OpenRaiser/Intern-Atlas huggingface dataset:
A 65 nm Bayesian Neural Network Accelerator with 360 fJ/Sample In-Word GRNG for AI Uncertainty Estimation
Venue: arXiv cs.AR 2025
Year: 2025

Uncertainty estimation is an indispensable capability for AI-enabled, safety-critical applications, e.g. autonomous vehicles or medical diagnosis. Bayesian neural networks (BNNs) use Bayesian statistics to provide both classification predictions and uncertainty estimation, but they suffer from high computational overhead associated with random number generation and repeated sample iterations. Furthermore, BNNs are not immediately amenable to acceleration through compute-in-memory architectures due to the frequent memory writes necessary after each RNG operation. To address these challenges, we present an ASIC that integrates 360 fJ/Sample Gaussian RNG directly into the SRAM memory words. This integration reduces RNG overhead and enables fully-parallel compute-in-memory operations for BNNs. The prototype chip achieves 5.12 GSa/s RNG throughput and 102 GOp/s neural network throughput while occupying 0.45 mm2, bringing AI uncertainty estimation to edge computation.
Design of a 6-bit Threshold Inverter Quantization (TIQ) Flash Analog to Digital Converter (ADC)
Venue: arXiv cs.AR 2025
Year: 2025

An ADC is used to convert analog signals into binary signals. Compared with many other types of ADCs, flash converters are incredibly quick. A typical Flash ADC consists of 2n resistors, 2n-1 op-amp comparators, and an encoder which requires more area. The resistors and comparators can be eliminated by using threshold inverter quantization (TIQ) comparators. As a voltage comparator, TIQ technique uses two cascaded CMOS inverters. So that there will be no variation in the fabrication process, and temperature. A 6-bit flash ADC based on threshold inverter quantization (TIQ) comparator was designed and software implementation was performed employing a fat tree encoder with 0.25 micrometer CMOS technology. The design consists of 2n-1 TIQ comparator arrays, a gain booster, a 1-out-of-n code generator, and a fat tree encoder. This TIQ flash ADC is simulated with the Tanner EDA Tool. Here the supply voltage is 2.5 V, input frequency of 10 kHz and 10 MHz. The power consumption of the ADC is 6.25 mW, and the propagation delay is 1.07 microseconds for 10 kHz input frequency. For 10 MHz input frequency, power consumption is 12.12 mW and propagation delay is 947.14 ms.
Improving Human-Robot Teaching by Quantifying and Reducing Mental Model Mismatch
Venue: arXiv cs.RO 2025
Year: 2025

The rapid development of artificial intelligence and robotics has had a significant impact on our lives, with intelligent systems increasingly performing tasks traditionally performed by humans. Efficient knowledge transfer requires matching the mental model of the human teacher with the capabilities of the robot learner. This paper introduces the Mental Model Mismatch (MMM) Score, a feedback mechanism designed to quantify and reduce mismatches by aligning human teaching behavior with robot learning behavior. Using Large Language Models (LLMs), we analyze teacher intentions in natural language to generate adaptive feedback. A study with 150 participants teaching a virtual robot to solve a puzzle game shows that intention-based feedback significantly outperforms traditional performance-based feedback or no feedback. The results suggest that intention-based feedback improves instructional outcomes, improves understanding of the robot's learning process and reduces misconceptions. This research addresses a critical gap in human-robot interaction (HRI) by providing a method to quantify and mitigate discrepancies between human mental models and robot capabilities, with the goal of improving robot learning and human teaching effectiveness.
An Analysis on Stabilizability and Reliability Relationship in Wireless Networked Control Systems
Venue: arXiv cs.NI 2025
Year: 2025

