Recent advancements in Convolutional Neural Networks (CNNs) have led to significant increases in computational complexity and power consumption. Approximate computing, especially through the use of approximate multipliers, has emerged as a promising approach to realize the reduction of power, area and delay by sacrificing some computational precision. Approximate multipliers produce results with errors, thus their effects to CNN accuracies need to be evaluated. However, the lack of efficient software tools for simulating approximate multipliers hinders their widespread adoption. This paper proposes ApproxTorch 2.0, a high perfomance simulation framework based on PyTorch and CUDA for evaluating 8-bit signed integer based approximate multipliers for CNNs. To realize higher performance, CUDA and Nvidia GPUs are used to accelerate the simulation process. Two approximate layers are implemented: 2D Convolution layer and Linear layer. The simulation of approximate multiplications is done by accessing pre-defined look-up tables (LUTs) stored in GPU memory. By dedicated optimization in the customized CUDA approximate GEMM kernel, ApproxTorch 2.0 achieves 171× speedup compared with CPU simulation and 17.2× speedup compared with previous ApproxTorch 1.0 on running ResNet50. Lastly, gradient estimation is added to support the retraining of approximate CNNs with quantized values and proved to be effective for recovering the accuracy loss after approximation.

A distributed network system is a dynamical system in which multiple subsystems are connected through a physical/ communication network. There are many applications such as power systems. In this paper, we propose a method of event-triggered model predictive control for distributed network systems with switching topologies. In the proposed method, each subsystem is controlled by state-feedback using its state and the state of neighbors. Its gain is calculated by the upper controller (supervisor), only when a certain event-triggering condition is satisfied. The design problem of state-feedback gains is reduced to an LMI (linear matrix inequality) optimization problem. Finally, the proposed method is demonstrated by two numerical examples.

Accurately predicting energy consumption of the heating ventilating and air conditioning system is crucial for achieving building energy conservation. To overcome the limitations of traditional methods in terms of generalization capacity, this study proposed a novel prediction method for the HVAC system energy consumption based on the ensemble learning model. In this method, the operation variables of chilled and cooling water systems (i.e., supply/return temperature and flowrate) and environmental variables (i.e., temperature and humidity) are utilized as inputs, three single models (i.e., support vector regression, extreme learning machine, and decision tree) are employed as the base models, and the Gaussian process regression is utilized as the stacked model. Experiments are conducted on the HVAC system of the public building, and the performance of the ensemble learning model is verified using practical measured data. Results show that the ensemble learning model is superior to the single models in predicting cooling capacity, heat dissipation and total power. More importantly, the ensemble learning model can provide reliable confidence intervals, effectively quantifying the uncertainty of the prediction results.

As the global energy mix shifts toward renewable energy, microgrids, as a key enabler for efficient energy utilization and the integration of distributed generation, face complex and ever-changing energy management challenges. The uncertainty of the output of distributed power sources (such as solar and wind power), the interactions between devices, and the multi-objective optimization requirements within microgrids make it difficult for traditional energy management methods to achieve accurate forecasting and efficient scheduling. To address these challenges, this paper proposes the TGD-RL model, an innovative approach that combines deep learning techniques. This model integrates three advanced techniques: the Transformer, the Graph Neural Network (GNN), and the Deep Q-Network (DQN). The Transformer module utilizes a multi-head attention mechanism to capture long-term temporal dependencies in microgrid data, making it suitable for processing data with time-series characteristics. The GNN uses graph convolution operations and node embedding techniques to model the topology and dynamic interactions of each device in the microgrid. The DQN uses a state-action-reward mechanism to continuously optimize energy management strategies and achieve efficient scheduling decisions. Experimental results on two public datasets, PJM and MISO electricity market price data and NREL wind and solar data, demonstrate that the TGD-RL model outperforms other baseline models in energy forecast accuracy, achieving mean absolute percentage errors (MAPEs) of 6.5% and 5.8%, respectively, representing reductions of 36.3% to 39.0% compared to the optimal baseline model. The operating costs of the microgrids were reduced to 1,250 yuan and 1,180 yuan, respectively, representing decreases of 17.4% to 37.8%, while energy self-sufficiency increased to 78% and 82%, respectively, representing increases of 10.0% to 26.0%. Ablation experiments further validated the essential role of each component in the model's performance. This research demonstrates that the TGD-RL model can effectively address complex energy management issues in microgrids, providing a new technical path for improving the economic efficiency, stability, and energy self-sufficiency of microgrids. It also holds significant implications for the development of smart grids and the efficient use of renewable energy.

This paper proposes a distributed dual decomposition algorithm for solving mixed-integer linear programming (MILP) problems in multi-agent systems. In the proposed approach, a MILP problem is transformed into an approximated problem by linear programming relaxation, which enables each agent to independently solve their local subproblems. The theoretical analysis provides guarantees on both the feasibility error bounds and the optimality gap of the solutions obtained by the proposed method. The effectiveness of the proposed method is shown through a numerical example of a fairness-aware multiple traveling salesman problem.

