This paper addresses a variant of the conflict-based search (CBS) algorithm for multi-agent path finding, presenting an effective cluster reasoning heuristic that searches for and detects conflicts involving more than two agents. The proposed in-depth cluster reasoning technique systematically alters the reference agent when computing the heuristic cost-to-go, and this yields a tighter estimate. It then balances the complexity of expanding CBS nodes against that of locating clusters by systematically scheduling the associated parameters. Numerical experiments demonstrate that the proposed algorithm successfully detects clusters that state-of-the-art algorithms cannot identify and produces solutions for various extreme scenarios that existing algorithms cannot solve.

Analog/mixed-signal circuit design encounters significant challenges due to performance degradation from process, voltage, and temperature (PVT) variations. To achieve commercial-grade reliability, iterative manual design revisions and extensive statistical simulations are required. While several studies have aimed to automate variation-aware analog design to reduce time-to-market, the substantial mismatches in real-world wafers have not been thoroughly addressed. In this paper, we present GLOVA, an analog circuit sizing framework that effectively manages the impact of diverse random mismatches to improve robustness against PVT variations. In the proposed approach, risk-sensitive reinforcement learning is leveraged to account for the reliability bound affected by PVT variations, and ensemble-based critic is introduced to achieve sample-efficient learning. For design verification, we also propose μ-σ evaluation and simulation reordering method to reduce simulation costs of identifying failed designs. GLOVA supports verification through industrial-level PVT variation evaluation methods, including corner simulation as well as global and local Monte Carlo (MC) simulations. Compared to previous state-of-the-art variation-aware analog sizing frameworks, GLOVA achieves up to $80.5 \times$ improvement in sample efficiency and $76.0 \times$ reduction in time.

Masked diffusion models (MDMs) offer a promising non-autoregressive alternative for large language modeling.Standard decoding methods for MDMs, such as confidence-based sampling, select tokens independently based on individual token confidences at each diffusion step.However, we observe that this independent token selection often results in generation orders resembling sequential autoregressive processes, limiting the advantages of non-autoregressive modeling.To mitigate this pheonomenon, we propose Reward-Weighted Sampling (RWS), a novel decoding strategy that leverages an external reward model to provide a principled global signal during the iterative diffusion process.Specifically, at each diffusion step, RWS evaluates the quality of the entire intermediate sequence and scales token logits accordingly, guiding token selection by integrating global sequence-level coherence.This method selectively increases the confidence of tokens that initially have lower scores, thereby promoting a more non-autoregressive generation order.Furthermore, we provide theoretical justification showing that rewardweighted logit scaling induces beneficial rank reversals in token selection and consistently improves expected reward.Experiments demonstrate that RWS significantly promotes nonautoregressive generation orders, leading to improvements across multiple evaluation metrics.These results highlight the effectiveness of integrating global signals in enhancing both the non-autoregressive properties and overall performance of MDMs.

Recent efforts to apply Large Language Models (LLMs) to time-series anomaly detection (TSAD) have yielded limited success, often performing worse than even simple methods. While prior work has focused solely on downstream performance evaluation, the fundamental question—why do LLMs struggle with TSAD?—has remained largely unexplored. In this paper, we present an in-depth analysis that identifies two core challenges in understanding complex temporal dynamics and accurately localizing anomalies. To address these challenges, we propose a simple yet effective method that combines statistical decomposition with index-aware prompting. Our method outperforms 21 existing prompting strategies on the AnomLLM benchmark, achieving up to a 66.6\% improvement in F1 score. We further compare LLMs with 16 non-LLM baselines on the TSB-AD benchmark, highlighting scenarios where LLMs offer unique advantages via contextual reasoning. Our findings provide empirical insights into how and when LLMs can be effective for TSAD. The code is publicly available at: https://github.com/junwoopark92/LLM-TSAD

To accelerate the execution of transformer models, compute-in-memory (CIM) has been widely adopted. However, the CIM architecture has the drawback of fixed one-way computing structure supporting only horizontal input sharing vertical accumulation (HIVA). So, it faces two major obstacles: 1) Matrix transposition processing needs large hardware overheads, 2) A fixed dataflow incurs CIM underutilization problem, degrading throughput. In this paper, we present a digital SRAM CIM (DCIM)-based transformer accelerator that supports two-way computing: HIVA and vertical input sharing horizontal accumulation (VIHA). The proposed two-way computing DCIM macro features a novel 9T SRAM bitcell and transposable adder tree, which efficiently reduces matrix transposition costs. We also present a novel dataflow to improve overall self-attention latency by resolving CIM underutilization and hiding dynamic input generation latency. In addition, CIM-friendly computation skipping scheme is exploited to enhance energy efficiency with negligible accuracy loss. The simulation results show that the proposed DCIM-based accelerator achieves up to 2.5x latency improvement and 24% energy savings compared to previous CIM-based accelerators.

