This study proposes a low-complexity genetic algorithm-based (LC-GA) detector to achieve the complexity-accuracy trade-off in non-coherent short-packet communication (SPC) employing modulation on conjugate-reciprocal zeros (MOCZ). This study rigorously proves that the inverse matrix in the maximum likelihood (ML) detector is a Hermitian positive definite matrix. In addition, by exploiting Cholesky decomposition, the computationally intensive full matrix inversion is reduced to the lower triangular matrix inversion, thus significantly decreasing the fitness function’s complexity under the ML criterion. The proposed genetic algorithm (GA) incorporates tailored crossover and mutation operators combined with an elitism strategy and achieves optimal detection accuracy without exhaustive search. Further, single-input multiple-output (SIMO) diversity reception is validated through simulations to combat frequency-selective fading. The results demonstrate that for transmitted data length ofK= 16, the LC-GA detector can achieve identical bit error rate (BER) performance as the ML detector with 88% fewer iterations, yielding the SNR improvements of 0.5 dB and 1.2 dB over the Viterbi and direct zero-testing (DiZeT) detectors at BER = 10−3, respectively. Similarly, forK= 32, the proposed LC-GA detector reduces the iteration number by six orders of magnitude compared to the ML detector, which requires 1010iterations and is computationally prohibitive, while maintaining the SNR improvements of 0.5 dB and 1.3 dB compared to the existing detectors at BER = 10−3.

E-graphs have gained increasing popularity in compiler optimization, program synthesis, and theorem proving tasks. They enable compact representation of many equivalent expressions and facilitate transformations via rewrite rules without phase ordering limitations. A major benefit of using e-graphs is the ability to explore a large space of equivalent expressions, allowing the extraction of an expression that best meets certain optimization objectives (or cost models). However, current e-graph extraction methods often face unfavorable scalability-quality trade-offs and only support simple linear cost functions, limiting their applicability to more realistic optimization problems.

Recent studies have shown that video action recognition models are also vulnerable to fooling by adversarial samples. However, currently existing video attack methods usually require high computational overhead (e.g., they generate adversarial perturbations for all frames by default), and most of them are difficult to implement printable attacks in the physical world. To address the above issues, we devise a novel efficient and effective framework for video action recognition attack: Bullet-Screen-Emoji Attack with Temporal Difference Noise (BSE), a reinforcement learning-based black-box attack method that fools the model by simply generating adversarial bullet screens for key frame and scrolling them on clean video. The agent is optimized to make the optimal actions, i.e., searching key frame. Moreover, we introduce a simple and effective temporal difference noise to enhance the attack capability of the adversarial bullet screen and accelerate the convergence speed. Most importantly, BSE enables printable physical attacks. Extensive experiments show that our proposed BSE achieves promising attack performance on mainstream datasets (HMDB51, UCF101 and Kinetics-400) and in the physical world with high efficiency.

Logic locking is a promising technique for protecting integrated circuit designs while outsourcing their fabrication. Recently, graph neural network (GNN)-based link prediction attacks have been developed which can successfully break all the multiplexer-based locking techniques that were expected to be learning-resilient. We present SimLL, a novel similarity-based locking technique which locks a design using multiplexers and shows robustness against the existing structure-exploiting oracle-less learning-based attacks. Aiming to confuse the machine learning (ML) models, SimLL introduces key-controlled multiplexers between logic gates or wires that exhibit high levels of topological and functional similarity. Empirical results show that SimLL can degrade the accuracy of existing ML-based attacks to approximately 50%, resulting in a negligible advantage over random guessing.

MOTIVATION: Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed significant analytical challenges. RESULTS: We proposed a two-step Bayesian linear mixed model framework (TBLMM) for risk prediction analysis on multi-omics data. TBLMM models the predictive effects from multi-omics data using a hybrid of the sparsity regression and linear mixed model with multiple random effects. It can resemble the shape of the true effect size distributions and accounts for non-linear, including interaction effects, among multi-omics data via kernel fusion. It infers its parameters via a computationally efficient variational Bayes algorithm. Through extensive simulation studies and the prediction analyses on the positron emission tomography imaging outcomes using data obtained from the Alzheimer's Disease Neuroimaging Initiative, we have demonstrated that TBLMM can consistently outperform the existing method in predicting the risk of complex traits. AVAILABILITY AND IMPLEMENTATION: The corresponding R package is available on GitHub (https://github.com/YaluWen/TBLMM).