In this paper, we propose applying bit-serial arithmetic units to reduce the circuit area of neural network inference engines. Additionally, we propose applying datapath pipelining and zero skipping to significantly reduce the required clock cycles. In recent years, studies have demonstrated the efficacy of neural networks in voice and image recognition applications; however, an extremely large number of multiply-and-accumulate operations are required in order to achieve high accuracy. Therefore, we explored the application of bit-serial arithmetic units to these operations to reduce circuit area. Bit-serial arithmetic is a method of sequentially calculating multi-bit data by inputting and outputting one bit at a time, which enables the reduction of the circuit area and amount of wiring. The disadvantage of this method is that it requires a large number of clock cycles. For example, a bit-serial multiplier with an input of N bits requires 2N cycles. In this study, pipeline processing and zero skipping were applied to reduce the required clock cycles. Zero skipping reduces the required clock cycles by skipping the calculation of an input activation when the value of that activation is zero. We propose two methods of zero skipping: reactive zero skipping, which checks whether activation is zero before the bit-serial operation starts, and proactive zero skipping, which reads ahead, examining subsequent memory locations, during the bit-serial operation and skips all consecutive zeros in one step. The effectiveness of zero skipping is highly dependent on the ratio of zeros in the input activation. In a convolutional neural network (CNN) that uses a rectified linear unit (ReLU) as the activation function, the input activation of the second and subsequent convolution layers has a high ratio of zeros. To further increase sparsity and improve the effectiveness of zero skipping, we propose setting the dropout rate during training as high as possible without affecting the recognition accuracy. We implemented a CNN using the proposed bit-serial arithmetic units and a CNN using conventional parallel arithmetic units, and compared their performances. The former exhibited a 22.9% smaller circuit area than the latter. In addition, the increase in the number of required clock cycles was limited to 2.12 times, and the clock period was reduced by 47.4%, resulting in a 7.8% reduction in runtime.

Modulation is crucial for multiplexing, reducing bandwidth, and improving transmission efficiency. By incorporating neural networks, we can optimize modulation for specific communication channels. We propose two neural network optimized modulation schemes: a regular constellation mapping for 16-ary modulation and an irregular mapping for 2^m-ary modulation. These maintain gradient flow during backpropagation, allowing adjustments to constellation points to minimize bit error rates (BER) while keeping system complexity manageable. The results show our polar-coded modulation schemes outperform traditional uniform QAM with about a 0.5 dB gain under low SNR. Additionally, these schemes can also be applied to LDPC-coded modulation systems to improve BER performance.

Specific emitter identification plays a crucial role in the field of information security. To improve identification performance in complex electromagnetic environments, this letter proposes a dual-branch network, FADC-Transformer, which combines frequency-aware dynamic convolution (FADC) and Transformer. It can adaptively fuse frequency-domain features from FADC and time-domain features from the Transformer. Specifically, FADC introduces a frequency-band attention mechanism and dynamic kernel generation, which can dynamically adjust convolutional kernel parameters according to the inputs, resulting in better robustness. Experimental results show that the accuracy of FADC is improved by 16% compared with static convolution, and the dual-branch structure significantly enhances identification performance.

Private information retrieval (PIR) is a mechanism for retrieval of a message while keeping its index secret. Tian's and Sun's methods are representative PIR methods, which are mainly evaluated in terms of download rate. In this paper, we propose a new method based on Shamir's secret sharing. Furthermore, a comprehensive evaluation including communication cost, computation time, and message size is performed; our results demonstrate that the proposed method outperforms conventional ones.

In this article, the authors propose an electronic, linear, continuously tunable phase shifter (PS) with an adjustable phase range of leading +180° to delay -180° within the phase shift bandwidth (PSBW) and a maximum adjustable phase range of +240° to -240° within the half power bandwidth (HPBW). This PS enables flexible design of its operating frequency and PSBW and maintaining a constant linear slope of -116.6° per susceptance (jb) for any design PSBW. The PS consists of two different wavelengths of transmission lines and four identical LC parallel resonators (LC tanks), with phase variation achieved by adjusting the capacitance values, susceptance values, or resonance frequencies within the LC tanks. The authors also present an analysis of the electrical theory underlying this PS. Theoretically, the transmission coefficient T ranges from 0dB to -0.973dB, and the reflection coefficient Γ peaks at -6.99dB. Additionally, the authors provide design guidelines for the PS. Based on these guidelines and available lumped components, a PS operating at 3GHz with a PSBW of 400MHz was developed. Finally, measurement results confirm a generally consistent between theoretical predictions and practical implementation. This demonstrates that the proposed PS can be designed with arbitrary center frequencies and PSBW, exhibiting excellent S-parameters and linear continuously tunable phase shifting performance.

Although a sampling rate and a resolution of a time-interleaved A/D converter (ADC) have improved remarkably by recent advance of digital calibration, it is still difficult to achieve an effective resolution larger than 12 bits for sampling rate higher than 1 GS/s. This limitation is mainly caused by higher-order effect of sampling-timing mismatch among unit converters. To overcome the fundamental limitation, fully digital calibration of a time-interleaved ADC with cascaded higher-order sampling-timing correction is presented. In the proposed correction method, by using a reference ADC as an only additional analog component, analog tuning is eliminated, allowing mismatch effects to be corrected solely through post-digital processing. Due to its fully digital nature and unlimited correction in principle provided by the cascaded processing, accuracy is only limited by digital implementation cost, which is mitigated significantly with CMOS scaling. The extension to a sub-sampling time-interleaved ADC is also presented for a broad range of applications. Effectiveness of the proposed calibration was verified by extensive simulation with the 3rd-order sampling-timing correction for both standard and sub-sampling time-interleaved ADCs as well as measurement of a prototype time-interleaved ADC, which proved 11.5-bit effective resolution (71.2-dB SNDR) at 1GS/s with the 2nd-order correction.

