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There is a need to design a hardware synapse array appropriate for enhancing the efficiency of neuromorphic computing systems while minimizing energy consumption. This study introduces a memristor device with an AlOx overshoot suppression layer (A‐OSL) to achieve a self‐compliance effect. By optimizing each cell within the 16 × 16 crossbar array, synaptic devices are successfully fabricated with reliable characteristics and 3‐bit multilevel capabilities. In addition, the oxygen composition of TiOx and the annealing conditions are optimized to reduce the forming voltage and minimize the variation in the switching voltage. As a result, stable forming‐free characteristics are obtained through A‐OSL insertion, a reduction in forming voltage, and TiOx oxygen composition optimization. Also, target weights are accurately transferred to the A‐OSL memristor crossbar array and conducted the inference process by applying spike signals to the array following the designated time step. The spiking neural network (SNN) is demonstrated by measuring vector‐matrix multiplication (VMM) of the 16 × 16 crossbar array. The VMM results exhibit a classification accuracy of 90.80% for the MNIST dataset, which is close to the accuracy achieved by software‐based approaches, amounting to 91.85%. [ABSTRACT FROM AUTHOR]

In this work, a memristor using Si nano-tip bottom electrode has been fabricated and evaluated. Compared with the control device fabricated in a planar structure, the invented Si nano-tip device statistically demonstrates 400 times of area shrinkage and 3 and 8 times of current reductions in the low- and high-resistance states, respectively, owing to the effects of highly confined switching area on the operations of multiple-weak-filament (MWF)-type memristor. The results reveal the validity of structural scaling on the MWF-type memristors as effective as a material approach. Moreover, the fabricated Si memristor has been evaluated as a synaptic device via pattern recognition toward neuromorphic applications in consideration of interconnect resistance. The proposed Si memristor shows substantially reduced inference power consumption by an increase in resistances and improved accuracy by 5% compared with the control device. [ABSTRACT FROM AUTHOR]

We propose new 3d N = 2 Seiberg-like dualities by considering various monopole superpotential deformations on 3d N = 2 U(Nc) SQCDs with fundamental and adjoint matter fields. We provide nontrivial evidence of these new dualities by comparing the superconformal indices, from which we analyze the change of the moduli space due to the monopole deformation. In addition, we perform the F-maximization to check the relevance of the monopole deformation for some examples, one of which is found to exhibit nontrivial symmetry enhancement in the IR. We prove such enhancement of the global symmetry using the superconformal index. [ABSTRACT FROM AUTHOR]

In this study, the "resting-potential-adjustable soft-reset (RSR)" integrate-and-fire neuron model is proposed for higher reliability of hardware-based spiking neural networks (SNNs). Recently, researchers have attempted to implement neuronal models using hardware. However, previously proposed hard-reset (HR) and soft-reset (SR) neuron models are not suitable for hardware implementation for the following reasons. First, it is difficult to compensate for the neuron-threshold variation, which generally occurs in the fabrication processes. Second, they require a negative voltage supply that consumes substantial energy to implement the negative membrane potential (NMP). Third, for the HR and SR neuron models, maintaining the residual membrane potential (RMP) is challenging. The proposed RSR neuron model solves these challenges without any additional systems and algorithms and provides the following advantages: easy compensation for the neuron-threshold variation, significant energy reduction without using a negative voltage supply for the NMP, and precise preservation of the RMP. We discuss the behavior of the RSR neuron model and evaluate performance by comparing the inference results. Even with the neuron-threshold variation within the six-sigma range, the RSR neuron model restores the inference accuracy to the values of artificial neural networks (ANNs) for various datasets (CIFAR-10: 92.74%, SVHN: 95.00%, MNIST: 98.30%). In the case of the CIFAR-10, the RSR neuron model consumes 32.80% lower energy than the SR neuron model. [Display omitted] [ABSTRACT FROM AUTHOR]

Recently, various memory devices have been actively studied as suitable candidates for synaptic devices, which are important memory and computing units in neuromorphic systems. One of the ways to manage these devices is off-chip training, where it is essential to transfer the pretrained weights accurately. Previous studies, however, have a few limitations, such as a lack of consideration of program errors that occur during the transfer process. Although the smaller the program error, the higher the accuracy, the corresponding increase in the program time must be considered. To evaluate the practical applicability, we fabricated Al2O3/TiOx-based resistive random access memory (RRAM) and investigated the effect of program errors on program time and system degradation. It was confirmed that for smaller program errors, the program time was exponentially longer. Furthermore, we examined the effect of variation with respect to the number of quantized weight states (${N}_{state}$) through system-level simulation. We observed that the optimized ${N}_{state}$ varies depending on whether the program error is small or large. This result is meaningful as it experimentally shows the tradeoff between the program error, program time, and system performance. We expect it to be useful in the development of neuromorphic systems. [ABSTRACT FROM AUTHOR]