The stabilizability of wireless networked control systems (WNCSs) is a deterministic binary valued parameter proven to hold if the communication data rate is higher than the sum of the logarithm of unstable eigenvalues of the open-loop control system. In this analysis, it is assumed that the communication system provides a fixed deterministic transmission rate between the sensors and controllers. Due to the stochastic parameters of communication channels, such as small-scale fading, the instantaneous rate is an intrinsically stochastic parameter. In this sense, it is a common practice in the literature to use the deterministic ergodic rate in analyzing the asymptotic stabilizability. Theoretically, there exists no work in the literature investigating how the ergodic rate can be incorporated into the analysis of asymptotic stabilizability. Considering the stochastic nature of channel parameters, we introduce the concept of probability of stabilizability by interconnecting communication link reliability with the system's unstable eigenvalues and derive a closed-form expression that quantifies this metric. Numerical results are provided to visualize how communication and control systems' parameters affect the probability of stabilizability of the overall system.
Improved Approximation Algorithms for (1,2)-TSP and Max-TSP Using Path Covers in the Semi-Streaming Model
Venue: arXiv cs.DS 2025
Year: 2025

We investigate semi-streaming algorithms for the Traveling Salesman Problem (TSP). Specifically, we focus on a variant known as the $(1,2)$-TSP, where the distances between any two vertices are either one or two. Our primary emphasis is on the closely related Maximum Path Cover Problem, which aims to find a collection of vertex-disjoint paths that cover the maximum number of edges in a graph. We propose an algorithm that, for any $ε> 0$, achieves a $(\frac{2}{3}-ε)$-approximation of the maximum path cover size for an $n$-vertex graph, using $\text{poly}(\frac{1}ε)$ passes. This result improves upon the previous $\frac{1}{2}$-approximation by Behnezhad et al. [ICALP 2024] in the semi-streaming model. Building on this result, we design a semi-streaming algorithm that constructs a tour for an instance of $(1,2)$-TSP with an approximation factor of $(\frac{4}{3} + ε)$, improving upon the previous $\frac{3}{2}$-approximation actor algorithm by Behnezhad et al. [ICALP 2024] (Although it is not explicitly stated in the paper that their algorithm works in the semi-streaming model, it is easy to verify). Furthermore, we extend our approach to develop an approximation algorithm for the Maximum TSP (Max-TSP), where the goal is to find a Hamiltonian cycle with the maximum possible weight in a given weighted graph $G$. Our algorithm provides a $(\frac{7}{12} - ε)$-approximation for Max-TSP in $\text{poly}(\frac{1}ε)$ passes, improving on the previously known $(\frac{1}{2}-ε)$-approximation obtained via maximum weight matching in the semi-streaming model.
EDMB: Edge Detector with Mamba
Venue: arXiv cs.CV 2025
Year: 2025

Transformer-based models have made significant progress in edge detection, but their high computational cost is prohibitive. Recently, vision Mamba have shown excellent ability in efficiently capturing long-range dependencies. Drawing inspiration from this, we propose a novel edge detector with Mamba, termed EDMB, to efficiently generate high-quality multi-granularity edges. In EDMB, Mamba is combined with a global-local architecture, therefore it can focus on both global information and fine-grained cues. The fine-grained cues play a crucial role in edge detection, but are usually ignored by ordinary Mamba. We design a novel decoder to construct learnable Gaussian distributions by fusing global features and fine-grained features. And the multi-grained edges are generated by sampling from the distributions. In order to make multi-granularity edges applicable to single-label data, we introduce Evidence Lower Bound loss to supervise the learning of the distributions. On the multi-label dataset BSDS500, our proposed EDMB achieves competitive single-granularity ODS 0.837 and multi-granularity ODS 0.851 without multi-scale test or extra PASCAL-VOC data. Remarkably, EDMB can be extended to single-label datasets such as NYUDv2 and BIPED. The source code is available at https://github.com/Li-yachuan/EDMB.
Multilinear Tensor Low-Rank Approximation for Policy-Gradient Methods in Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement learning (RL) aims to estimate the action to take given a (time-varying) state, with the goal of maximizing a cumulative reward function. Predominantly, there are two families of algorithms to solve RL problems: value-based and policy-based methods, with the latter designed to learn a probabilistic parametric policy from states to actions. Most contemporary approaches implement this policy using a neural network (NN). However, NNs usually face issues related to convergence, architectural suitability, hyper-parameter selection, and underutilization of the redundancies of the state-action representations (e.g. locally similar states). This paper postulates multi-linear mappings to efficiently estimate the parameters of the RL policy. More precisely, we leverage the PARAFAC decomposition to design tensor low-rank policies. The key idea involves collecting the policy parameters into a tensor and leveraging tensor-completion techniques to enforce low rank. We establish theoretical guarantees of the proposed methods for various policy classes and validate their efficacy through numerical experiments. Specifically, we demonstrate that tensor low-rank policy models reduce computational and sample complexities in comparison to NN models while achieving similar rewards.
Beyond Life: A Digital Will Solution for Posthumous Data Management
Venue: arXiv cs.CR 2025
Year: 2025