This study examines data-driven attack detectors that utilize maximum likelihood estimation for cyber-physical systems. The proposed methodology optimally estimates the input-output trajectories of these systems using both pre-experimental data and real-time measurements, in accordance with the principles of maximum likelihood. The attack detector identifies the presence of an attack by comparing the estimated trajectories with actual measured trajectories. The theoretical contributions of this study include the demonstration of a fundamental limitation, specifically, the set of undetectable attacks when disturbances are negligible. Furthermore, when disturbances can not be disregarded, the proposed method can detect attacks with a specified false rate based on the detectable condition derived through a χ² test.

Many researchers expect to apply microrobots in narrow environments for tasks such as exploration and maintenance. However, digital control, which is the primary robot control method, faces computational cost and circuit miniaturization challenges. The authors have been studying neuromorphic circuits, which mimic biological neural functions for robot control, acting as a central pattern generator (CPG) as a driving circuit to perform the walking motion. Previously, we constructed a neuromorphic circuit on an integrated circuit and we successfully implemented the neuromorphic integrated circuit in millimeter-scale microrobots. However, the microrobot lacked sensory input, which prevented the robot from adapting to the robot's movement in response to external environmental changes. This paper proposes a neuromorphic integrated circuit capable of adaptively switching the gait of an insect-type microrobot in response to light stimuli. The proposed circuit incorporates a receptor cell model that mimics biological sensory neurons, enabling the transformation of external light input into electrical signals using photovoltaic cells (PV cells). The electrical signals are processed through synaptic and CPG models to switch locomotion patterns. The authors systematically measured gait patterns to evaluate the operating range while varying the power supply voltage of the receptor cell model and the output voltage of PV cells. We observed the results, which clarified the regions where stable wave gait, tripod gait, or unstable outputs. Furthermore, we measured the I-V characteristics of a PV cell. Also, we confirmed that its output voltage matches the designed switching threshold of the proposed circuit, enabling optical control without additional signal conditioning. These findings demonstrate that the proposed circuit can be a low-power, sensor-responsive gait controller for future autonomous microrobots.

We propose a method for reliability research at the circuit-level chips. The degradation model used combines the Wiener process and the Arrhenius acceleration model. The degradation data analyzed is the pin leakage current sampled during the constant stress acceleration degradation test of Flash memory chips at different temperatures. This method has low testing costs while providing a comprehensive reflection of the degradation conditions of the tested samples. In this work, we established the model through mathematical derivation, and then estimated the distribution of the model parameters by generating bootstrap samples. Then, under the premise of completing the model accuracy test, we completed the estimation of the remaining life of the sample through Monte Carlo simulation.

Integrated sensing and communication (ISAC) enables simultaneous wireless communication and environmental sensing, where joint beamforming design is key to balancing both tasks. To address the complexity and trade-off challenges of joint beamforming design in multi-user multi-target ISAC systems, this letter proposes the cross-attentive Pareto transformer (CAPT), an end-to-end deep learning framework that integrates enhanced spatial embeddings and cross-attention to jointly optimize beamforming. By leveraging Pareto multi-task learning, CAPT efficiently generates the Pareto front of solutions in a single inference. Simulation results show that CAPT achieves better Pareto front quality and generalization than weighted minimum mean square error (WMMSE) and convolutional neural network (CNN)-based baselines.

Compression by Substring Enumeration (CSE), which is one of the lossless data compression algorithms, and various versions of CSE have been proposed. In encoding of CSE, substrings of given fixed length and their frequencies within circular string for an input string are output as a codeword. The circular string is made by connecting the first symbol and the last symbol of an input string. In decoding of CSE, the circular string is reconstructed from its substrings and their frequencies. Furthermore, the minimum length of substrings for which the decoding does reconstruct the circular string has been proved, together with a reconstruction algorithm. However, the algorithm requires substrings to have no errors.

Therefore, in this paper, we propose an error correcting algorithm which can detect one of the substrings having one bit-flipping, one bit-insertion, or one bit-deletion error and correct the bit error. By applying the proposed algorithm, we can reconstruct a circular string from a set of substrings and their frequencies including only one substring which has at most one bit error.

Cayley hash functions, which are hash functions based on walks on Cayley graphs of groups, are a well-studied class of hash functions with provable security (under assumptions on computational hardness of some group-theoretical problems). In a recent work by Aikawa, Jo, and Satake (Transactions on Mathematical Cryptology, 2023), they proposed a variant of Cayley hash functions called left-right Cayley hash functions, whose design intended the problem of finding a collision to be as difficult as the problem of finding collisions of two Cayley hash functions simultaneously. In this paper we show, as opposed to the expectation, that finding a collision of their hash function is reduced to finding a collision of a single Cayley hash function. We also propose a possible countermeasure against this issue.

With the development of artificial intelligence technology, the demand for multi-domain pose estimation in complex scenes is growing. The existing single model solution faces the problem of insufficient ability in cross domains of multi-scene, which is caused by the different numbers of keypoints and complex features of samples. To solve this problem, we propose a new method that has a four-stage pose estimation framework. This framework applies methods such as object detection, domain classification and pose estimation. As the experimental results show, on the testing set of mixed domains, the accuracy of our method is 5.1% higher than the best one of the existing methods, which ensures high performance pose estimation in many applications.