We propose a novel procedure of assigning a pair of non-unitary topological quantum field theories (TQFTs), TFT±[ T rank 0], to a (2+1)D interacting N = 4 superconformal field theory (SCFT) T rank 0 of rank 0, i.e. having no Coulomb and Higgs branches. The topological theories arise from particular degenerate limits of the SCFT. Modular data of the non-unitary TQFTs are extracted from the supersymmetric partition functions in the degenerate limits. As a non-trivial dictionary, we propose that F = maxα (− log| S 0 α + |) = maxα (− log| S 0 α − |), where F is the round three-sphere free energy of T rank 0 and S 0 α ± is the first column in the modular S-matrix of TFT±. From the dictionary, we derive the lower bound on F, F ≥ − log 5 − 5 10 ≃ 0.642965, which holds for any rank 0 SCFT. The bound is saturated by the minimal N = 4 SCFT proposed by Gang-Yamazaki, whose associated topological theories are both the Lee-Yang TQFT. We explicitly work out the (rank 0 SCFT)/(non-unitary TQFTs) correspondence for infinitely many examples. [ABSTRACT FROM AUTHOR]

Although many studies have been continuously conducted to reduce the power consumption of a resistive random access memory (RRAM) cross-point array with the current-compliance effect, it has been difficult yet to realize intrinsic self-compliance effects in an RRAM device itself. In this study, a simple oxygen-rich TiOy layer is inserted into the Al2O3/TiOx-based RRAM stack as a current suppression layer, and XPS analysis is provided to compare the stoichiometry of the TiOx and TiOy layers. A self-compliance region is formed between the different breakdown voltages of Al2O3 and TiOy layers, and a relatively thinner current path is formed in the Al2O3 layer than a device without the TiOy layer and the overall current level is significantly decreased since the TiOy layer limits the overshoot current. [ABSTRACT FROM AUTHOR]

This study implements a highly uniform 3D vertically stack resistive random‐access memory (VRRAM) with a four‐layer contact hole structure. The fabrication process of a four‐layer VRRAM is demonstrated, and its physical and electrical properties are thoroughly examined. X‐ray photoelectron spectroscopy and transmission electron microscopy are employed to analyze the chemical distribution and physical structure of the VRRAM device. Multilevel capability, reliable endurance (>104 cycles), and retention (104 s) are successfully obtained. Synaptic memory plasticity, such as spike time‐dependent plasticity, spike rate‐dependent plasticity, excitatory post‐synaptic current, paired‐pulse facilitation, and long‐term potentiation and depression is presented. Finally, the vector‐matrix multiplication (VMM) operation is conducted on a 4 × 12 VRRAM array, according to the low resistance state ratio. It is ascertained that the accuracy drop, which can occur due to VMM error, can be limited to a decrease of less than 0.44% point. Utilizing the high‐density, multilevel, and biological characteristics of VRRAM, it is possible to implement high‐performance neuromorphic systems that require densely integrated synaptic devices. [ABSTRACT FROM AUTHOR]

Graphical abstract Steric hindrance of diethylzinc adsorption disappears at 150–200 °C. Highlights • Governing factors of growth per cycle (gpc) were identified depending on growth temperature. • The saturated gpc values at T < 150 °C are governed by steric hindrance. • Above 200 °C, the saturated gpc values are decided by surface hydroxyl concentration. • Effective activation energy of the DEZ adsorption was found to be ∼8.50 kJ/mol. Abstract We present a kinetic model for the reaction of diethylzinc (DEZ) adsorption in atomic layer deposition (ALD) of ZnO from DEZ and water. The proposed model has been verified by comparing kinetic experimental data to the prediction of the model in the temperature range of 60–200 °C. In this model, DEZ molecules are molecularly adsorbed on the hydroxyl-terminated surface in the first elementary reaction. Then the molecularly-adsorbed DEZs either desorb from the surface, or undergo an irreversible ligand exchange reaction and form monoethylzinc (MEZ)-terminated surface by liberating ethane molecules. According to the integrated rate law of the model, as the exposure time of DEZ increases, the growth per cycle (gpc) of ALD, i.e., the thickness increment per cycle, rapidly increases and then saturates showing the self-limiting growth behavior. The required DEZ exposure time to reach the saturated gpc value is shortened when the chemical equilibrium between the molecular adsorption and desorption shifts toward the adsorption, as this leads to higher effective rate constant in the overall mechanism of the DEZ adsorption. Although the saturated gpc value is primarily governed by the hydroxyl concentration on the ZnO surface, it is also heavily influenced by the steric hindrance due to the bulkiness of the ethyl ligands. We have determined that the critical temperature at which the steric hindrance disappears is around 150–200 °C, by investigating the variation of the hydroxyl concentration with temperature. At temperatures lower than 150 °C, we have observed that the saturated gpc value is governed by the steric hindrance rather than the hydroxyl concentration. However, the saturated gpc value at 200 °C has been achieved purely by the hydroxyl concentration. In other words, all hydroxyl groups at 200 °C have been consumed and saturated by the MEZ molecules of which the steric hindrance-free concentration has been evaluated to be ∼7.1 /nm2. In addition, the effective activation energy was estimated to be ∼8.50 kJ/mol by using the effective rate constants of all temperatures investigated. [ABSTRACT FROM AUTHOR]

