability problems, and its efficiency is compared to the traditional Hamiltonian Monte Carlo method. We note that this approach can reach its limitations for gradient estimations in low-probability regions of complex and high-dimensional distributions. Thus, we propose techniques to improve gradient prediction in these particular situations and enable accurate estimations of the probability of failure. The highlight of this study is the reliability analysis of a system whose parameter distributions must be inferred with Bayesian inference problems. In such a case, the Hamiltonian Monte Carlo method requires a full model evaluation for each gradient evaluation and, therefore, comes at a very high cost. However, using Hamiltonian neural networks in this framework replaces the expensive model evaluation, resulting in tremendous improvements in computational efficiency.

The final set of Jeffrey Epstein-related documents in a 2015 lawsuit between accuser Virginia Giuffre and his accomplice Ghislaine Maxwell revealed the plaintiff had accused Bill Richardson, Marvin Minsky and Les Wexner of sex trafficking her in a 2016 deposition. Their names had been redacted in a previous version of the 223-page filing unsealed in May 2022. Jean-Luc Brunel, who died in a French jail while awaiting trial on sex trafficking charges of his own, is also accused of victimizing her in the latest filings. Richardson was the former Democratic governor of New Mexico who died in September. Minsky was a leading computer scientist at the Massachusetts Institute of Technology who died in 2016.

Black voters spoke out Tuesday against President Biden, with some suggesting he has "failed" during his time in office as support among the critical voting bloc has dwindled. Georgia Republican voter Dorothy Harpe and Alabama Democrat voter Jason Brown weighed in on the president's report card as he struggles to garner Black support ahead of 2024. "Black voters realize now that Biden has failed this administration," Harpe told "Fox & Friends" host Lawrence Jones on Tuesday. "The prices of gas and everything is so expensive, and I spoke with some of the Black voters yesterday, and they said that everything was better under the administration of Donald Trump." President Biden speaks during a campaign event at Emanuel AME Church on January 8, 2024 in Charleston, South Carolina.

Heritage Foundation researcher Emma Waters joins'Fox & Friends Weekend' to discuss a recent report that a global birth decline is good for the planet. More than 60 heads of state and government and hundreds of business leaders are coming to Switzerland to discuss the biggest global challenges during the World Economic Forum's annual gathering next week, ranging from Israeli President Isaac Herzog to Ukrainian President Volodymyr Zelenskyy. The likes of U.S. Secretary of State Antony Blinken, Chinese Premier Li Qiang, EU Commission President Ursula von der Leyen, French President Emmanuel Macron, U.N. Secretary-General Antonio Guterres and many others will descend on the Alpine ski resort town of Davos on Jan. 15-19, organizers said Tuesday. Attendees have their work cut out for them with two major wars -- the Israel-Hamas conflict and Russia's invasion of Ukraine -- plus problems like climate change, major disruptions to trade in the Red Sea, a weak global economy and misinformation powered by rapidly advancing artificial intelligence in a major election year. Trust has eroded on peace and security, with global cooperation down since 2016 and plummeting since 2020, forum President Borge Brende said at a briefing.

The European Union is considering whether to launch a review of Microsoft's investment in ChatGPT maker OpenAI under the bloc's merger regulations, a month after the U.K. said it was also weighing whether the tech partnership could have an impact on competition. The European Commission, the EU's executive arm, made the disclosure on Tuesday as it sought input from interested parties on the level of competition in virtual worlds and generative artificial intelligence, and feedback on what competition law can do to keep these new markets competitive.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The former head of Britain's state-owned Post Office said Tuesday she will hand back a royal honor in response to mounting fury over a miscarriage of justice that saw hundreds of postmasters wrongfully accused of theft because of a faulty computer system. The British government is considering whether to offer a mass amnesty to more than 700 branch managers convicted of theft or fraud between 1999 and 2015, because Post Office computers wrongly showed that money was missing from their shops. The real culprit was a defective accounting system called Horizon, supplied by the Japanese technology firm Fujitsu.

Microsoft's multibillion-dollar investment in the ChatGPT developer OpenAI could face a merger investigation in the European Union, officials have said. Microsoft is the largest minority investor in OpenAI Global LLC, a "capped profit" subsidiary company that is controlled by OpenAI Inc, the non-profit majority owner of the organisation. Its investment, given in the form of cloud-computing credits as well as cash, officially gives it no control of the company itself, but the possibility of a maximum of a 100-times return on its capital. The European Commission said on Tuesday it was "checking whether Microsoft's investment in OpenAI might be reviewable under the EU merger regulation". OpenAI's unusual corporate structure was thrust into the limelight last year, when its chief executive, Sam Altman, was ousted and then reappointed in a bitter struggle with the non-profit's board.

A senior Hezbollah commander said the terrorist organization does not want an expanded war with Israel Tuesday, the same day that it launched a drone attack against an Israeli army base. Hezbollah, an Iran-backed group, claimed the Tuesday attack was in retribution for an Israeli strike that killed Wissam al-Tawil, who commanded Hezbollah's Radwan forces. Hezbollah deputy leader Naim Qassem released a televised speech stating that his group does not seek an all-out war with Israel, "but if Israel expands it, the response is inevitable to the maximum extent required to deter Israel." President Biden's administration has sought to prevent Israel's war against Hamas from boiling over into a regional conflict. Nevertheless, Iran's proxy terrorist groups have carried out more than 100 attacks on U.S. and Israeli targets since October.