Binary Gates Research Articles

Ultrafast Visual Detection of a Trace Amount of Water by Highly Efficient Hybrid Manganese Halides.

A quantitative water detection method is urgently needed in storage facilities, space exploration, and the chemical industry. Although numerous physical techniques have been widely utilized to determine the water content, they still suffer from many disadvantages such as highly expensive special instruments, complicated analysis processes, etc. Hence, a convenient, rapid, and sensitive water analysis method is highly desirable. Herein, we developed a visual fluorescence sensing technology for water detection based on reversible PL off-on switching of organic-inorganic hybrid zero-dimensional (0D) manganese halides. In this work, a family of hybrid manganese halides were synthesized through a facile solution method, namely, [NH4(18-Crown-6)]2MnBr4, [Ca(18-Crown-6)·3H2O](18-Crown-6)MnBr4, [NH4(dibenzo-18-Crown-6)]2MnBr4, and [Ca(dibenzo-18-Crown-6)·2H2O]MnBr4. Excited by UV light, these highly crystalline manganese halides exhibit strong green light emissions from the d-d electron transition of Mn2+ with near-unity photoluminescence quantum yield and submillisecond lifetime. Benefiting from the dynamic and weak ionic bonding interactions, these 0D manganese halides display reversible water-response on/off luminescence switching but fail in any other aprotic solvents. Therefore, these 0D hybrid manganese halides can be explored as ultrafast visual fluorescence probes to detect the trace amount of water in organic solvents with multiple superiorities of rapid response time (< 2 s), ultralow detection limit (9.71 ppm), excellent repeatability, etc. The reversible water-response luminescent on/off switching also provides a binary optical gate with advanced applications in anticounterfeiting and information security, etc.

Homopolar Chemical Bonds Induce In‐Plane Anisotropy in Layered Semiconductors

Main‐group layered binary semiconductors, in particular, the III–VI alloys in the binary Ga–Te system are attracting increasing interest for a range of practical applications. The III–VI semiconductor, monoclinic gallium monotelluride (m‐GaTe), has been recently used in high‐sensitivity photodetectors/phototransistors and electronic memory applications due to its anisotropic properties yielding superior optical and electrical performance. Despite these applications, the origin of such anisotropy, namely the complex structural and bonding environments in GaTe nanostructures remain to be fully understood. In the present work, a comprehensive atomic‐scale characterization of m‐GaTe is reported by element‐resolved atomic‐scale microscopy experiments, enabling a direct measure of the in‐plane anisotropy at the sub‐Angstrom level. It is shown that these experimental images compare well with the results of first‐principles modeling. Quantum‐chemical bonding analyses provide a detailed picture of the atomic neighbor interactions within the layers, revealing that vertical GaGa homopolar bonds get stronger when they are distorted and rotated, inducing the strong in‐plane anisotropy. Beyond GaTe, using a systematic screening over the Materials Project database, the four additional low‐symmetric layered crystals with similar distorted tetrahedral patterns are identified, indicating that the homopolar‐bond‐induced anisotropy is a more generic feature in these layered van der Waals (vdW) materials.

Remote Sensing Image Classification Based on Neural Networks Designed Using an Efficient Neural Architecture Search Methodology

Successful applications of machine learning for the analysis of remote sensing images remain limited by the difficulty of designing neural networks manually. However, while the development of neural architecture search offers the unique potential for discovering new and more effective network architectures, existing neural architecture search algorithms are computationally intensive methods requiring a large amount of data and computational resources and are therefore challenging to apply for developing optimal neural network architectures for remote sensing image classification. Our proposed method uses a differentiable neural architecture search approach for remote sensing image classification. We utilize a binary gate strategy for partial channel connections to reduce the sizes of the network parameters, creating a sparse connection pattern that lowers memory consumption and NAS computational costs. Experimental results indicate that our method achieves a 15.1% increase in validation accuracy during the search phase compared to DDSAS, although slightly lower (by 4.5%) than DARTS. However, we reduced the search time by 88% and network parameter size by 84% compared to DARTS. In the architecture evaluation phase, our method demonstrates a 2.79% improvement in validation accuracy over a manually configured CNN network.