This paper introduces a latent port-Hamiltonian framework using deep learning to improve the robustness for vision-based control. Although reinforcement learning and deep learning are promising solutions to control system states with differentiable policies, physics-free methods usually suffer from unstable and low-confident results with respect to the system dynamics. We propose a vision-based control architecture by employing a port-Hamiltonian model in the latent space of autoencoder (AE) to achieve physically consistent control. Specifically, we apply a variational autoencoder (VAE) to encode visual observations into a low-dimensional latent space, where the port-Hamiltonian energy structure is learned. Moreover, we introduce AI-Pontryagin, which generates control signals similar to optimal control inputs through a neural network inspired by optimal control theory. The experimental results show that our method achieves more accurate and stable control performance compared to baseline approaches.

In order to terminate iterations earlier of the linear programming (LP) decoder based on the alternating direction method of multipliers (ADMM) (ADMM-LP), this letter proposes an early termination (ET) criterion based on the number of parity-check constraints of iterative solution vector. The criterion can be used to detect erroneous codewords in advance and thereby avoid unnecessary iterations, without increasing the additional computational complexity. Compared with existing termination criteria of ADMM-LP decoding algorithm, the proposed ET criterion can significantly reduce the average number of iterations at low signal-to-noise ratio (SNR) regions with little effect on the decoding performance.

In stabilizer-based quantum secret sharing schemes, it is known that some shares can be distributed to participants before a secret is given to the dealer. This distribution is known as advance sharing. It is already known that a set of shares is advance shareable only if it is a forbidden set. However, it was not known whether any forbidden set is advance shareable. We provide an example of a set of shares such that it is a forbidden set but is not advance shareable in the previous scheme. Furthermore, we propose an advance sharing scheme for stabilizer-based quantum secret sharing of quantum secrets such that any forbidden set is advance shareable.

Classic McEliece has gathered attention as a candidate in NIST post-quantum cryptography standardization. However, it suffers from high demands on the decryption algorithm, making it unsuitable for resource-constrained devices. In this paper, we propose a novel implementation method, named giant footprint sharing, that reduces memory size during decryption in Classic McEliece. The decryption algorithm processes a large number of intermediate variables computed from a secret key in memory. The giant footprint sharing identifies the largest variable among them and allocates a memory-sharing structure to store it, thereby reducing the overall memory size regardless of the implementation platform. The giant footprint sharing can also be combined with existing acceleration techniques, such as fast Fourier transformation. We evaluate Classic McEliece with the giant footprint sharing on the Arm Cortex-M33 CPU and show that it reduces memory size by up to 46% without significant degradation in computation time compared with the existing fast-implementation by Chen et al. (at TCHES 2021). Extensive experiments with the giant footprint sharing further reveal that it maintains a constant memory size regardless of the compiler optimization, and it also achieves an optimal balance in the trade-off between memory size and computation time. The giant footprint sharing is remarkable for any scheme, that contains a large-scale matrix computation and the life cycle for each variable is limited.

A Periodic-Finite-Type shift (PFT) is a set of bi-infinite sequences that prohibit the appearance of forbidden words in a periodic manner. More precisely, a PFT $\cX_{\{T, \tilde \F\}}$ is a set of bi-infinite sequences $\x$ characterized by a period $T\in \N$ and a family $\tilde\F=(\tilde\cF^{(0)},\tilde\cF^{(1)}, \cdots, \tilde\cF^{(T-1)})$ of indexed finite sets of forbidden words $\tilde\cF^{(0)},\tilde\cF^{(1)}, \cdots, \tilde\cF^{(T-1)}$, so that the $r$-shifted sequence $\sigma^r(\x)$ of $\x$ does not contain words in $\tilde\cF^{(i \mod T)}$ at position $i\in \Z$. The study on PFTs is strongly related to the study on constrained systems with unconstrained positions, which have the property as both error-correcting codes and constrained codes.

The capacity of a PFT is an important value that gives us the maximum coding rate when a random sequence is encoded to a sequence in the PFT. In this paper, we derive the capacity of a PFT in two ways, using the fact that an arbitrary family $\tilde \F$ is transformed into a family $\F=(\cF^{(0)},\emptyset \cdots, \emptyset)$, where each forbidden word in $\cF^{(0)}$ has the same length $k$, so that $\cX_{\{T, \tilde \F\}}=\cX_{\{T, \F\}}$. When $k \le T$, the first proof derives the capacity directly from the definition, and the other proof does from block partitioning of the adjacency matrix of a certain graph representing $\cX_{\{T, \F\}}$. We also present a partial result on the capacity when $k>T$.

Accurately predicting demand and adapting to rapid market changes are common difficulties for enterprise supply chain management. Traditional forecasting methods and even many existing deep learning models often fail to capture complex dependencies across multiple temporal features. This results in limited accuracy and delayed adjustments, which directly impact inventory, logistics, and profitability. To overcome these issues, we introduce a puzzled BiLSTM (PZ-BiLSTM) model, a specialized deep learning architecture designed for supply chain forecasting. Instead of traditional BiLSTM, our approach integrates structured feature blocks and temporal alignment techniques, which allows the model to identify both short-term fluctuations and long-term trends more effectively. The simulation of the model is performed using the publicly available Walmart sales dataset. When compared with existing demand forecasting models, our puzzled BiLSTM achieved an R² of 0.9708, demonstrating its superior predictive performance. This model not only improves forecast precision but also enables real-time adjustment in supply chain decisions by combining the strengths of bidirectional sequence modelling with a dynamic puzzle-based feature integration strategy.

