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In construction quality monitoring, accurately detecting and segmenting cracks in concrete structures is paramount for safety and maintenance. Current convolutional neural networks (CNNs) have demonstrated strong performance in crack segmentation tasks, yet they often struggle with complex backgrounds and fail to capture fine-grained tubular structures fully. In contrast, Transformers excel at capturing global context but lack precision in detailed feature extraction. We introduce DSCformer, a novel hybrid model that integrates an enhanced Dynamic Snake Convolution (DSConv) with a Transformer architecture for crack segmentation to address these challenges. Our key contributions include the enhanced DSConv through a pyramid kernel for adaptive offset computation and a simultaneous bi-directional learnable offset iteration, significantly improving the model's performance to capture intricate crack patterns. Additionally, we propose a Weighted Convolutional Attention Module (WCAM), which refines channel attention, allowing for more precise and adaptive feature attention. We evaluate DSCformer on the Crack3238 and FIND datasets, achieving IoUs of 59.22\% and 87.24\%, respectively. The experimental results suggest that our DSCformer outperforms state-of-the-art methods across different datasets.

Assessment of the sympathetic nervous system (SNS) is one of the major approaches for studying affective states. Skin nerve activity (SKNA) derived from high-frequency components of electrocardiogram (ECG) signals has been a promising surrogate for assessing the SNS. However, current SKNA analysis tools have shown high variability across study protocols and experiments. Hence, we propose a time-varying spectral approach based on SKNA to assess the SNS with higher sensitivity and reliability. We collected ECG signals at a sampling frequency of 10 KHz from sixteen subjects who underwent various SNS stimulations. Our spectral analysis revealed that frequency bands between 150 - 1,000 Hz showed significant increases in power during SNS stimulations. Using this information, we developed a time-varying index of sympathetic function measurement based on SKNA, termed, Time-Varying Skin Nerve Activity (TVSKNA). TVSKNA is calculated in three steps: time-frequency decomposition, reconstruction using selected frequency bands, and smoothing. TVSKNA indices exhibited generally higher Youden's J, balanced accuracy, and area under the receiver operating characteristic curve, indicating higher sensitivity. The coefficient of variance was lower with TVSKNA indices for most SNS tasks. TVSKNA can serve as a highly sensitive and reliable marker of quantitative assessment of sympathetic function, especially during emotion and stress., Comment: Submitted to IEEE Transactions on Affective Computing for publication consideration

Detecting mitigating and correcting errors in quantum control is among the most pertinent contemporary problems in quantum technologies. We consider three of the most common bosonic error correction codes -- the CLY, binomial and dual rail and compare their performance under typical errors in bosonic systems. We find that the dual rail code shows the best performance. We also develop a new technique for error mitigation in quantum control. We consider a quantum system with large Hilbert space dimension, e.g., a qudit or a multi-qubit system and construct two $2- $ dimensional subspaces -- a code space, $\mathcal C = \text{span}\{|\bar{0}\rangle, |\bar{1}\rangle\}$ where the logical qubit is encoded and a ``proxy'' space $\mathcal P = \text{span}\{|\bar{0}'\rangle, |\bar{1}'\rangle\}$. While the qubit (i.e., $\mathcal C$) can be a part of a quantum circuit, the proxy (i.e., $\mathcal P$) remains idle. In the absence of errors, the quantum state of the proxy qubit does not evolve in time. If $\mathcal E$ is an error channel acting on the full system, we consider its projections on $\mathcal C$ and $\mathcal P$ represented as pauli transfer matrices $T_{\mathcal E}$ and $T'_{\mathcal E}$ respectively. Under reasonable assumptions regarding the origin of the errors, $T_{\mathcal E}$ can be inferred from $T'_{\mathcal E}$ acting on the proxy qubit and the latter can be measured without affecting the qubit. The latter can be measured while the qubit is a part of a quantum circuit because, one can perform simultaneous measurements on the logical and the proxy qubits. We use numerical data to learn an \textit{affine map} $\phi$ such that $T_{\mathcal E} \approx \phi(T'_{\mathcal E})$. We also show that the inversion of a suitable proxy space's logical pauli transfer matrix can effectively mitigate the noise on the two modes bosonic system or two qudits system., Comment: 7+4 pages, 4+2 figures

