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We study the problem of allocating a set of indivisible chores to three agents, among whom two have additive cost functions, in a fair manner. Two fairness notions under consideration are envy-freeness up to any chore (EFX) and a relaxed notion, namely envy-freeness up to transferring any chore (tEFX). In contrast to the case of goods, the case of chores remain relatively unexplored. In particular, our results constructively prove the existence of a tEFX allocation for three agents if two of them have additive cost functions and the ratio of their highest and lowest costs is bounded by two. In addition, if those two cost functions have identical ordering (IDO) on the costs of chores, then an EFX allocation exists even if the condition on the ratio bound is slightly relaxed. Throughout our entire framework, the third agent is unrestricted besides having a monotone cost function.

Federated learning (FL) allows agents to jointly train a global model without sharing their local data. However, due to the heterogeneous nature of local data, it is challenging to optimize or even define fairness of the trained global model for the agents. For instance, existing work usually considers accuracy equity as fairness for different agents in FL, which is limited, especially under the heterogeneous setting, since it is intuitively "unfair" to enforce agents with high-quality data to achieve similar accuracy to those who contribute low-quality data, which may discourage the agents from participating in FL. In this work, we propose a formal FL fairness definition, fairness via agent-awareness (FAA), which takes different contributions of heterogeneous agents into account. Under FAA, the performance of agents with high-quality data will not be sacrificed just due to the existence of large amounts of agents with low-quality data. In addition, we propose a fair FL training algorithm based on agent clustering (FOCUS) to achieve fairness in FL measured by FAA. Theoretically, we prove the convergence and optimality of FOCUS under mild conditions for linear and general convex loss functions with bounded smoothness. We also prove that FOCUS always achieves higher fairness in terms of FAA compared with standard FedAvg under both linear and general convex loss functions. Empirically, we show that on four FL datasets, including synthetic data, images, and texts, FOCUS achieves significantly higher fairness in terms of FAA while maintaining competitive prediction accuracy compared with FedAvg and state-of-the-art fair FL algorithms.

To mitigate the bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure fair treatment across all demographics, but it often comes at the expense of model performance. Understanding such tradeoffs, therefore, underlies the design of fair algorithms. To this end, this paper provides a complete characterization of the inherent tradeoff of demographic parity on classification problems, under the most general multi-group, multi-class, and noisy setting. Specifically, we show that the minimum error rate achievable by randomized and attribute-aware fair classifiers is given by the optimal value of a Wasserstein-barycenter problem. On the practical side, our findings lead to a simple post-processing algorithm that derives fair classifiers from score functions, which yields the optimal fair classifier when the score is Bayes optimal. We provide suboptimality analysis and sample complexity for our algorithm, and demonstrate its effectiveness on benchmark datasets., Comment: ICML 2023. Code is at https://github.com/rxian/fair-classification. Comparison to v2: corrected proof of Theorem 4.4

With the rapid development of the complexity and diversification of robot services, large number of data processing and analysis problems occurred during the robot working process. In this paper, we designed a collaborative system consisted multi-user and multi-robot based on cloud platform. By pre-setting the rights and restrictions owned by each user in different scenarios, the server determines the scene in which the robot is located by the picture taken by the robot, thereby giving each user different permissions. Enable the robot to listen to the user’s instructions to protect privacy. The integration of cloud technology and multi-robot systems made it possible to improve energy efficiency, real-time performance and reduce memory cost.

Recently, there have been a slew of design automation contests and released benchmarks. ISPD place & route contests, DAC placement contests, timing analysis contests at TAU and CAD contests at ICCAD are good examples in the past and more of new contests are planned in the upcoming conferences. These are interesting and important events that stimulate the research of the target problems and advance the cutting edge technologies. Nevertheless, most contests focus only on the point tool problems instead of addressing the design flow or co-optimization among design tools. OpenDesign Flow Database platform is developed to direct attentions to the overall design flow from logic synthesis to physical design optimization [1]. The goals are to provide an academic reference design flow based on past CAD contest results, the database for design benchmarks and point tool libraries, and standard design input/output formats to build a customized design flow by composing point tool libraries.

Ferroelectric PZT thin films were deposited on Pt and SrRuO3 substrates in a cold-wall reactor, using the Pb(C2H5)4/Zr(OBu)4/Ti(OPri)4/O2 reaction system. In comparison with Pt substrate, the growth rate of lead zirconate titanate (PZT) thin film was higher on SrRuO3. Lead content of the thin film deposited on either substrate at low temperatures (723–863 K) was much more temperature dependent than the other two metal contents. The strong temperature dependence originated from the high activation energy in the initial decomposition of Pb(C2H5)4 vapor, which was 54 kcal/mol. The surface reaction constant of lead precursor had much lower temperature dependence. The activation energy of surface reaction for PbO, estimated from deposition in a mini-chamber, was 6 kcal/mol on Pt and 9 kcal/mol on the SrRuO3 substrate. Therefore, the incorporation path of component oxide PbO, whose apparent activation energy was 31 kcal/mol on Pt and 29 kcal/mol on SrRuO3, essentially involved considerable gas-phase reaction. The PZT (50/50) thin film on SrRuO3 bottom electrode possessed a lower coercive field and a smaller remnant polarization than that on Pt. The PZT capacitor on SrRuO3 was also less vulnerable to polarization fatigue.

Nanoscale yttria doped ceria powder was successfully synthesized by azeotropic distillation process, which effectively dehydrated hydrous ceria and completely prevented the formation of hard agglomerates. After being calcined at 500°C for 2 h, the average powder size was less than 10 nm. Powder X-ray diffraction (XRD), transmission electron microscopy (TEM) and Raman spectroscopy were performed to characterize the powder properties. The electrical conductivity of the sintered pellets from this powder was also measured by a.c. impedance spectroscopy, with the values of 0.025 and 0.062 S cm −1 at 700°C and 800°C, respectively.