In the digital era, managing posthumous data presents a growing challenge, with current technical solutions often falling short in practicality. Existing tools are typically closed-source, lack transparency, fail to offer cross-platform support, and provide limited access control. This paper introduces `Beyond Life', a cross-platform digital will management solution designed to securely handle and distribute digital assets after death. At the core of this solution is a customized Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme, referred to as PD-CP-ABE, which enables efficient, fine-grained control over access to will content at scale. Unlike existing systems, Beyond Life operates independently of service providers, offering users greater transparency and control over how their will is generated, stored, and executed. The system is also designed to be portable, allowing users to change their will service provider. The proposed system has been fully developed and rigorously evaluated to ensure performance and real-world feasibility. The system implementation is made publicly available.
HaVen: Hallucination-Mitigated LLM for Verilog Code Generation Aligned with HDL Engineers
Venue: arXiv cs.PL 2025
Year: 2025

Recently, the use of large language models (LLMs) for Verilog code generation has attracted great research interest to enable hardware design automation. However, previous works have shown a gap between the ability of LLMs and the practical demands of hardware description language (HDL) engineering. This gap includes differences in how engineers phrase questions and hallucinations in the code generated. To address these challenges, we introduce HaVen, a novel LLM framework designed to mitigate hallucinations and align Verilog code generation with the practices of HDL engineers. HaVen tackles hallucination issues by proposing a comprehensive taxonomy and employing a chain-of-thought (CoT) mechanism to translate symbolic modalities (e.g. truth tables, state diagrams, etc.) into accurate natural language descriptions. Furthermore, HaVen bridges this gap by using a data augmentation strategy. It synthesizes high-quality instruction-code pairs that match real HDL engineering practices. Our experiments demonstrate that HaVen significantly improves the correctness of Verilog code generation, outperforming state-of-the-art LLM-based Verilog generation methods on VerilogEval and RTLLM benchmark. HaVen is publicly available at https://github.com/Intelligent-Computing-Research-Group/HaVen.
A Machine Learning Model for Crowd Density Classification in Hajj Video Frames
Venue: arXiv cs.CV 2025
Year: 2025

Managing the massive annual gatherings of Hajj and Umrah presents significant challenges, particularly as the Saudi government aims to increase the number of pilgrims. Currently, around two million pilgrims attend Hajj and 26 million attend Umrah making crowd control especially in critical areas like the Grand Mosque during Tawaf, a major concern. Additional risks arise in managing dense crowds at key sites such as Arafat where the potential for stampedes, fires and pandemics poses serious threats to public safety. This research proposes a machine learning model to classify crowd density into three levels: moderate crowd, overcrowded and very dense crowd in video frames recorded during Hajj, with a flashing red light to alert organizers in real-time when a very dense crowd is detected. While current research efforts in processing Hajj surveillance videos focus solely on using CNN to detect abnormal behaviors, this research focuses more on high-risk crowds that can lead to disasters. Hazardous crowd conditions require a robust method, as incorrect classification could trigger unnecessary alerts and government intervention, while failure to classify could result in disaster. The proposed model integrates Local Binary Pattern (LBP) texture analysis, which enhances feature extraction for differentiating crowd density levels, along with edge density and area-based features. The model was tested on the KAU-Smart Crowd 'HAJJv2' dataset which contains 18 videos from various key locations during Hajj including 'Massaa', 'Jamarat', 'Arafat' and 'Tawaf'. The model achieved an accuracy rate of 87% with a 2.14% error percentage (misclassification rate), demonstrating its ability to detect and classify various crowd conditions effectively. That contributes to enhanced crowd management and safety during large-scale events like Hajj.
Self-Supervised Representation Learning with Joint Embedding Predictive Architecture for Automotive LiDAR Object Detection
Venue: arXiv cs.RO 2025
Year: 2025