Sparse Mixture-of-Experts (SMoE) models represent a significant breakthrough in large language model development. These models enable performance improvements without a proportional increase in inference costs. By selectively activating a small set of parameters during task execution, SMoEs enhance model capacity. However, their deployment remains challenging due to the substantial memory footprint required to accommodate the growing number of experts. This constraint renders them less feasible in environments with limited hardware resources. To address this challenge, we propose Hierarchical Clustering for Sparsely activated Mixture of Experts (HC-SMoE), a task-agnostic expert merging framework that reduces SMoE model parameters without retraining. Unlike previous methods, HC-SMoE employs hierarchical clustering based on expert outputs. This approach ensures that the merging process remains unaffected by routing decisions. The output-based clustering strategy captures functional similarities between experts, offering an adaptable solution for models with numerous experts. We validate our approach through extensive experiments on eight zero-shot language tasks and demonstrate its effectiveness in large-scale SMoE models such as Qwen and Mixtral. Our comprehensive results demonstrate that HC-SMoE consistently achieves strong performance, which highlights its potential for real-world deployment., Comment: Code: https://github.com/wazenmai/HC-SMoE

Quantum computing, ultra-low-noise sensing, and high-energy physics experiments often rely on superconducting circuits or semiconductor qubits and devices operating at deep cryogenic temperatures (4K and below). Photonic integrated circuits and interconnects have been demonstrated for scalable communications and optical domain transduction in these systems. Due to energy and area constraints, many of these devices need enhanced light-matter interaction, provided by photonic resonators. A key challenge, however, for using these resonators is the sensitivity of resonance wavelength to process variations and thermal fluctuations. While thermo-optical tuning methods are typically employed at room temperature to mitigate this problem, the thermo-optic effect is ineffective at 4K. To address this issue, we demonstrate a non-volatile approach to tune the resonance of photonic resonators using integrated phase-change materials (PCMs) at cryogenic temperatures. In this work, we report a 10Gb/s free-carrier dispersion based resonant photonic modulator that can be tuned in a non-volatile fashion at sub-4K temperatures using a commercial silicon photonics process. This method paves the way for realizing scalable cryogenic integrated photonics with thousands of resonant devices for quantum and high-energy physics applications.

Simulating strongly coupled gauge theories at finite temperature and density is a longstanding challenge in nuclear and high-energy physics that also has fundamental implications for condensed matter physics. In this work, we investigate the utility of minimally entangled typical thermal state (METTS) approaches to facilitate both classical and quantum computational studies of such systems. METTS techniques combine classical random sampling with imaginary time evolution, which can be performed on either a classical or a quantum computer, to estimate thermal averages of observables. We study the simplest model of a confining gauge theory, namely $\mathbb{Z}_2$ gauge theory coupled to spinless fermionic matter in 1+1 dimensions, which can be directly mapped to a local quantum spin chain with two- and three-body interactions. We benchmark both a classical matrix-product-state implementation of METTS and a recently proposed adaptive variational approach to METTS that is a promising candidate for implementation on near-term quantum devices, focusing on the equation of state as well as on various measures of fermion confinement. Of particular importance is the choice of basis for obtaining new METTS samples, which impacts both the classical sampling complexity (a key factor in both classical and quantum simulation applications) and complexity of circuits used in the quantum computing approach. Our work sets the stage for future studies of strongly coupled gauge theories with both classical and quantum hardware., Comment: 14 pages, 8 figures

Within the evolving domain of quantum computational chemistry, the Variational Quantum Eigensolver (VQE) has been developed to explore not only the ground state but also the excited states of molecules. In this study, we compare the performance of Variational Quantum Deflation (VQD) and Subspace-Search Variational Quantum Eigensolver (SSVQE) methods in determining the low-lying excited states of $LiH$. Our investigation reveals that while VQD exhibits a slight advantage in accuracy, SSVQE stands out for its efficiency, allowing the determination of all low-lying excited states through a single parameter optimization procedure. We further evaluate the effectiveness of optimizers, including Gradient Descent (GD), Quantum Natural Gradient (QNG), and Adam optimizer, in obtaining $LiH$'s first excited state, with the Adam optimizer demonstrating superior efficiency in requiring the fewest iterations. Moreover, we propose a novel approach combining Folded Spectrum VQE (FS-VQE) with either VQD or SSVQE, enabling the exploration of highly excited states. We test the new approaches for finding all three $H_4$'s excited states. Folded Spectrum SSVQE (FS-SSVQE) can find all three highly excited states near $-1.0$ Ha with only one optimizing procedure, but the procedure converges slowly. In contrast, although Folded spectrum VQD (FS-VQD) gets highly excited states with individual optimizing procedures, the optimizing procedure converges faster., Comment: 10 pages, 8 figures