Chameleon Hash Function (CH) is a hash function with a public and secret key pair. CH is collision-resistant for users without a secret key, while users with a secret key can find collisions in hash values. Chameleon Hash has been used in various cryptographic schemes, including online/offline signatures by Shamir et al. and blockchain modification by Ateniese et al. However, once the secret key is exposed in CH, its collision resistance is lost, and the security of all existing CH-based methods cannot be guaranteed. In this paper, we propose a generic Forward-Secure CH scheme, capable of converting any given CH into a Forward-Secure CH (FSCH) through the implementation of forward-secure encryption techniques. The security of the proposed protocol is reduced to Forward-Secure collision resistance, meaning that even if the current secret key is compromised, it ensures that collisions involving past hash values cannot be exploited or detected.

In visible light positioning (VLP) systems, factors like multipath reflection and noise interference degrade positioning accuracy. To address this challenge, this letter innovatively applies a Transformer-Encoder model to a VLP system with a dual-LED and multi-photodiode (PD) architecture. The proposed Transformer-Encoder model captures the spatial distribution information of the PDs through a Positional Encoding module. Its core Multi-Head Attention mechanism, incorporating positional information, enables the model to focus on critical channel features. This significantly enhances the model's robustness against multipath interference and noise while strengthening its capability to characterize channel features. Simulation results demonstrate that within a 4 m × 4 m × 3 m space, the Transformer-Encoder model achieves an average positioning error of 0.75 cm, with 90% of errors below 1.27 cm. Comparative analysis with other positioning models confirms the high precision of the proposed method.

To address the insufficient image restoration quality of Deep Joint Source-Channel Coding (DJSCC) systems under low signal-to-noise ratio (SNR) conditions, this paper proposes an enhanced system called DJSCC-H, which integrates a Hybrid Automatic Repeat Request (HARQ) mechanism. By designing a HARQ neural network module (HARQnn) that leverages the Log-Likelihood Ratio (LLR) of transmitted data, channel SNR, and a custom retransmission penalty factor, the paper enables the module to adaptively generate retransmission probabilities and optimize the weighted merging strategy for multi-round transmission data. As a result, this module effectively improves transmission quality under low SNR conditions. Experimental results show that in AWGN channels, when the SNR is 0-5 dB, the DJSCC-H system achieves maximum performance improvements of 18.62% in Peak Signal-to-Noise Ratio (PSNR) and 7.79% in Structural Similarity Index (SSIM) for image reconstruction compared to the baseline DJSCC system, verifying the effectiveness of deep integration of the retransmission mechanism with DJSCC. This paper provides an enhanced scheme for image transmission under low-reliability channels.

In recent years, various systems have become more complex, and the network structures used to model them have also become more complex. The concept of multidimensional network has been proposed in complex networks and multidimensional networks are modeled as graphs. The properties of multidimensional networks have been studied focusing on factors such as degree, neighbors, eccentricity, closeness and betweenness. In this paper, we define the correlation coefficient between dimensions based on neighbors in a multidimensional network, and discuss the change in correlation between dimensions based on neighbors due to addition of an edge in multidimensional networks and a method for adding an edge to strengthen or weaken the correlation.

Frequency-hopping multiple access (FHMA) systems offer significant advantages such as jamming resistance, multi-user capability, and enhanced security, making them crucial for secure communications and wireless networks. This letter establishes new theoretical bounds on the maximum periodic Hamming autocorrelation (PHAC) and periodic partial Hamming autocorrelation (PPHAC) for multi-timeslot wide-gap frequency-hopping sequences (MTWGFHSs), along with the maximum periodic partial Hamming correlation (PPHC) for MTWGFHS sets. These advancements address the challenges posed by dynamic channels and intelligent jamming, contributing to a robust FHMA system.

This paper proposes a vector similarity search (VSS) based offline learning approach for the deep-unfolded multiple-input multiple-output (MIMO) detector. The VSS offline learning approach consists of an offline learning phase and a real-time detection phase. In the offline learning phase, trained parameters of the deep-unfolded MIMO detector are stored in a vector database with a feature vector extracted from the channel matrix. In the real-time detection phase, the detector parameters are retrieved from the database with similarity matching of the feature vector. The critical advantage of the proposal is that it can offload the training computational cost from the edge server to the training server. Numerical results indicate that the VSS offline learning provides appropriate convergence acceleration in almost all cases, and that it improves the robustness of the deep-unfolded MIMO detector in dynamic channel environments.

Updatable encryption (UE) allows a thirdparty server to update outsourced encrypted data without exposing keys and plaintexts. The server can update ciphertexts to ones under a new key using an update token provided by the client. UE can realize efficient key rotation and is effective against key compromise. The standard security notions of UE capture the property that even if keys or update tokens are compromised, the confidentiality of messages is maintained by the key update and ciphertext update. In general, the randomnesses used in the encryption and ciphertext update algorithms must be kept secret in the same way as the keys. On the other hand, while key compromise is considered in existing security notions, randomness compromise is not. In this paper, we define a new security notion for UE, IND-UE-R security, that is resilient to the compromise of randomnesses used to generate or update ciphertexts. Furthermore, we prove that the UE construction RISE (EUROCRYPT'18) satisfies our proposed security notion.

Intel Software Guard eXtensions (SGX) allows users to confirm the confidentiality and integrity of running programs on cloud platforms by remote attestation. SGX has recently adopted the new remote attestation, ECDSA Attestation, and will abolish the previous one, EPID Attestation. ECDSA Attestation enables third parties to build their own verification environment. However, its high degree of freedom obscures the boundary of responsibility between the CPU vendor and third parties regarding ECDSA Attestation.