This study describes a novel chemical route to grow ZnS thin films via atomic layer deposition ( ALD). By using diethylzinc and 1,5-pentanedithiol as precursors of Zn and S, respectively, ZnS films are grown on substrates with an atomic precision by repeating self-limiting chemisorption of each precursor. The growth-per-cycle of the ALD process is around 0.1 Å per cycle at 150°C, and the as-grown films are characterized to be amorphous ZnS by X-ray diffraction and X-ray photoelectron spectroscopy. [ABSTRACT FROM AUTHOR]

Mobile phones emit radiofrequency (RF) electromagnetic waves (EMWs), a low-level RF that can be absorbed by the human body and exert potential adverse effects on the brain, heart, endocrine system, and reproductive function. Owing to the novel findings of numerous studies published since 2012 regarding the effect of mobile phone use on sperm quality, we conducted a systematic review and updated meta-analysis to determine whether the exposure to RF-EMWs affects human sperm quality. This study was conducted in accordance with the PRISMA guidelines. The outcome measures depicting sperm quality were motility, viability, and concentration, which are the most frequently used parameters in clinical settings to assess fertility. We evaluated 18 studies that included 4280 samples. Exposure to mobile phones is associated with reduced sperm motility, viability, and concentration. The decrease in sperm quality after RF-EMW exposure was not significant, even when the mobile phone usage increased. This finding was consistent across experimental in vitro and observational in vivo studies. Accumulated data from in vivo studies show that mobile phone usage is harmful to sperm quality. Additional studies are needed to determine the effect of the exposure to EMWs from new mobile phone models used in the present digital environment. [ABSTRACT FROM AUTHOR]

Abstract: Using a chemical genetic approach, we identified four novel physiological substrates of Hog1 kinase (Krs1, Tdh3, Hsp26, and Shm2). These substrates suggest plausible mechanisms for actin reorganization, cell cycle arrest and regulation of protein synthesis observed upon osmotic stress. We further show that the human homolog of Shm2 (SHMT1) is a novel physiological substrate of p38 MAP kinase in vitro and in vivo. Down-regulation of its enzymatic activity was observed following p38-mediated phosphorylation revealing a potential cancer-modulating property of p38 MAP kinase. This screen has uncovered several novel Hog1 substrates that provide new avenues for investigation into the mechanism of osmoadaptation by this kinase. [Copyright &y& Elsevier]

In this study, oxygen‐rich TiOy and TiOx layers are intentionally designed to have different oxygen compositions, functioning as an overshoot suppression layer (OSL) and oxygen reservoirs. Furthermore, by natural oxidation reactions occurring between the TiOy/TiOx/Al2O3 switching layer and the Pt/Al top electrode, an additional AlOy layer can be induced to act as an additional OSL. The proposed annealing process accelerates the oxidation reaction of AlOy/TiOy OSLs, thereby enhancing the self‐compliance feature of devices. Moreover, the ultrathin AlN serves as an oxygen barrier layer (OBL) that inhibits the movement of oxygen ions at the interface between the Al2O3 layer and the Pt/Ti bottom electrode. The optimized devices are tested by DC sweep and pulses for neuromorphic computing systems. To realize biological synapse characteristics, several key synaptic memory plasticities are proposed. Finally, a 24 × 24 crossbar array based on the 0T‐1R structure, incorporating optimized AlOy/TiOy OSLs and OBL via the annealing process, is characterized. During the electroforming step, all specified target cells (marked with the letters “ESDL”) achieved self‐compliance at low current levels without experiencing hard‐breakdown failures or interference among neighboring cells. The successful array performance is demonstrated by the accurate tuning of target weights. [ABSTRACT FROM AUTHOR]

Studies the solution structure of the c-terminal core domain of human TFIIB, a component of the machinery that transcribes protein-coding genes. Similarity of the TFIIB to cyclin A; Interaction of the TFIIB with TATA-binding protein; Identification of the three-dimensional structure of the TFIIB using multidimensional heteronuclear magnetic resonance spectroscopy.