Recently, self-supervised representation learning relying on vast amounts of unlabeled data has been explored as a pre-training method for autonomous driving. However, directly applying popular contrastive or generative methods to this problem is insufficient and may even lead to negative transfer. In this paper, we present AD-L-JEPA, a novel self-supervised pre-training framework with a joint embedding predictive architecture (JEPA) for automotive LiDAR object detection. Unlike existing methods, AD-L-JEPA is neither generative nor contrastive. Instead of explicitly generating masked regions, our method predicts Bird's-Eye-View embeddings to capture the diverse nature of driving scenes. Furthermore, our approach eliminates the need to manually form contrastive pairs by employing explicit variance regularization to avoid representation collapse. Experimental results demonstrate consistent improvements on the LiDAR 3D object detection downstream task across the KITTI3D, Waymo, and ONCE datasets, while reducing GPU hours by 1.9x-2.7x and GPU memory by 2.8x-4x compared with the state-of-the-art method Occupancy-MAE. Notably, on the largest ONCE dataset, pre-training on 100K frames yields a 1.61 mAP gain, better than all other methods pre-trained on either 100K or 500K frames, and pre-training on 500K frames yields a 2.98 mAP gain, better than all other methods pre-trained on either 500K or 1M frames. AD-L-JEPA constitutes the first JEPA-based pre-training method for autonomous driving. It offers better quality, faster, and more GPU-memory-efficient self-supervised representation learning. The source code of AD-L-JEPA is ready to be released.
Self-Adaptive Ising Machines for Constrained Optimization
Venue: arXiv cs.ET 2025
Year: 2025

Ising machines (IM) are physics-inspired alternatives to von Neumann architectures for solving hard optimization tasks. By mapping binary variables to coupled Ising spins, IMs can naturally solve unconstrained combinatorial optimization problems such as finding maximum cuts in graphs. However, despite their importance in practical applications, constrained problems remain challenging to solve for IMs that require large quadratic energy penalties to ensure the correspondence between energy ground states and constrained optimal solutions. To relax this requirement, we propose a self-adaptive IM that iteratively shapes its energy landscape using a Lagrange relaxation of constraints and avoids prior tuning of penalties. Using a probabilistic-bit (p-bit) IM emulated in software, we benchmark our algorithm with multidimensional knapsack problems (MKP) and quadratic knapsack problems (QKP), the latter being an Ising problem with linear constraints. For QKP with 300 variables, the proposed algorithm finds better solutions than state-of-the-art IMs such as Fujitsu's Digital Annealer and requires 7,500x fewer samples. Our results show that adapting the energy landscape during the search can speed up IMs for constrained optimization.
On the Diagnosis of Flaky Job Failures: Understanding and Prioritizing Failure Categories
Venue: arXiv cs.SE 2025
Year: 2025

The continuous delivery of modern software requires the execution of many automated pipeline jobs. These jobs ensure the frequent release of new software versions while detecting code problems at an early stage. For TELUS, our industrial partner in the telecommunications field, reliable job execution is crucial to minimize wasted time and streamline Continuous Deployment (CD). In this context, flaky job failures are one of the main issues hindering CD. Prior studies proposed techniques based on machine learning to automate the detection of flaky jobs. While valuable, these solutions are insufficient to address the waste associated with the diagnosis of flaky failures, which remain largely unexplored due to the wide range of underlying causes. This study examines 4,511 flaky job failures at TELUS to identify the different categories of flaky failures that we prioritize based on Recency, Frequency, and Monetary (RFM) measures. We identified 46 flaky failure categories that we analyzed using clustering and RFM measures to determine 14 priority categories for future automated diagnosis and repair research. Our findings also provide valuable insights into the evolution and impact of these categories. The identification and prioritization of flaky failure categories using RFM analysis introduce a novel approach that can be used in other contexts.
Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?
Venue: arXiv cs.LG 2025
Year: 2025