Pruning Self-Attentions Into Convolutional Layers in Single Path.

Vision Transformers (ViTs) have achieved impressive performance over various computer vision tasks. However, modeling global correlations with multi-head self-attention (MSA) layers leads to two widely recognized issues: the massive computational resource consumption and the lack of intrinsic inductive bias for modeling local visual patterns. To solve both issues, we devise a simple yet effective method named Single-Path Vision Transformer pruning (SPViT), to efficiently and automatically compress the pre-trained ViTs into compact models with proper locality added. Specifically, we first propose a novel weight-sharing scheme between MSA and convolutional operations, delivering a single-path space to encode all candidate operations. In this way, we cast the operation search problem as finding which subset of parameters to use in each MSA layer, which significantly reduces the computational cost and optimization difficulty, and the convolution kernels can be well initialized using pre-trained MSA parameters. Relying on the single-path space, we introduce learnable binary gates to encode the operation choices in MSA layers. Similarly, we further employ learnable gates to encode the fine-grained MLP expansion ratios of FFN layers. In this way, our SPViT optimizes the learnable gates to automatically explore from a vast and unified search space and flexibly adjust the MSA-FFN pruning proportions for each individual dense model. We conduct extensive experiments on two representative ViTs showing that our SPViT achieves a new SOTA for pruning on ImageNet-1 k. For example, our SPViT can trim 52.0% FLOPs for DeiT-B and get an impressive 0.6% top-1 accuracy gain simultaneously.

Remora Optimized Dynamic Neural Network to Design Low-Power CNTFET Logic Gate Realization with Multiple-Valued Circuits

The emerging trend of new digital technology leads to the requirement of small, faster, and low-power consumed processors. The advanced 5 G technology forces the usage of enhanced battery life, maximized spectral efficiency, etc., these can be achieved by the inclusion of a Carbon nanotube field-effect transistor (CNTFET). The proposed multiple-valued logic circuits (MVL) are designed using some of the unique features of the CNTFET such as high transconductance, setting of desired threshold voltage, and similar mobility of P and N-type devices. For designing low-power and optimized logic gate realization we have utilized the inherent binary gates along with the CMOS architecture. Further, an artificial intelligence approach known as Dynamic Neural Network (DNN) is adopted for the realization of logic gates in the MVL with the employment of lesser MVL operators. To enhance the parameter utilization of DNN with the help of tuning, we exploited the Remora Optimization algorithm (ROA). The results are obtained by conducting experiments on HFSS software. This approach is designed to reduce the static power dissipation, the number of logic gates, and network propagation delay. Compared to the existing methods, the proposed technique outlined a standard voltage of 32 nm that considers 0.9 V supply voltage. The result of our proposed method can be deviated by only 25% and thus outperforms all the other approaches. The standby power consumption of proposed approach is 2.12 W

Low-voltage operating, high mobility top-gate structural flexible organic thin-film transistor with a one-step spin-coated binary polymer gate dielectric

Low-voltage operation is one of the prerequisites for the practical applications of the organic thin-film transistors (OTFTs). Up to date, the most reported low-voltage operatable OTFTs use a bottom-gate structure, and are fabricated by several different technologies in the whole process, in which the organic semiconductors and/or gate dielectrics are prepared in the expensive vacuum equipment. The simple fabricating technologies and fewer processes can better demonstrate the inherent advantages, and enhance the commercial competitiveness of OTFTs. Here, we propose a strategy to fabricate the binary polymers gate dielectric by one-step spin-coating in the top-gate structured flexible OTFTs, by which not only the device performances are prominently improved, but also the fabricating process of the OTFTs is minimized. As a result, the flexible OTFTs exhibit a high mobility over 0.5 cm2 Vs−1, low threshold voltage near to −0.5 V, and excellent mechanical bending durability with a very slightly performances degradation after the tensile and compressive bending at a small curvature radius of 3.0 mm over 1000 cycles.