This paper clarifies the scope of responsibility for Intel, the developer of SGX, in ECDSA Attestation. To achieve this, we compared each component of ECDSA Attestation and EPID Attestation. Our analysis revealed that Intel is no longer responsible for the entire verification process but is instead limited to distributing signed data. Furthermore, we demonstrate that modifying DCAP does not violate responsibility boundaries in ECDSA Attestation. To the best of our knowledge, this study is the first to highlight the necessity of discussing the scope of responsibility in TEE.

Fully homomorphic encryption (FHE) is a cryptographic scheme that allows users to perform arbitrary arithmetic operations over plaintexts by operations (called homomorphic operations) on ciphertexts without decryption. A multi-key FHE (MK-FHE) can perform homomorphic operations on ciphertexts encrypted with different encryption keys.

In MK-FHE schemes, to decrypt a ciphertext encrypted with different users' keys, users having the corresponding decryption keys run a threshold decryption, which is a combination of each user's partial decryption and merging of their results. However, it has a drawback that the merging process requires communication and hence these users must be online during the process. Moreover, the computation and communication costs grow when the number of involved users increases. There is a previous work to overcome this issue by applying the idea of proxy re-encryption (PRE), where a proxy can convert a multi-key ciphertext, using re-encryption keys given by the key holders, into a ciphertext decryptable by a single receiver's decryption key. However, a collusion of only an adversarial receiver and the single proxy can reveal the original user's decryption key.

To resolve the issue, we propose a new framework of MK-FHE with threshold PRE. Here we introduce N proxies performing re-encryption in threshold manner; now the adversarial receiver needs to collude with all of the N proxies, which becomes more difficult than the previous single-proxy case. We also propose an instantiation based on the BFV scheme and prove its security. In addition, we implement our scheme and measure the running time of its algorithms.

As the critical role of power systems in emergency response continues to escalate, the efficiency of power emergency material distribution directly influences the speed of grid recovery. To address the challenge of optimizing the distribution paths for emergency power materials, this paper presents a bi-objective optimization model aimed at minimizing both the total distribution delay time and the average unmet demand rate at failure-prone demand points. In the context of disaster relief, real-time road repair data and environmental signals from fault areas, provided by the power Internet of Things (IoT), are incorporated as constraints into the distribution path, thereby formulating a multi-objective path optimization model. To solve this optimization problem, a hybrid algorithm combining Genetic Algorithm (GA) and Simulated Annealing (SA) is proposed. This hybrid approach effectively balances time efficiency and resource utilization within the multi-objective optimization framework, offering a robust and efficient solution for power emergency material distribution. Simulation results indicate that the optimized model significantly enhances material distribution efficiency, reduces delays, and ensures a timely and effective response to grid fault demands.

The quasi-synchronous frequency-hopping (FH) multiple access (QS-FHMA) communication system has the advantages of not requiring precise time synchronization, low equipment complexity, and easy to implement in engineering applications. And it is widely applied in military communication systems, vehicle-to-everything (V2X) communication, satellite communication systems, and industrial internet of things (IIoT), among others. FH sequences set with Low-Hit-Zone (LHZ), in LHZ both the Hamming autocorrelation of each sequence and the Hamming cross-correlation between distinct sequences remain low, are an important component of QS-FHMA communication system. The utilization of LHZ FH sequence set (LHZ FHS set) with optimal partial Hamming correlation (PHC) can effectively enhance the communication performance of QS-FHMA system. Based on q-ary m-sequence with degree n and its decimated sequence, q ≠ 2, n ≥ 2, this paper constructs three types of LHZ FHS sets. Experimental results show that these sequence sets have strictly optimal periodic PHC property, good wide gap property, and favorable complexity. The new constructions can provide more high-performance LHZ FHS sets for QS-FHMA communication systems.

In this paper, a switching control method for pedestrian flows using the discrete Hughes model with signal temporal logic is proposed. In the discrete Hughes model, the space is given by an undirected graph, and pedestrian flows are considered as changes of the density at each vertex. To specify various properties of the system, we introduce signal temporal logic. In the proposed method, pedestrian flows are controlled by switching the graph structure. For each graph structure, a safety penalty is set. The switching times of the graph structure that minimize the penalty are calculated under constraints on a given signal temporal logic formula including the evacuation rate defined in this paper. A numerical example shows that the proposed method is effective for evacuation planning and exit control.

The main challenge of multi-object tracking (MOT) is how to maintain a continuous trajectory of each object. Existing tracking methods typically rely on linear prediction models and intersection over union (IOU) values between objects in adjacent frames to predict associations. However, in dense scenes, complex motions and extreme occlusions tend to undermine the reliability of linear predictions and the discriminability of IoU matching. In this paper, we propose a simple yet effective multi-object tracking method that achieves more accurate prediction and association by leveraging the motion behavior information of the objects. We first design a Union Unscented Kalman Filter (UUKF) to capture the non-linear motion and interaction between objects. It is more suited for the non-linear movement patterns of pedestrians and captures the interaction between moving objects, enabling more accurate prediction of their positions. Moreover, to enhance the ability of data association in complex scenes, this paper proposes a bounding box scaling strategy based on orientation consistency. By dynamically adjusting the size of the bounding boxes, the interference caused by highly overlapping objects is reduced. Additionally, orientation consistency is used to achieve more accurate data association, thereby lowering the probability of mismatch. At last, in order to fully validate the performance of the proposed method, we have conducted extensive experiments and ablation studies on MOT16, MOT17, and MOT20, respectively. The experimental results show that it achieves superior performance.