The Boolean satisfiability (SAT) attack is an oracle-guided attack that can break most combinational logic locking schemes by efficiently pruning out all the wrong keys from the search space. Extending such an attack to sequential logic locking requires multiple time-consuming rounds of SAT solving, performed using an “unrolled” version of the sequential circuit, and model checking, used to determine the successful termination of the attack. This article addresses these challenges by formally characterizing the relation between the minimum unrolling depth required to prune out the wrong keys of an SAT-based attack and a notion of functional corruptibility (FC) for sequential circuits, which can be efficiently estimated from a locked circuit to indicate the progress of an SAT-based attack. Based on this analysis, we present an FC-guided SAT-based attack that can significantly reduce unnecessary SAT and model-checking tasks. We present two versions of the attack, namely,Fun-SATandFun-SAT+, based on whether the attacker has a priori knowledge of the key length.Fun-SATaims to find the correct key sequence, whileFun-SAT+aims to retrieve the correct initial state of the circuit. The numerical evaluation shows thatFun-SATcan be, on average,$90\boldsymbol {\times }$faster than previous attacks against state-of-the-art locking methods. On the other hand, when using an approximate termination condition,Fun-SAT+can find an initial state that leads to at most 0.1% FC in 76.9% instances that would otherwise time out after one day.

We introduce a systematic framework for logic locking of integrated circuits based on the analysis of the sources of information leakage from both the circuit and the locking scheme and their formalization into a notion of risk that can guide the design against existing and possible future attacks. We further propose a two-level optimization-based methodology to generate locking strategies minimizing a cost function and balancing security, risk, and implementation overhead, out of a collection of locking primitives. Optimization results on a set of case studies show the potential of layering multiple locking primitives to provide high security at significantly lower risk.

Reverse engineering an integrated circuit netlist is a powerful tool to help detect malicious logic and counteract design piracy. A critical challenge in this domain is the correct classification of data-path and control-logic registers in a design. We present ReIGNN, a novel learning-based register classification methodology that combines graph neural networks (GNNs) with structural analysis to classify the registers in a circuit with high accuracy and generalize well across different designs. GNNs are particularly effective in processing circuit netlists in terms of graphs and leveraging properties of the nodes and their neighborhoods to learn to efficiently discriminate between different types of nodes. Structural analysis can further rectify any registers misclassified as state registers by the GNN by analyzing strongly connected components in the netlist graph. Numerical results on a set of benchmarks show that ReIGNN can achieve, on average, 96.5% balanced accuracy and 97.7% sensitivity across different designs.

We propose SANSCrypt, a novel sequential logic encryption scheme to protect integrated circuits against reverse engineering. Previous sequential encryption methods focus on modifying the circuit state machine such that the correct functionality can be accessed by applying the correct key sequence only once. Considering the risk associated with one-time authentication, SANSCrypt adopts a new temporal dimension to logic encryption, by requiring the user to sporadically perform multiple authentications according to a protocol based on pseudorandom number generation. Analysis and validation results on a set of benchmark circuits show that SANSCrypt offers a substantial output corruptibility if the key sequences are applied incorrectly. Moreover, it exhibits an exponential resilience to existing attacks, including SAT-based attacks, while maintaining a reasonably low overhead.

We established an experimental region located at Wang Ye Dian forest farm in Chifeng City, Inner Mongolia, China. Within this region, the catastrophe theory model applied for 6 common species single wood volume measurement and calculation to establish a growth model for trees. The experiment was carried out through the actual measurement of the tree height (H), DBH (D), volume (V), diameter (D0) as the true value. The internal and external accord precision of “V-H-D” model established were conducted by using the improved differential evolution algorithm and edge species model and then the difference between model calculation value and instrument measuring. the results showed that: The overall relative error (RS) of the model is in the range of [0.001, 0.05], the average relative error (E) is in the range of [-0.11, 0.02], the overall prediction accuracy (P) is greater than 80%. The results show that the model is consistent with the northern China 6 single species tree volume. Meanwhile, this study provides a theoretical and practical basis for the study of tree volume and various species of spatial competition.