Isolates and characterizes the function of the yeast counterpart of Dr1/DRAP1 complex, a general repressor in humans. Genes encoding the complex; Evidence for an effect on the accumulation of RNA polymerase III transcripts from the overexpression of Dr1 in vivo; Indication that Dr1 directly targets TBP, a global regulator of transcription.

We consider the analogue of Seiberg duality for two-dimensional N = (2, 2) gauge theory with orthogonal gauge groups and with fundamental chiral multiplets proposed by Hori. Following Hori, when we consider O(k) gauge group as the (semi)-direct product of SO(k) ⋉ Z2, we have to consider two kinds of the theories O±(k) depending on the orbifold action of Z2. We give the evidences for the proposed dualities by working out the elliptic genus of dual pair. The matching of the elliptic genus is worked out perfectly for the proposed dualities. [ABSTRACT FROM AUTHOR]

As interest in artificial intelligence (AI) and relevant hardware technologies has been developed rapidly, algorithms and network structures have become significantly complicated, causing serious power consumption issues because an enormous amount of computation is required. Neuromorphic computing, a hardware AI technology with memory devices, has emerged to solve this problem. For this application, multilevel operations of synaptic devices are important to imitate floating point weight values in software AI technologies. Furthermore, weight transfer methods to desired weight targets must be arranged for off-chip training. From this point of view, we fabricate 32 × 32 memristor crossbar array and verify the 3-bit multilevel operations. The programming accuracy is verified for 3-bit quantized levels by applying a reset-voltage-control programming scheme to the fabricated TiOx/Al2O3-based memristor array. After that, a synapse composed of two differential memristors and a fully-connected neural network for modified national institute of standards and technology (MNIST) pattern recognition are constructed. The trained weights are post-training quantized in consideration of the 3-bit characteristics of the memristor. Finally, the effect of programming error on classification accuracy is verified based on the measured data, and we obtained 98.12% classification accuracy for MNIST data with the programming accuracy of 1.79% root-mean-square-error. These results imply that the proposed reset-voltage-control programming scheme can be utilized for a precise tuning, and expected to contribute for the development of a neuromorphic system capable of highly precise weight transfer. [ABSTRACT FROM AUTHOR]

NO3− is a prominent waste byproduct in ammunition industries, and valorization of NO3− into NH3 is desired. NH3 synthesized from waste using renewable energy could be used as an energy storage medium. For such a process, higher selectivity, stability, and enhanced activity per unit mass of catalyst is required. This work reports CoO nanoclusters functionalized on graphene as an efficient catalyst for electrochemical NO3− reduction to NH3 with >98% NH3 Faradaic efficiency and an NH3 current density (geometric) of ≈400 mA cm−2. A highest NH3 yield of 25.63 mol g−1 h−1 is obtained. The developed catalyst prevents the NN coupling step which is required for promoting the selectivity of NH3 over N2. Density functional theory calculations reveal that CoO NC/graphene has similar selectivity to that of polycrystalline Co and that higher activity can be obtained with lower catalyst loading. [ABSTRACT FROM AUTHOR]

To analyze the effect of the intrinsic variations of the memristor device on the neuromorphic system, we fabricated 32 × 32 Al2O3/TiO x -based memristor crossbar array and implemented 3 bit multilevel conductance as weight quantization by utilizing the switching characteristics to minimize the performance degradation of the neural network. The tuning operation for 8 weight levels was confirmed with a tolerance of ±4 μ A (±40 μ S). The endurance and retention characteristics were also verified, and the random telegraph noise (RTN) characteristics were measured according to the weight range to evaluate the internal stochastic variation effect. Subsequently, a memristive neural network was constructed by off-chip training with differential memristor pairs for the Modified National Institute of Standards and Technology (MNIST) handwritten dataset. The pre-trained weights were quantized, and the classification accuracy was evaluated by applying the intrinsic variations to each quantized weight. The intrinsic variations were applied using the measured weight inaccuracy given by the tuning tolerance, RTN characteristics, and the fault device yield. We believe these results should be considered when the pre-trained weights are transferred to a memristive neural network by off-chip training. [ABSTRACT FROM AUTHOR]