The hierarchical and recursive expressive capability of rooted trees is applicable to represent statistical models in various areas, such as data compression, image processing, and machine learning. On the other hand, such hierarchical expressive capability causes a problem to avoid overfitting. One unified approach to solve this is a Bayesian approach, in which the rooted tree is regarded as a random variable and a direct loss function can be assumed on the selected model or the predicted value for a new data point. However, all the previous studies on this approach are based on the probability distribution on full trees, to the best of our knowledge. In this paper, we propose a generalized probability distribution for any rooted trees in which only the maximum number of child nodes and the maximum depth are fixed. Furthermore, we derive recursive methods to evaluate the characteristics of the probability distribution without any approximations.

In message authentication, we consider a situation where a sender transmits a message to a receiver through an insecure channel. In the insecure channel, there is a risk of impersonation or substitution by an adversary. Message authentication is a scheme to detect such attacks and to accept the message sent by the sender as legitimate. One of the research topics in message authentication is the estimation of limits on how small the probabilities of a successful attack can be. Previous research has shown impossibility bounds using Rényi entropy in the case where a shared key may be non-uniform. In this study, we consider a situation where the adversary has side-information that is correlated with a possibly non-uniform shared key, and investigate the impossibility using conditional Rényi entropy. In particular, we show that, in contrast to the previous research, impossibility bounds using conditional min-entropy do not hold in general. In addition, the success probabilities can be bounded in general using conditional collision entropy, and we show that the bound is the tightest in terms of conditional Rényi entropy.

Exact synthesis is a method to find an optimal circuit that meets the specification. SAT-based exact synthesis is commonly used for its ability to allow for a more versatile approach to addressing synthesis requirements. However, the runtime of SAT-based exact synthesis is very long and unpredictable to check all possible structures. Recently, topologybased exact synthesis methods have been studied to reduce the runtime of exact synthesis, where all structures are classified into sub-classes of topologies and each sub-class is checked using a simpler Conjunctive Normal Form separately. There is a freedom on the order of topologies to be checked, and we found that the Transformer is effective to decide the order. This paper proposes a Transformer-guided topology-based exact synthesis method (TGSyn) to achieve better time efficiency. A Transformer model is used to predict the success probability of synthesis for each topology in topology-based exact synthesis, and the order is decided based on the predicted probability to accelerate the synthesis process. The proposed Transformer-based model archives 98.56% of top-15 categorical accuracy. To evaluate the TGSyn, we used subgraphs in MIG with 3 to 5 inputs in EPFL and ISCAS'85 benchmarks using cut enumeration. The TGSyn reduces the runtime of the exact synthesis of 19,148 circuits by 64.02% compared with a method without Transformer guiding.

A (t₁, t₂)-overlap-free code is a set of sequences such that for all integers i with t₁ ≤ i ≤ t₂, no prefix of length i of any sequence is the suffix of any sequence (not necessarily distinct) in the set. In this paper, we first present a generalized construction of (t₁, t₂)-overlap-free codes and analyze the size of the code via generating functions. We then investigate the non-expandable property of (1, t)-overlap-free codes. Finally, we give the size of the (1, t)-overlap-free codes under certain constraint conditions.

In the context of multi-label classification on the Semantic Web—where entities and resources are often associated with multiple, interdependent labels—traditional methods typically assume complete label information during training. However, due to the heterogeneous and incomplete nature of Semantic Web data, missing labels are a common challenge that significantly degrades model performance. To address this critical issue, We introduce SWLP-LAM, an integrated architectural framework that uniquely combines semantic web principles with multi-label learning techniques to address the challenge of missing labels. SWLP-LAM introduces an energy-constrained diffusion mechanism to effectively encode instance graphs, capturing global semantic consistency across latent structures. This mechanism ensures that semantic relationships between instances are preserved even when label information is incomplete, thereby mitigating the impact of missing annotations. Additionally, SWLP-LAM employs a Teacher-Student framework, where the Teacher module learns distinct modalities for each label class, constructing a composite latent space. This composite space guides the Student module in mapping instance features, thereby enhancing label prediction accuracy while reducing the dependency on complete label information during training. A key innovation of SWLP-LAM lies in its graph-based label propagation module. This module not only recovers missing label confidences but also explicitly leverages label dependencies, which are particularly prevalent in Semantic Web data. By modeling these dependencies, SWLP-LAM can infer missing labels more accurately, even in scenarios with high label sparsity. The propagation mechanism further ensures that semantic information flows through the graph structure, reinforcing the model's ability to handle incomplete annotations while preserving the integrity of interdependent label relationships. Extensive experiments conducted on five benchmark multi-label datasets, as well as a real-world Semantic Web-inspired dataset, JUSTICE, demonstrate the superiority of SWLP-LAM. The results show that SWLPLAM consistently outperforms state-of-the-art methods in scenarios with missing labels, achieving significant improvements in classification accuracy, F1-score, and robustness. These findings highlight the effectiveness of SWLP-LAM in addressing the challenges of multi-label learning with missing labels, positioning it as a promising approach for semantic annotation and knowledge discovery in incomplete and heterogeneous data environments.

This paper focuses on the set-pair routing problem. The contributions of this paper are as follows: 1) proving the NP-hardness of the set-pair routing problem; and 2) presenting an Integer Linear Programming (ILP) formulation addressing the set-pair routing problem. The proposed method outputs the optimal solution for the benchmarks within the practical run-time.

