When Sen. Steven Bradford (D-Gardena) thinks about hip-hop's influence, he remembers all the wrongdoings artists illuminated throughout history. Even as hip-hop turns 50, the music is still provoking discussions about high-stakes political issues from police brutality to gun violence. "I'm in elected office today because of hip-hop," said Bradford, pointing to his time as a former nightclub promoter. "My attitude was if I can bring folks out to a club to listen to music, we can get folks out in our communities to rock the vote, and I use that as a catalyst to do what I do." Standing on the steps of the State Capitol in Sacramento on Monday, Bradford was among members of the California Legislative Black Caucus celebrating hip-hop's 50th anniversary.

Beijing is poised to implement sweeping new regulations for artificial intelligence services this week, trying to balance state control of the technology with enough support that its companies can become viable global competitors. The government has issued 24 guidelines that require platform providers to register their services and conduct a security review before they're brought to market. Seven agencies will take responsibility for oversight, including the Cyberspace Administration of China (CAC) and the National Development and Reform Commission. The final regulations are less onerous than an original draft from April, but they show that China, like Europe, is moving ahead with government oversight of what may be the most promising -- and controversial -- technology of the last 30 years.

Beijing is poised to implement sweeping new regulations for artificial intelligence services this week, trying to balance state control of the technology with enough support that its companies can become viable global competitors. The government issued 24 guidelines that require platform providers to register their services and conduct a security review before they're brought to market. Seven agencies will take responsibility for oversight, including the Cyberspace Administration of China and the National Development and Reform Commission. The final regulations are less onerous than an original draft from April, but they show China, like Europe, moving ahead with government oversight of what may be the most promising -- and controversial -- technology of the last 30 years. The U.S., by contrast, has no legislation under serious consideration even after industry leaders warned that AI poses a "risk of extinction" and OpenAI's Sam Altman urged Congress in public hearings to get involved.

As phasor measurement units (PMUs) are usually placed on the highest voltage buses, many lower voltage levels of the bulk power system are not observed by them. This lack of visibility makes time-synchronized state estimation of the full system a challenging problem. We propose a Deep Neural network-based State Estimator (DeNSE) to overcome this problem. The DeNSE employs a Bayesian framework to indirectly combine inferences drawn from slow timescale but widespread supervisory control and data acquisition (SCADA) data with fast timescale but local PMU data to attain sub-second situational awareness of the entire system. The practical utility of the proposed approach is demonstrated by considering topology changes, non-Gaussian measurement noise, and bad data detection and correction. The results obtained using the IEEE 118-bus system show the superiority of the DeNSE over a purely SCADA state estimator, a SCADA-PMU hybrid state estimator, and a PMU-only linear state estimator from a techno-economic viability perspective. Lastly, the scalability of the DeNSE is proven by performing state estimation on a large and realistic 2000-bus Synthetic Texas system.

As Exascale computing becomes a reality, the energy needs of compute nodes in cloud data centers will continue to grow. A common approach to reducing this energy demand is to limit the power consumption of hardware components when workloads are experiencing bottlenecks elsewhere in the system. However, designing a resource controller capable of detecting and limiting power consumption on-the-fly is a complex issue and can also adversely impact application performance. In this paper, we explore the use of Reinforcement Learning (RL) to design a power capping policy on cloud compute nodes using observations on current power consumption and instantaneous application performance (heartbeats). By leveraging the Argo Node Resource Management (NRM) software stack in conjunction with the Intel Running Average Power Limit (RAPL) hardware control mechanism, we design an agent to control the maximum supplied power to processors without compromising on application performance. Employing a Proximal Policy Optimization (PPO) agent to learn an optimal policy on a mathematical model of the compute nodes, we demonstrate and evaluate using the STREAM benchmark how a trained agent running on actual hardware can take actions by balancing power consumption and application performance.

In Chinese building codes, it is required that residential buildings receive a minimum number of hours of natural, direct sunlight on a specified winter day, which represents the worst sunlight condition in a year. This requirement is a prerequisite for obtaining a building permit during the conceptual design of a residential project. Thus, officially sanctioned software is usually used to assess the sunlight performance of buildings. These software programs predict sunlight hours based on repeated shading calculations, which is time-consuming. This paper proposed a multilayer perceptron-based method, a one-stage prediction approach, which outputs a shading time interval caused by the inputted cuboid-form building. The sunlight hours of a site can be obtained by calculating the union of the sunlight time intervals (complement of shading time interval) of all the buildings. Three numerical experiments, i.e., horizontal level and slope analysis, and simulation-based optimization are carried out; the results show that the method reduces the computation time to 1/84~1/50 with 96.5%~98% accuracies. A residential neighborhood layout planning plug-in for Rhino 7/Grasshopper is also developed based on the proposed model. This paper indicates that deep learning techniques can be adopted to accelerate sunlight hour simulations at the conceptual design phase.

In recent years, there has been a surge in effort to formalize notions of fairness in machine learning. We focus on clustering -- one of the fundamental tasks in unsupervised machine learning. We propose a new axiom ``proportional representation fairness'' (PRF) that is designed for clustering problems where the selection of centroids reflects the distribution of data points and how tightly they are clustered together. Our fairness concept is not satisfied by existing fair clustering algorithms. We design efficient algorithms to achieve PRF both for unconstrained and discrete clustering problems. Our algorithm for the unconstrained setting is also the first known polynomial-time approximation algorithm for the well-studied Proportional Fairness (PF) axiom (Chen, Fain, Lyu, and Munagala, ICML, 2019). Our algorithm for the discrete setting also matches the best known approximation factor for PF.

Deep neural networks have been widely used in various downstream tasks, especially those safety-critical scenario such as autonomous driving, but deep networks are often threatened by adversarial samples [1]. Such adversarial attacks can be invisible to human eyes, but can lead to DNN misclassification, and often exhibits transferability between deep learning and machine learning models [2] and real-world achievability[3]. Adversarial attacks can be divided into white-box attacks (Section 2.1), for which the attacker knows the parameters and gradient of the model, and black-box attacks (Section 2.2), for the latter, the attacker can only obtain the input and output of the model. In terms of the attacker's purpose, it can be divided into targeted attacks and non-targeted attacks, which means that the attacker wants the model to misclassify the original sample into the specified class, which is more practical, while the non-targeted attack just needs to make the model misclassify the sample. The black box setting is a scenario we will encounter in practice. Black-box attacks can also be divided into query-based attacks, which require a lot of repeated query model output to adjust perturbations, while transfer-based attacks do not, which makes the latter easier to do because too many queries are not allowed in practice.Transferbased attacks often require the use of a white-box surrogate model to create adversarial perturbations, which are mostly developed from existing white-box attacks. Zhang Yutong - graduate student, Harbin Institute of Technology; e-mail: 22s112078@stu.hit.edu.cn Li Yao - assistant professor, Harbin Institute of Technology; e-mail: yaoli0508@hit.edu.cn,

Millimeter wave (mmWave) based speech recognition provides more possibility for audio-related applications, such as conference speech transcription and eavesdropping. However, considering the practicality in real scenarios, latency and recognizable vocabulary size are two critical factors that cannot be overlooked. In this paper, we propose Radio2Text, the first mmWave-based system for streaming automatic speech recognition (ASR) with a vocabulary size exceeding 13,000 words. Radio2Text is based on a tailored streaming Transformer that is capable of effectively learning representations of speech-related features, paving the way for streaming ASR with a large vocabulary. To alleviate the deficiency of streaming networks unable to access entire future inputs, we propose the Guidance Initialization that facilitates the transfer of feature knowledge related to the global context from the non-streaming Transformer to the tailored streaming Transformer through weight inheritance. Further, we propose a cross-modal structure based on knowledge distillation (KD), named cross-modal KD, to mitigate the negative effect of low quality mmWave signals on recognition performance. In the cross-modal KD, the audio streaming Transformer provides feature and response guidance that inherit fruitful and accurate speech information to supervise the training of the tailored radio streaming Transformer. The experimental results show that our Radio2Text can achieve a character error rate of 5.7% and a word error rate of 9.4% for the recognition of a vocabulary consisting of over 13,000 words.

When deploying machine learning models in production for any product/application, there are three properties that are commonly desired. First, the models should be generalizable, in that we can extend it to further use cases as our knowledge of the domain area develops. Second they should be evaluable, so that there are clear metrics for performance and the calculation of those metrics in production settings are feasible. Finally, the deployment should be cost-optimal as far as possible. In this paper we propose that these three objectives (i.e. generalization, evaluation and cost-optimality) can often be relatively orthogonal and that for large language models, despite their performance over conventional NLP models, enterprises need to carefully assess all the three factors before making substantial investments in this technology. We propose a framework for generalization, evaluation and cost-modeling specifically tailored to large language models, offering insights into the intricacies of development, deployment and management for these large language models.