Oblivious network intrusion detection systems

A main function of network intrusion detection systems (NIDSs) is to monitor network traffic and match it against rules. Oblivious NIDSs (O-NIDS) perform the same tasks of NIDSs but they use encrypted rules and produce encrypted results without being able to decrypt the rules or the results. Current implementations of O-NIDS suffer from slow searching speeds and/or lack of generality. In this paper, we present a generic approach to implement a privacy-preserving O-NIDS based on hybrid binary gates. We also present two resource-flexible algorithm bundles built upon the hybrid binary gates to perform the NIDS’s essential tasks of direct matching and range matching as a proof of concept. Our approach utilizes a Homomorphic Encryption (HE) layer in an abstract fashion, which makes it implementable by many HE schemes compared to the state-of-the-art where the underlying HE scheme is a core part of the approach. This feature allowed the use of already-existing HE libraries that utilize parallelization techniques in GPUs for faster performance. We achieved a rule encryption time as low as 0.012% of the state of the art with only 0.047% of its encrypted rule size. Also, we achieved a rule-matching speed that is almost 20,000 times faster than the state of the art.

Single-Path Bit Sharing for Automatic Loss-Aware Model Compression.

Network pruning and quantization are proven to be effective ways for deep model compression. To obtain a highly compact model, most methods first perform network pruning and then conduct quantization based on the pruned model. However, this strategy may ignore that the pruning and quantization would affect each other and thus performing them separately may lead to sub-optimal performance. To address this, performing pruning and quantization jointly is essential. Nevertheless, how to make a trade-off between pruning and quantization is non-trivial. Moreover, existing compression methods often rely on some pre-defined compression configurations (i.e., pruning rates or bitwidths). Some attempts have been made to search for optimal configurations, which however may take unbearable optimization cost. To address these issues, we devise a simple yet effective method named Single-path Bit Sharing (SBS) for automatic loss-aware model compression. To this end, we consider the network pruning as a special case of quantization and provide a unified view for model pruning and quantization. We then introduce a single-path model to encode all candidate compression configurations, where a high bitwidth value will be decomposed into the sum of a lowest bitwidth value and a series of re-assignment offsets. Relying on the single-path model, we introduce learnable binary gates to encode the choice of configurations and learn the binary gates and model parameters jointly. More importantly, the configuration search problem can be transformed into a subset selection problem, which helps to significantly reduce the optimization difficulty and computation cost. In this way, the compression configurations of each layer and the trade-off between pruning and quantization can be automatically determined. Extensive experiments on CIFAR-100 and ImageNet show that SBS significantly reduces computation cost while achieving promising performance. For example, our SBS compressed MobileNetV2 achieves 22.6× Bit-Operation (BOP) reduction with only 0.1% drop in the Top-1 accuracy.

Direct training high-performance spiking neural networks for object recognition and detection.

The spiking neural network (SNN) is a bionic model that is energy-efficient when implemented on neuromorphic hardwares. The non-differentiability of the spiking signals and the complicated neural dynamics make direct training of high-performance SNNs a great challenge. There are numerous crucial issues to explore for the deployment of direct training SNNs, such as gradient vanishing and explosion, spiking signal decoding, and applications in upstream tasks. To address gradient vanishing, we introduce a binary selection gate into the basic residual block and propose spiking gate (SG) ResNet to implement residual learning in SNNs. We propose two appropriate representations of the gate signal and verify that SG ResNet can overcome gradient vanishing or explosion by analyzing the gradient backpropagation. For the spiking signal decoding, a better decoding scheme than rate coding is achieved by our attention spike decoder (ASD), which dynamically assigns weights to spiking signals along the temporal, channel, and spatial dimensions. The SG ResNet and ASD modules are evaluated on multiple object recognition datasets, including the static ImageNet, CIFAR-100, CIFAR-10, and neuromorphic DVS-CIFAR10 datasets. Superior accuracy is demonstrated with a tiny simulation time step of four, specifically 94.52% top-1 accuracy on CIFAR-10 and 75.64% top-1 accuracy on CIFAR-100. Spiking RetinaNet is proposed using SG ResNet as the backbone and ASD module for information decoding as the first direct-training hybrid SNN-ANN detector for RGB images. Spiking RetinaNet with a SG ResNet34 backbone achieves an mAP of 0.296 on the object detection dataset MSCOCO.

FP-DARTS: Fast parallel differentiable neural architecture search for image classification

Neural Architecture Search (NAS) has made remarkable progress in automatic machine learning. However, it still suffers massive computing overheads limiting its wide applications. In this paper, we present an efficient search method, Fast Parallel Differential Neural Architecture Search (FP-DARTS). The proposed method is carefully designed from three levels to construct and train the super-network. Firstly, at the operation-level, to reduce the computational burden, different from the standard DARTS search space (8 operations), we decompose the operation set into two non-overlapping operator sub-sets (4 operations for each). Adopting these two reduced search spaces, two over-parameterized sub-networks are constructed. Secondly, at the channel-level, the partially-connected strategy is adopted, where each sub-network only adopts partial channels. Then these two sub-networks construct a two-parallel-path super-network by addition. Thirdly, at the training-level, the binary gate is introduced to control whether a path participates in the super-network training. It may suffer an unfair issue when using softmax to select the best input for intermediate nodes across two operator sub-sets. To tackle this problem, the sigmoid function is introduced, which measures the performance of operations without compression. Extensive experiments demonstrate the effectiveness of the proposed algorithm. Specifically, FP-DARTS achieves 2.50% test error with only 0.08 GPU-days on CIFAR10, and a state-of-the-art top-1 error rate of 23.7% on ImageNet using only 2.44 GPU-days for search.

Weight-Dependent Gates for Network Pruning

In this paper, a simple yet effective network pruning framework is proposed to simultaneously address the problems of pruning indicator, pruning ratio, and efficiency constraint. This paper argues that the pruning decision should depend on the convolutional weights, and thus proposes novel weight-dependent gates (W-Gates) to learn the information from filter weights and obtain binary gates to prune or keep the filters automatically. To prune the network under efficiency constraints, a switchable Efficiency Module is constructed to predict the hardware latency or FLOPs of candidate pruned networks. Combined with the proposed Efficiency Module, W-Gates can perform filter pruning in an efficiency-aware manner and achieve a compact network with a better accuracy-efficiency trade-off. We have demonstrated the effectiveness of the proposed method on ResNet34, ResNet50, and MobileNet V2, respectively achieving up to 1.33/1.28/1.1 higher Top-1 accuracy with lower hardware latency on ImageNet. Compared with state-of-the-art methods, W-Gates also achieves superior performance.

Road testing new CAR design strategies in multiple myeloma.

A deeper understanding of basic immunology principles and advances in bioengineering have accelerated the mass production of genetically-reprogrammed T-cells as living drugs to treat human diseases. Autologous and allogeneic cytotoxic T-cells have been weaponized to brandish MHC-independent chimeric antigen receptors (CAR) that specifically engage antigenic regions on tumor cells. Two distinct CAR-based therapeutics designed to target BCMA are now FDA-approved based upon robust, sustained responses in heavily-pretreated multiple myeloma (MM) patients enrolled on the KarMMa and CARTITUDE-1 studies. While promising, CAR T-cells present unique challenges such as antigen escape and T-cell exhaustion. Here, we review novel strategies to design CARs that overcome current limitations. Co-stimulatory signaling regions were added to second-generation CARs to promote IL-2 synthesis, activate T-cells and preclude apoptosis. Third-generation CARs are composed of multiple co-stimulatory signaling units, e.g., CD28, OX40, 4-1BB, to reduce exhaustion. Typically, CAR T-cells incorporate a potent constitutive promoter that maximizes long-term CAR expression but extended CAR activation may also promote T-cell exhaustion. Hypoxia-inducible elements can be incorporated to conditionally drive CAR expression and selectively target MM cells within bone marrow. CAR T-cell survival and activity is further realized by blocking intrinsic regulators of T-cell inactivation. T-Cells Redirected for Universal Cytokine Killing (TRUCKs) bind a specific tumor antigen and produce cytokines to recruit endogenous immune cells. Suicide genes have been engineered into CAR T-cells given the potential for long-term on-target, off-tumor effects. Universal allo-CAR T-cells represent an off-the-shelf source, while logic-gated CAR T-cells are designed to recognize tumor-specific features coupled with Boolean-generated binary gates that then dictate cell-fate decisions. Future generations of CARs should further revitalize immune responses, enhance tumor specificity and reimagine strategies to treat myeloma and other cancers.