On-chip learning is an effective method for adjusting artificial neural networks in neuromorphic computing systems by considering hardware intrinsic properties. However, it faces challenges due to hardware nonidealities, such as the nonlinearity of potentiation and depression and limitations on fine weight adjustment. In this study, we propose a threshold learning algorithm for a variation-tolerant ternary neural network in a memristor crossbar array. This algorithm utilizes two tightly separated resistance states in memristive devices to represent weight values. The high-resistance state (HRS) and low-resistance state (LRS) defined as read current of < 0.1 μA and > 1 μA, respectively, were successfully programmed in a 32 × 32 crossbar array, and exhibited half-normal distributions due to the programming method. To validate our approach experimentally, a 64 × 10 single-layer fully connected network were trained in the fabricated crossbar for an 8 × 8 MNIST dataset using the threshold learning algorithm, where the weight value is updated when a gradient determined by backpropagation exceeds a threshold value. Thanks to the large margin between the two states of the memristor, we observed only a 0.42 % drop in classification accuracy compared to the baseline network results. The threshold learning algorithm is expected to alleviate the programming burden and be utilized in variation-tolerant neuromorphic architectures. [ABSTRACT FROM AUTHOR]

Abstract: Irreversible HER/erbB inhibitors selectively inhibit HER-family kinases by targeting a unique cysteine residue located within the ATP-binding pocket. Sequence alignment reveals that this rare cysteine is also present in ten other protein kinases including all five Tec-family members. We demonstrate that the Tec-family kinase Bmx is potently inhibited by irreversible modification at Cys496 by clinical stage EGFR inhibitors such as CI-1033. This cross-reactivity may have significant clinical implications. [Copyright &y& Elsevier]

Electrochemical Reduction of Nitrates on CoO Nanoclusters-Functionalized Graphene with Highest Mass Activity and Nearly 100% Selectivity to Ammonia (Adv. Electrochemical ammonia synthesis, electrochemical nitrate reduction, nitrate removal, solar-to-ammonia efficiency Keywords: electrochemical ammonia synthesis; electrochemical nitrate reduction; nitrate removal; solar-to-ammonia efficiency EN electrochemical ammonia synthesis electrochemical nitrate reduction nitrate removal solar-to-ammonia efficiency 1 1 1 05/09/23 20230505 NES 230505 B Electrochemical Ammonia Synthesis b The electrochemical reduction of nitrate to ammonia represents a promising pathway towards wastewater remediation, energy storage, and fertilizer production. [Extracted from the article]

Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase belonging to the insulin receptor superfamily. Expression of ALK in normal human tissues is only found in a subset of neural cells, however it is involved in the genesis of several cancers through genetic aberrations involving translocation of the kinase domain with multiple fusion partners (e.g., NPM-ALK in anaplastic large cell lymphoma ALCL or EML4-ALK in non-small cell lung cancer) or activating mutations in the full-length receptor resulting in ligand-independent constitutive activation (e.g., neuroblastoma). Here we are reporting the discovery of novel and selective anaplastic lymphoma kinase inhibitors from specific modifications of the 2,4-diaminopyridine core present in TAE684 and LDK378. Synthesis, structure activity relationships (SAR), absorption, distribution, metabolism, and excretion (ADME) profile, and in vivo efficacy in a mouse xenograft model of anaplastic large cell lymphoma are described. [ABSTRACT FROM AUTHOR]

The synthesis, preclinical profile,and in vivo efficacy in rat xenograft models of the novel and selectiveanaplastic lymphoma kinase inhibitor 15b(LDK378) aredescribed. In this initial report, preliminary structure–activityrelationships (SARs) are described as well as the rational designstrategy employed to overcome the development deficiencies of thefirst generation ALK inhibitor 4(TAE684). Compound 15bis currently in phase 1 and phase 2 clinical trials withsubstantial antitumor activity being observed in ALK-positive cancerpatients. [ABSTRACT FROM AUTHOR]

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens. [ABSTRACT FROM AUTHOR]

Abstract: Influences of nitrogen on the passivity of Fe–20Cr–(0, 1.1)N alloys were examined by in situ electrochemical techniques. Nitrogen was incorporated in the form of (Fe, Cr)-nitrides in the passive film, and Cr was enriched in the film of the alloy with nitrogen. Photocurrent analysis demonstrated that the structure of passive film formed on Fe–20Cr–1.1N alloy is Cr-substituted γ-Fe2O3 with (Fe, Cr)-nitrides. