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This paper introduces an adaptive logic synthesis dataset generation framework designed to enhance machine learning applications within the logic synthesis process. Unlike previous dataset generation flows that were tailored for specific tasks or lacked integrated machine learning capabilities, the proposed framework supports a comprehensive range of machine learning tasks by encapsulating the three fundamental steps of logic synthesis: Boolean representation, logic optimization, and technology mapping. It preserves the original information in the intermediate files that can be stored in both Verilog and Graphmal format. Verilog files enable semi-customizability, allowing researchers to add steps and incrementally refine the generated dataset. The framework also includes an adaptive circuit engine to facilitate the loading of GraphML files for final dataset packaging and sub-dataset extraction. The generated OpenLS-D dataset comprises 46 combinational designs from established benchmarks, totaling over 966,000 Boolean circuits, with each design containing 21,000 circuits generated from 1000 synthesis recipes, including 7000 Boolean networks, 7000 ASIC netlists, and 7000 FPGA netlists. Furthermore, OpenLS-D supports integrating newly desired data features, making it more versatile for new challenges. The utility of OpenLS-D is demonstrated through four distinct downstream tasks: circuit classification, circuit ranking, quality of results (QoR) prediction, and probability prediction. Each task highlights different internal steps of logic synthesis, with the datasets extracted and relabeled from the OpenLS-D dataset using the circuit engine. The experimental results confirm the dataset's diversity and extensive applicability. The source code and datasets are available at //github.com/Logic-Factory/ACE/blob/master/OpenLS-D/readme.md.

We propose a machine learning algorithm for solving finite-horizon stochastic control problems based on a deep neural network representation of the optimal policy functions. The algorithm has three features: (1) It can solve high-dimensional (e.g., over 100 dimensions) and finite-horizon time-inhomogeneous stochastic control problems. (2) It has a monotonicity of performance improvement in each iteration, leading to good convergence properties. (3) It does not rely on the Bellman equation. To demonstrate the efficiency of the algorithm, it is applied to solve various finite-horizon time-inhomogeneous problems including recursive utility optimization under a stochastic volatility model, a multi-sector stochastic growth, and optimal control under a dynamic stochastic integration of climate and economy model with eight-dimensional state vectors and 600 time periods.

Rate splitting multiple access (RSMA) is regarded as an essential and powerful physical-layer (PHY) paradigm for next generation communication systems. Under such a system, users employ successive interference cancellation (SIC), allowing them to decode a portion of the interference and treat the remainder as noise. However, a problem is that current RSMA systems rely on fixed-position antenna arrays, limiting their capacity to fully exploit spatial freedom. This constraint restricts beamforming gain, which substantially degrades RSMA performance. To address this problem, we propose an movable antenna (MA)-aided RSMA scheme that allows the antennas at the base station (BS) to adjust their positions dynamically. Our target is to maximize the system's sum rate of both common and private messages by jointly optimizing the MA positions, beamforming matrix, and common rate allocation. To tackle the formulated non-convex problem, we employ fractional programming (FP) and develop a two-stage, coarse-to-fine-grained search algorithm to obtain suboptimal solutions. Numerical results demonstrate that, with appropriate antenna adjustments, the MA-enabled system significantly enhances the overall performance and reliability of RSMA when employing the proposed algorithm compared to fixed-position antenna configurations.

Solving constrained multi-objective optimization problems (CMOPs) is a challenging task. While many practical algorithms have been developed to tackle CMOPs, real-world scenarios often present cases where the constraint functions are unknown or unquantifiable, resulting in only binary outcomes (feasible or infeasible). This limitation reduces the effectiveness of constraint violation guidance, which can negatively impact the performance of existing algorithms that rely on this approach. Such challenges are particularly detrimental for algorithms employing the epsilon-based method, as they hinder effective relaxation of the feasible region. To address these challenges, this paper proposes a novel algorithm called DRMCMO based on the detection region method. In DRMCMO, detection regions dynamic monitor feasible solutions to enhance convergence, helping the population escape local optima. Additionally, these regions collaborate with the neighbor pairing strategy to improve population diversity within narrow feasible areas. We have modified three existing test suites to serve as benchmark test problems for CMOPs with binary constraints(CMOP/BC) and conducted comprehensive comparative experiments with state-of-the-art algorithms on these test suites and real-world problems. The results demonstrate the strong competitiveness of DRMCMO against state-of-the-art algorithms. Given the limited research on CMOP/BC, our study offers a new perspective for advancing this field.

Quantum low-density parity-check (LDPC) codes are a promising family of quantum error-correcting codes for fault tolerant quantum computing with low overhead. Decoding quantum LDPC codes on quantum erasure channels has received more attention recently due to advances in erasure conversion for various types of qubits including neutral atoms, trapped ions, and superconducting qubits. Belief propagation with guided decimation (BPGD) decoding of quantum LDPC codes has demonstrated good performance in bit-flip and depolarizing noise. In this work, we apply BPGD decoding to quantum erasure channels. Using a natural modification, we show that BPGD offers competitive performance on quantum erasure channels for multiple families of quantum LDPC codes. Furthermore, we show that the performance of BPGD decoding on erasure channels can sometimes be improved significantly by either adding damping or adjusting the initial channel log-likelihood ratio for bits that are not erased. More generally, our results demonstrate BPGD is an effective general-purpose solution for erasure decoding across the quantum LDPC landscape.

We present a novel approach for minimally invasive flexible needle manipulations by pairing a real-time finite element simulator with the cross-entropy method. Additionally, we demonstrate how a kinematic-driven bang-bang controller can complement the control framework for better tracking performance. We show how electromagnetic (EM) tracking can be readily incorporated into the framework to provide controller feedback. Tissue phantom experiment with EM tracking shows the average targeting error is $0.16 \pm 0.29mm$.

We study the benefits of complex-valued weights for neural networks. We prove that shallow complex neural networks with quadratic activations have no spurious local minima. In contrast, shallow real neural networks with quadratic activations have infinitely many spurious local minima under the same conditions. In addition, we provide specific examples to demonstrate that complex-valued weights turn poor local minima into saddle points.

Knowledge distillation provides an effective method for deploying complex machine learning models in resource-constrained environments. It typically involves training a smaller student model to emulate either the probabilistic outputs or the internal feature representations of a larger teacher model. By doing so, the student model often achieves substantially better performance on a downstream task compared to when it is trained independently. Nevertheless, the teacher's internal representations can also encode noise or additional information that may not be relevant to the downstream task. This observation motivates our primary question: What are the information-theoretic limits of knowledge transfer? To this end, we leverage a body of work in information theory called Partial Information Decomposition (PID) to quantify the distillable and distilled knowledge of a teacher's representation corresponding to a given student and a downstream task. Moreover, we demonstrate that this metric can be practically used in distillation to address challenges caused by the complexity gap between the teacher and the student representations.

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.

We introduce a generic framework that reduces the computational cost of object detection while retaining accuracy for scenarios where objects with varied sizes appear in high resolution images. Detection progresses in a coarse-to-fine manner, first on a down-sampled version of the image and then on a sequence of higher resolution regions identified as likely to improve the detection accuracy. Built upon reinforcement learning, our approach consists of a model (R-net) that uses coarse detection results to predict the potential accuracy gain for analyzing a region at a higher resolution and another model (Q-net) that sequentially selects regions to zoom in. Experiments on the Caltech Pedestrians dataset show that our approach reduces the number of processed pixels by over 50% without a drop in detection accuracy. The merits of our approach become more significant on a high resolution test set collected from YFCC100M dataset, where our approach maintains high detection performance while reducing the number of processed pixels by about 70% and the detection time by over 50%.