Long-term forecasting presents unique challenges due to the time and memory complexity of handling long sequences. Existing methods, which rely on sliding windows to process long sequences, struggle to effectively capture long-term variations that are partially caught within the short window (i.e., outer-window variations). In this paper, we introduce a novel approach that overcomes this limitation by employing contrastive learning and enhanced decomposition architecture, specifically designed to focus on long-term variations. To this end, our contrastive loss incorporates global autocorrelation held in the whole time series, which facilitates the construction of positive and negative pairs in a self-supervised manner. When combined with our decomposition networks, our constrative learning significantly improves long-term forecasting performance. Extensive experiments demonstrate that our approach outperforms 14 baseline models on well-established nine long-term benchmarks, especially in challenging scenarios that require a significantly long output for forecasting. This paper not only presents a novel direction for long-term forecasting but also offers a more reliable method for effectively integrating long-term variations into time-series representation learning.

Recently, deep learning-based methods have drawn huge attention due to their simple yet high performance without domain knowledge in sound classification and localization tasks. However, a lack of gun sounds in existing datasets has been a major obstacle to implementing a support system to spot criminals from their gunshots by leveraging deep learning models. Since the occurrence of gunshot is rare and unpredictable, it is impractical to collect gun sounds in the real world. As an alternative, gun sounds can be obtained from an FPS game that is designed to mimic real-world warfare. The recent FPS game offers a realistic environment where we can safely collect gunshot data while simulating even dangerous situations. By exploiting the advantage of the game environment, we construct a gunshot dataset, namely BGG, for the firearm classification and gunshot localization tasks. The BGG dataset consists of 37 different types of firearms, distances, and directions between the sound source and a receiver. We carefully verify that the in-game gunshot data has sufficient information to identify the location and type of gunshots by training several sound classification and localization baselines on the BGG dataset. Afterward, we demonstrate that the accuracy of real-world firearm classification and localization tasks can be enhanced by utilizing the BGG dataset.

Deep neural networks improved the accuracy of sequential recommendation approach which takes into account the sequential patterns of user logs, e.g., a purchase history of a user. However, incorporating only the individual's recent logs may not be sufficient in properly reflecting global preferences and trends across all users and items. In response, we propose a self-attentive sequential recommender system with topic modeling-based category embedding as a novel approach to exploit global information in the process of sequential recommendation. Our self-attention module effectively leverages the sequential patterns from the user's recent history. In addition, our novel category embedding approach, which utilizes the information computed by topic modeling, efficiently captures global information that the user generally prefers. Furthermore, to provide diverse recommendations as well as to prevent overfitting, our model also incorporates a vector obtained by random sampling. Experimental studies show that our model outperforms state-of-the-art sequential recommendation models, and that category embedding effectively provides global preference information.

This paper presents a highly sensitive dual mode tactile and proximity sensor for robotic applications that uses Carbon Microcoils (CMCs). The sensor consists of multiple electrode layers printed on a Flexible Printed Circuit Board (FPCB) and a dielectric substrate into which the CMCs are dispersed. The dielectric layer is simply put on the top of the FPCB. Thus, the sensor provides ease of fabrication and robustness against repetitive external contacts because the dielectric layer protects the electrodes. The electrical properties of the sensor are changed when an object approaches or touches the sensor. The sensor uses a capacitive sensing mode for tactile sensing and an inductive sensing mode for proximity sensing. CMCs amplify the change of the sensor signal because of electrical impedance formed by the CMCs, and thus, the sensitivity of the sensor increases. We fabricate the prptotype sensor with the dimensions of 30 × 30 × 0.6 mm3, and with 3 mm spatial-resolution. The sensor detects the applied pressure up to 330 kPa and the distance of an object as much as 150 mm away.

Aiming to apply soft actuators in driving a walking robot, the design, fabrication and locomotion of a bio-inspired printable hexapod robot are studied. The robot mimics the insect's design and walking posture by driving six legs with alternating tripod gait which provides its locomotive adaptability on flat terrains. The versatile movements of the robot's leg are achieved by using soft and multiple degree-of-freedom actuators. The actuators are made by dielectric elastomers with a simple mechanism based on antagonistic configuration. By using 3D printing method, the actuator can be embedded into the frame of the robot and a control system is developed. Finally, the robot's locomotion is successfully demonstrated with variable speeds and stride lengths.