Crowd counting and localization have become increasingly important in computer vision due to their wide-ranging applications. While point-based strategies have been widely used in crowd counting methods, they face a significant challenge, i.e., the lack of an effective learning strategy to guide the matching process. This deficiency leads to instability in matching point proposals to target points, adversely affecting overall performance. To address this issue, we introduce an effective approach to stabilize the proposal-target matching in point-based methods. We propose Auxiliary Point Guidance (APG) to provide clear and effective guidance for proposal selection and optimization, addressing the core issue of matching uncertainty. Additionally, we develop Implicit Feature Interpolation (IFI) to enable adaptive feature extraction in diverse crowd scenarios, further enhancing the model's robustness and accuracy. Extensive experiments demonstrate the effectiveness of our approach, showing significant improvements in crowd counting and localization performance, particularly under challenging conditions. The source codes and trained models will be made publicly available.

Robotic kitting has attracted considerable attention in logistics and industrial settings. However, existing kitting methods encounter challenges such as low precision and poor efficiency, limiting their widespread applications. To address these issues, we present a novel kitting framework that improves both the precision and computational efficiency of complex kitting tasks. Firstly, our approach introduces a fine-grained orientation estimation technique in the picking module, significantly enhancing orientation precision while effectively decoupling computational load from orientation granularity. This approach combines an SO(2)-equivariant network with a group discretization operation to preciously predict discrete orientation distributions. Secondly, we develop the Hand-tool Kitting Dataset (HKD) to evaluate the performance of different solutions in handling orientation-sensitive kitting tasks. This dataset comprises a diverse collection of hand tools and synthetically created kits, which reflects the complexities encountered in real-world kitting scenarios. Finally, a series of experiments are conducted to evaluate the performance of the proposed method. The results demonstrate that our approach offers remarkable precision and enhanced computational efficiency in robotic kitting tasks., Comment: 8 pages, 6 figures

In this article, we study the stationary Boltzmann equation with the incoming boundary condition for the hard potential cases. Assuming the smallness of the domain and a suitable normal curvature condition on the boundary, we find a suitable solution space which is a proper subset of the $W^{1,p}$ space for $1 \leq p <3$.

Our primary objective is to conduct a brief survey of various classical and quantum neural net sequence models, which includes self-attention and recurrent neural networks, with a focus on recent quantum approaches proposed to work with near-term quantum devices, while exploring some basic enhancements for these quantum models. We re-implement a key representative set of these existing methods, adapting an image classification approach using quantum self-attention to create a quantum hybrid transformer that works for text and image classification, and applying quantum self-attention and quantum recurrent neural networks to natural language processing tasks. We also explore different encoding techniques and introduce positional encoding into quantum self-attention neural networks leading to improved accuracy and faster convergence in text and image classification experiments. This paper also performs a comparative analysis of classical self-attention models and their quantum counterparts, helping shed light on the differences in these models and their performance., Comment: 6 pages, 10 figures, accepted as a COMSNETS paper

We study the incoming boundary value problem for the stationary linearized Boltzmann equation in bounded convex domains. The geometry of the domain has a dramatic effect on the space of solutions. We prove the existence of solutions in $W^{1,p}$ spaces for $1 \leq p<2$ for small domains. In contrast, if we further assume the positivity of the Gaussian curvature on the boundary, we prove the existence of solutions in $W^{1, p}$ spaces for $1 \leq p < 3$ provided that the diameter of the domain is small enough. In both cases, we provide counterexamples in the hard sphere model; a bounded convex domain with a flat boundary for $p = 2$, and a small ball for $p = 3$., Comment: 34 pages, no figures

Phonons traveling in solid-state devices are emerging as a universal excitation that can couple to different physical systems through mechanical interaction. At microwave frequencies and in solid-state materials, phonons have a similar wavelength to optical photons, enabling them to interact efficiently with light and produce strong optomechanical effects that are highly desirable for classical and quantum signal transduction between optical and microwave. It becomes conceivable to build optomechanical integrated circuits (OMIC) that guide both photons and phonons and interconnect discrete photonic and phononic devices. Here, we demonstrate an OMIC including an optomechanical ring resonator (OMR), in which infrared photons and GHz phonons co-resonate to induce significantly enhanced interconversion. The OMIC is built on a hybrid platform where wide bandgap semiconductor gallium phosphide (GaP) is used as the waveguiding material and piezoelectric zinc oxide (ZnO) is used for phonon generation. The OMR features photonic and phononic quality factors of $>1\times10^5$ and $3.2\times10^3$, respectively, and resonantly enhances the optomechanical conversion between photonic modes to achieve an internal conversion efficiency $\eta_i=(2.1\pm0.1)%$ and a total device efficiency $\eta_{tot}=0.57\times10^{-6}$ at a low acoustic pump power of 1.6 mW. The efficient conversion in OMICs enables microwave-optical transduction for many applications in quantum information processing and microwave photonics., Comment: 11 pages, 4 figures

