3D ICs extend integration freedom beyond post-Moore limits and can improve performance. Yet, existing true-3D placers remain primarily wirelength-driven, and partition-based 3D flows struggle to incorporate timing during design-space exploration. Prior 2D timing-driven approaches often rely on RSMT-based routing lookahead, which is unstable under z-moves and lacks a smooth objective for gradient-based optimization; simple net weighting further fails to capture path-level timing. We present IDDA-3D, the first timing-driven placement framework for face-to-face (F2F) bonded 3D ICs. IDDA-3D introduces a quadratic RC formulation that models intra-/inter-die driver-sink delay as a differentiable timing cost for analytical placement. The RC parameters are derived directly from the technology library, ensuring that the model reflects physical delay accurately and remains applicable across diverse technology nodes without manual tuning. To handle the discrete nature of die assignment, we employ a finite-difference approximation (FDA)-based gradient computation with preconditioning, which integrates seamlessly with the analytical placement engine. Experimental results show that IDDA-3D improves total negative slack (TNS) by up to 44% and worst negative slack (WNS) by 22%, while maintaining competitive wirelength and runtime compared with state-of-the-art true-3D placers.

We study a two-sided market, wherein, price-sensitive heterogeneous customers and servers arrive and join their respective queues. A compatible customer-server pair can then be matched by the platform, at which point, they leave the system. Our objective is to design pricing and matching algorithms that maximize the platform's profit, while maintaining reasonable queue lengths. As the demand and supply curves governing the price-dependent arrival rates may not be known in practice, we design a novel online-learning-based pricing policy and establish its near-optimality. In particular, we prove a tradeoff among three performance metrics: $\tilde{O}(T^{1-\gamma})$ regret, $\tilde{O}(T^{\gamma/2})$ average queue length, and $\tilde{O}(T^{\gamma})$ maximum queue length for $\gamma \in (0, 1/6]$, significantly improving over existing results (Yang & Ying, 2024). Moreover, barring the permissible range of $\gamma$, we show that this trade-off between regret and average queue length is optimal up to logarithmic factors under a class of policies, matching the optimal one as in (Varma et al., 2023) which assumes the demand and supply curves to be known. Our proposed policy has two noteworthy features: a dynamic component that optimizes the tradeoff between low regret and small queue lengths; and a probabilistic component that resolves the tension between obtaining useful samples for fast learning and maintaining small queue lengths.

The traditional multi-armed bandit (MAB) model for recommendation systems assumes the user stays in the system for the entire learning horizon. In new online education platforms such as ALEKS or new video recommendation systems such as TikTok, the amount of time a user spends on the app depends on how engaging the recommended contents are. Users may temporarily leave the system if the recommended items cannot engage the users. To understand the exploration, exploitation, and engagement in these systems, we propose a new model, called MAB-A where “A” stands for abandonment and the abandonment probability depends on the current recommended item and the user's past experience (called state). We propose two algorithms, ULCB and KL-ULCB, both of which do more exploration (being optimistic) when the user likes the previous recommended item and less exploration (being pessimistic) when the user does not. We prove that both ULCB and KL-ULCB achieve logarithmic regret, $O(\log K)$, where $K$ is the number of visits (or episodes). Furthermore, the regret bound under KL-ULCB is asymptotically sharp. We also extend the proposed algorithms to the general-state setting. Simulation results show that the proposed algorithms have significantly lower regret than the traditional UCB and KL-UCB, and Q-learning-based algorithms.

Existing approaches for vehicle repositioning on large-scale ride-hailing platforms either ignore the spatial-temporal mismatch between supply and demand in real-time or overlook the long-term balance of the system. To account for both, we propose a lookahead repositioning policy in this paper, which is a novel approach to repositioning idle vehicles from both a dynamic system and a long-term performance perspective. Our method consists of two parts; the first part utilizes linear programming (LP) to formulate the nonstationary system as a time-varying,$T$-step lookahead optimization problem and explicitly models the fraction of drivers who follow repositioning recommendations (called the repositioning rate). The second step is to incorporate a reinforcement learning (RL) method to maximize long-term return based on learned value functions after the$T$time slots. Extensive studies utilizing a real-world dataset on both small-scale and large-scale simulators show that our method outperforms previous baseline methods and is robust to prediction errors.

Multi-server queueing systems are widely used models for job scheduling in machine learning, wireless networks, and crowdsourcing. This paper considers a multi-server system with multiple servers and multiple types of jobs, where different job types require different amounts of processing time at different servers. The goal is to schedule jobs on servers without knowing the statistics of the processing times. To fully utilize the processing power of the servers, it is known that one has to at least learn the service rates of different job types on different servers. Prior works on this topic decouple the learning and scheduling phases which leads to either excessive exploration or extremely large job delays. We propose a new algorithm, which combines the MaxWeight scheduling policy with discounted upper confidence bound (UCB), to simultaneously learn the statistics and schedule jobs to servers. We obtain performance bounds for our algorithm that hold for both stationary and nonstationary service rates. Simulations confirm that the delay performance of our algorithm is several orders of magnitude better than previously proposed algorithms. Our algorithm also has the added benefit that it can handle non-stationarity in the service processes.

This paper studies online nonstochastic control problems with adversarial and static constraints. We propose online nonstochastic control algorithms that achieve both sublinear regret and sublinear adversarial constraint violation while keeping static constraint violation minimal against the optimal constrained linear control policy in hindsight. To establish the results, we introduce an online convex optimization with memory framework under adversarial and static constraints, which serves as a subroutine for the constrained online nonstochastic control algorithms. This subroutine also achieves the state-of-the-art regret and constraint violation bounds for constrained online convex optimization problems, which is of independent interest. Our experiments demonstrate the proposed control algorithms are adaptive to adversarial constraints and achieve smaller cumulative costs and violations. Moreover, our algorithms are less conservative and achieve significantly smaller cumulative costs than the state-of-the-art algorithm.

In this paper, we propose a multiple pulse position modulation (MPPM) aided reverse polarity optical orthogonal frequency division multiplexing (RPO- OFDM) scheme that aims to enhance the spectral efficiency (SE) for dimmable visible light communication (VLC) systems. In this scheme, we exploit the MPPM patterns to transmit the extra information bits without the increase of the signal bandwidth, and thus improving the SE. Specifically, for each frame, the binary information stream is divided into two parallel groups, where the first group is used to select the patterns of MPPM symbols, while the other is modulated by optical OFDM. Then the time-domain OFDM signal is accommodated by both the 'on' and 'off' slots of MPPM symbols for transmissions over the VLC channel. Theoretical analysis on the frame error probability (FEP) is provided and validated by simulation results, showing that the proposed system with extended dimming control achieves the highest effective spectral efficiency (ESE) in most of the scenarios compared with a few reference systems, without substantially increasing the computational complexity.