Generalizable Crowd Counting via Diverse Context Style Learning

Existing crowd counting approaches predominantly perform well on the training-testing protocol. However, due to large style discrepancies not only among images but also within a single image, they suffer from obvious performance degradation when applied to unseen domains. In this paper, we aim to design a generalizable crowd counting framework which is trained on a source domain but can generalize well on the other domains. To reach this, we propose a gated ensemble learning framework. Specifically, we first propose a diverse fine-grained style attention model to help learn discriminative content feature representations, allowing for exploiting diverse features to improve generalization. We then introduce a channel-level binary gating ensemble model, where diverse feature prior, input-dependent guidance and density grade classification constraint are implemented, to optimally select diverse content features to participate in the ensemble, taking advantage of their complementary while avoiding redundancy. Extensive experiments show that our gating ensemble approach achieves superior generalization performance among four public datasets. Codes are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wdzhao123/DCSL</uri> .

Ternary logic design in topological quantum computing

A quantum computer can perform exponentially faster than its classical counterpart. It works on the principle of superposition. But due to the decoherence effect, the superposition of a quantum state gets destroyed by the interaction with the environment. It is a real challenge to completely isolate a quantum system to make it free of decoherence. This problem can be circumvented by the use of topological quantum phases of matter. These phases have quasiparticles excitations called anyons. The anyons are charge-flux composites and show exotic fractional statistics. When the order of exchange matters, then the anyons are called non-abelian anyons. Majorana fermions in topological superconductors and quasiparticles in some quantum Hall states are non-abelian anyons. Such topological phases of matter have a ground state degeneracy. The fusion of two or more non-abelian anyons can result in a superposition of several anyons. The topological quantum gates are implemented by braiding and fusion of the non-abelian anyons. The fault-tolerance is achieved through the topological degrees of freedom of anyons. Such degrees of freedom are non-local, hence inaccessible to the local perturbations. In this paper, the Hilbert space for a topological qubit is discussed. The Ising and Fibonacci anyonic models for binary gates are briefly given. Ternary logic gates are more compact than their binary counterparts and naturally arise in a type of anyonic model called the metaplectic anyons. The mathematical model, for the fusion and braiding matrices of metaplectic anyons, is the quantum deformation of the recoupling theory. We proposed that the existing quantum ternary arithmetic gates can be realized by braiding and topological charge measurement of the metaplectic anyons.

Fourier Deconvolution Ion Mobility Spectrometry.

Multiplexing the ion packet injection with advanced signal processing is an effective method to improve both the ion throughput and signal-to-noise ratio for ion mobility spectrometry. Generally used multiplexing methods include Hadamard transform ion mobility spectrometry (HT-IMS), Fourier transform ion mobility spectrometry (FT-IMS), and correlation ion mobility spectrometry (C-IMS). However, HT-IMS sometimes suffer from false peaks and further processing is needed, FT-IMS generally requires longer spectra acquisition time than the traditional signal averaging method, and C-IMS also demonstrated drawbacks such as spectra baseline distortions when using traditional on-off binary gating switches. To improve the performance of multiplexing ion mobility spectrometry, this study investigates the Fourier deconvolution to increase the resolving power and signal-to-noise ratio at the same time. This approach modulates the ion gate with a linear square wave chirp sequence and synchronizes the data acquisition and ion gate modulation and then reconstructs the ion mobility spectra based on convolution theorems. The equivalent ion injection period is decreased to microseconds scale with the signal-to-noise ratio improved by up to 13 times on average, and the resolving power is improved by up to 50% compared with traditional signal averaging methods without hardware modifications.