Energy communities (ECs) play a key role in enabling local demand shifting and enhancing self-sufficiency, as energy systems transition toward decentralized structures with high shares of renewable generation. To optimally operate them, accurate short-term load forecasting is essential, particularly for implementing demand-side management strategies. With the recent rise of deep learning methods, data-driven forecasting has gained significant attention, however, it remains insufficiently explored in many practical contexts. Therefore, this study evaluates the effectiveness of state-of-the-art deep learning models-including LSTM, xLSTM, and Transformer architectures-compared to traditional benchmarks such as K-Nearest Neighbors (KNN) and persistence forecasting, across varying community size, historical data availability, and model complexity. Additionally, we assess the benefits of transfer learning using publicly available synthetic load profiles. On average, transfer learning improves the normalized mean absolute error by 1.97 percentage points when only two months of training data are available. Interestingly, for less than six months of training data, simple persistence models outperform deep learning architectures in forecast accuracy. The practical value of improved forecasting is demonstrated using a mixed-integer linear programming optimization for ECs with a shared battery energy storage system. For an energy community with 50 households, the most accurate deep learning model achieves an average reduction in financial energy costs of 8.06%. Notably, a simple KNN approach achieves average savings of 8.01%, making it a competitive and robust alternative. All implementations are publicly available to facilitate reproducibility. These findings offer actionable insights for ECs, and they highlight when the additional complexity of deep learning is warranted by performance gains.
On Measuring Unnoticeability of Graph Adversarial Attacks: Observations, New Measure, and Applications
Venue: arXiv cs.LG 2025
Year: 2025

Adversarial attacks are allegedly unnoticeable. Prior studies have designed attack noticeability measures on graphs, primarily using statistical tests to compare the topology of original and (possibly) attacked graphs. However, we observe two critical limitations in the existing measures. First, because the measures rely on simple rules, attackers can readily enhance their attacks to bypass them, reducing their attack "noticeability" and, yet, maintaining their attack performance. Second, because the measures naively leverage global statistics, such as degree distributions, they may entirely overlook attacks until severe perturbations occur, letting the attacks be almost "totally unnoticeable." To address the limitations, we introduce HideNSeek, a learnable measure for graph attack noticeability. First, to mitigate the bypass problem, HideNSeek learns to distinguish the original and (potential) attack edges using a learnable edge scorer (LEO), which scores each edge on its likelihood of being an attack. Second, to mitigate the overlooking problem, HideNSeek conducts imbalance-aware aggregation of all the edge scores to obtain the final noticeability score. Using six real-world graphs, we empirically demonstrate that HideNSeek effectively alleviates the observed limitations, and LEO (i.e., our learnable edge scorer) outperforms eleven competitors in distinguishing attack edges under five different attack methods. For an additional application, we show that LEO boost the performance of robust GNNs by removing attack-like edges.
Towards High-Performance Network Coding: FPGA Acceleration With Bounded-value Generators
Venue: arXiv cs.AR 2025
Year: 2025

Network coding enhances performance in network communications and distributed storage by increasing throughput and robustness while reducing latency. Batched Sparse (BATS) codes are a class of capacity-achieving network codes, but their practical applications are hindered by their structure, computational intensity, and power demands of finite field operations. Most literature focuses on algorithmic-level techniques to improve coding efficiency. Optimization with an algorithm/hardware co-designing approach has long been neglected. Leveraging the unique structure of BATS codes, we first present CS-BATS, a hardware-friendly variant. Next we propose a simple but effective bounded-value generator, to reduce the size of a finite field multiplier by up to 70%. Finally, we report on a scalable and resource-efficient FPGA-based network coding accelerator that achieves a throughput of 27 Gbps, a speedup of more than 300 over software.