Updatable encryption (UE) is a special type of symmetric-key encryption (SKE) that allows a third party to update ciphertexts while protecting plaintexts. Alamati et al. (CRYPTO 2019) showed a curious connection between UE and public-key encryption (PKE) that PKE can be constructed from UE. This implication result is somewhat surprising since it is well-known that PKE cannot be constructed from (ordinary) SKE in a black-box manner.

In this paper, we continue to study the relationships between UE and other cryptographic primitives to obtain further insights into the existence and power of UE, and assumptions required for it. More specifically, we introduce some security properties that are natural to consider for UE (and are indeed satisfied by some existing UE schemes), and then investigate what types of public-key cryptographic primitives can be constructed from UE with the additional properties. Specifically, we show the following results:

·2-round oblivious transfer (OT) can be constructed from UE that satisfies the oblivious samplability of original ciphertexts (i.e. those generated by the ordinary encryption algorithm, as opposed to those generated by the ciphertext-update algorithm) and the oblivious samplability of update tokens (that are used for updating ciphertexts).

·3-round OT can be constructed from UE with the oblivious samplability of updated ciphertexts (i.e. those generated by the ciphertext-update algorithm).

·Lossy encryption and PKE secure against selective-opening attacks can be constructed from UE if it satisfies what we call statistical confidentiality of original ciphertexts.

·IND-CPA secure PKE can be constructed from another variant of UE, ciphertext-dependent UE, if its algorithm to generate an update token is deterministic.

In 1990's, ID-based Non-Interactive Key Sharing Scheme (MK) based on the discrete logarithm problem over a composite number was proposed. In this letter, we propose an MK scheme with G-Numbers n = pq, where p-1 and q-1 consist of B-smooth prime factors. We implement the private-key computation using the ρ method with distinguished point method and Montgomery multiplications (DPMM-ρ method). We also parallelize the DPMM-ρ method using a server-client model and compare private key computation times between serial and parallel calculations.

Physics-informed neural network (PINN) is a machine learning-based approach for solving partial differential equations (PDEs). This paradigm leverages the system's prior knowledge in the form of PDE, as a regularization term to guide the training of the network. Traditionally, PINN relies on multilayer perceptrons (MLPs) to approximate the solution of PDEs. While this network's architecture has shown significant progress, PINN based on MLPs encounter several limitations for time-dependent PDEs, in particular for the temporal solutions. In addition, PINN is known to cope only with a fixed initial condition, which is a major drawback. In this work, we propose Physics-Informed Antisymmetric Recurrent Neural Network (PIARNN), which employs an initializer network, to solve time-dependent PDEs with various initial conditions. The recurrent connections are parameterized with a strictly upper-triangular parameter vector, which enforces a stable learning. PDEBench dataset is used to conduct experiments on reaction-diffusion and Burger's equations. It was observed that the proposed method can achieve competitive performance compared to the state-of-the-art methods such as conventional PINN, U-Net and Fourier neural operator (FNO).

These days, post-quantum cryptography based on the lattice isomorphism problem has been proposed. Ducas-Gibbons introduced the hull attack, which solves the lattice isomorphism problem for lattices obtained by Construction A from an LCD code over a finite field. Using this attack, they showed that the lattice isomorphism problem for such lattices can be reduced to the lattice isomorphism problem with the trivial lattice ℤⁿ and the graph isomorphism problem. While the previous work by Ducas-Gibbons only considered lattices constructed by a code over a finite field, this paper considers lattices constructed by a code over a finite ring ℤ/kℤ, which is a more general case. In particular, when k is odd, an odd prime power, or not divisible by 4, we show that the lattice isomorphism problem can be reduced to the lattice isomorphism problem for ℤⁿ and the graph isomorphism problem.

This letter proposes the MC-Net, which aims to improve salient object detection accuracy in complex environments by integrating multimodal information. By introducing cross-attention and cross-scale mechanism, the method facilitates the interactive learning between different feature representations. Experimental results show that MC-Net can make a significant breakthrough in detection performance.

In recent years, Built-In Self-Test (BIST) techniques have been widely used to reduce manufacturing test cost in large scale integrated circuits. However, it is difficult to achieve complete fault coverage on BIST, which uses pseudo-random test patterns, due to the presence of random pattern resistant faults. One-pass seed generation methods for a single target fault using satisfiability problem have been proposed as an efficient seed generation method. However, to target a single fault might require many seeds to obtain complete fault coverage. We propose a multiple target seed generation method for random pattern resistant stuck-at faults on BIST using pseudo-Boolean optimization and a compatible fault set to achieve complete fault coverage with the smaller number of seeds. The number of seeds is reduced by maximizing the number of detected faults per seed. Experimental results for ISCAS'89 benchmark circuits and ITC'99 benchmark circuits show that the proposed method could reduce the number of seeds by 49.69 % on average and by 76.72 % on maximum.

This paper proposes a ReRAM resistance design for ultrahigh-capacity digital memory and analog Computation-in-Memory (CiM). The read-out current of the bit-line is degraded by the interconnection resistance of the bit-line due to IR drop. The bit-line current formulation reveals that the ReRAM resistance should be set as high as 1.0×10⁵ Ω for the high-capacity digital ReRAM memory. In addition, the ReRAM resistance of LRS and HRS is designed as 1.0×10⁵ Ω and 1.0×10⁹ Ω for the high-capacity analog ReRAM CiM.