In this paper, an improved version of QTS (Quantum-inspired Tabu Search) has been proposed, which enhances the utilization of population information, called "amplitude-ensemble" QTS (AE-QTS). This makes AE-QTS more similar to the real quantum search algorithm, Grover Search Algorithm, in abstract concept, while keeping the simplicity of the algorithm. Later, we demonstrate the AE-QTS on the classical combinatorial optimization 0/1 knapsack problem. Experimental results show that the AE-QTS outperforms other algorithms, including the QTS, by at least an average of 20% in all cases and even by 30% in some cases. Even as the problem complexity increases, the quality of the solutions found by our method remains superior to that of the QTS. These results prove that our method has better search performance., Comment: 7 pages, 7 figures

The transport of conserved quantities like spin and charge is fundamental to characterizing the behavior of quantum many-body systems. Numerically simulating such dynamics is generically challenging, which motivates the consideration of quantum computing strategies. However, the relatively high gate errors and limited coherence times of today's quantum computers pose their own challenge, highlighting the need to be frugal with quantum resources. In this work we report simulations on quantum hardware of infinite-temperature energy transport in the mixed-field Ising chain, a paradigmatic many-body system that can exhibit a range of transport behaviors at intermediate times. We consider a chain with $L=12$ sites and find results broadly consistent with those from ideal circuit simulators over 90 Trotter steps, containing up to 990 entangling gates. To obtain these results, we use two key problem-tailored insights. First, we identify a convenient basis$\unicode{x2013}$the Pauli $Y$ basis$\unicode{x2013}$in which to sample the infinite-temperature trace and provide theoretical and numerical justifications for its efficiency relative to, e.g., the computational basis. Second, in addition to a variety of problem-agnostic error mitigation strategies, we employ a renormalization strategy that compensates for global nonconservation of energy due to device noise. We discuss the applicability of the proposed sampling approach beyond the mixed-field Ising chain and formulate a variational method to search for a sampling basis with small sample-to-sample fluctuations for an arbitrary Hamiltonian. This opens the door to applying these techniques in more general models., Comment: 10 + 11 pages, 4 + 8 figures. V2 adds a variational generalization of the method to other models section and an appendix for calculating fluctuations over Haar random product states

We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech recognition output rescoring. Although pretrained language models (LMs) like BERT have shown superior performance in second-pass rescoring, the high computational cost of scaling up the pretraining stage and adapting the pretrained models to specific domains limit their practical use in rescoring. Here we present a method based on low-rank decomposition to train a rescoring BERT model and adapt it to new domains using only a fraction (0.08%) of the pretrained parameters. These inserted matrices are optimized through a discriminative training objective along with a correlation-based regularization loss. The proposed low-rank adaptation Rescore-BERT (LoRB) architecture is evaluated on LibriSpeech and internal datasets with decreased training times by factors between 5.4 and 3.6., Comment: Accepted to IEEE ASRU 2023. Internal Review Approved. Revised 2nd version with Andreas and Huck. The first version is in Sep 29th. 8 pages

In this study, we re-evaluated the superconducting properties of LK-99. The LK-99 samples were synthesized using the process proposed by the original Korean team. Additionally, we examined whether the results of the Korean team are related to Cu$_2$S by using ammonia solution (NH$_3$-H$_2$O) to remove Cu$_2$S . Through x-ray diffraction (XRD) analysis, a distinct Cu$_2$S phase was identified in the LK-99 samples. A subsequent treatment using an ammonia solution effectively eliminated this phase. The appearance of blue Cu$^{+2}$ ions in the solution and the elimination of the Cu$_2$S peak in XRD support the conclusion. The magnetic and electrical properties of LK-99 with and without Cu$_2$S postulate that the superconducting-like behavior in LK-99 predominantly arises from a transition in resistivity due to the influence of Cu$_2$S . As such, LK-99 is better classified as a diamagnetic semiconductor than a room-temperature superconductor. The room-temperature superconductors still require further research.