Simulation Study of Linearly Graded Binary Metal Alloy PαQ1-α Gate TFET Device for Realization of Boolean Functions

Simulation Study of Linearly Graded Binary Metal Alloy PαQ1-α Gate TFET Device for Realization of Boolean Functions

Implementation of a Linearly Graded Binary Metal Gate Work Function VTFET with Air Pocket

Implementation of a Linearly Graded Binary Metal Gate Work Function VTFET with Air Pocket

Notes on the maximality of reversible gate sets under borrow and ancilla closure

• Determination of properties of maximal borrow and ancilla closed gate sets for reversible computation. • Determination of duals for these, indicating possible general dual structure for borrow and ancilla closure. • Remark that most 3-input binary reversible gates are ancilla universal. We investigate collections of reversible gates closed under parallel and serial composition, with and without borrow and ancilla closure. In order to better understand the structure of these collections of reversible gates, we investigate the lattice of closed sets and the maximal members of this lattice, that is, collections that are not all gates, but the addition of a single new gate will allow us to construct all gates. From previous work, we know the maximal closed sets over a finite alphabet. In this paper we extend this work to ancilla and borrow closure for reversible gates. Here we find several structural results, where we know that there are restricted possibilities for all maximal borrow and ancilla closed collections of gates. We determine several classes of maximal gates and determine their dual structures as weights as defined by Jeřábek. These maximal closed collections of gates correspond to minimal weight permutation co-clones. Maximality has several applications, one of the interesting ones being that a gate in no maximal closed set must be universal. Thus an understanding of maximality is intimately tied to an understanding of universality.

Polyadic Braid Operators and Higher Braiding Gates

A new kind of quantum gates, higher braiding gates, as matrix solutions of the polyadic braid equations (different from the generalized Yang–Baxter equations) is introduced. Such gates lead to another special multiqubit entanglement that can speed up key distribution and accelerate algorithms. Ternary braiding gates acting on three qubit states are studied in detail. We also consider exotic non-invertible gates, which can be related with qubit loss, and define partial identities (which can be orthogonal), partial unitarity, and partially bounded operators (which can be non-invertible). We define two classes of matrices, star and circle ones, such that the magic matrices (connected with the Cartan decomposition) belong to the star class. The general algebraic structure of the introduced classes is described in terms of semigroups, ternary and 5-ary groups and modules. The higher braid group and its representation by the higher braid operators are given. Finally, we show, that for each multiqubit state, there exist higher braiding gates that are not entangling, and the concrete conditions to be non-entangling are given for the obtained binary and ternary gates.

A Winner‐Take‐All Autoencoder Based Pieceswise Linear Model for Nonlinear Regression with Missing Data

AbstractMissing data is a prevailing problem in predictive analytics. In this paper, a winner‐take‐all (WTA) autoencoder‐based piecewise linear model is developed to solve the nonlinear regression problem under the missing value scenario, which consists of two parts: an overcomplete WTA autoencoder and a gated linear network. The overcomplete WTA autoencoder is a stacked denoising autoencoder (SDAE) designed to play two roles: (1) to estimate the missing values; (2) to realize a sophisticated partitioning by generating a broad set of binary gate control sequences. Besides, an iterative algorithm with renewed teacher signals is developed to train the SDAE. On the other hand, the gated linear network with the generated binary gate control sequences implements a flexible piecewise linear model for nonlinear regression. By composing a quasi‐linear kernel based on the gate control sequences, the piecewise linear model is then identified in the same way as a support vector regression. Experimental results have shown that our proposed hybrid model has a better performance than traditional models. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.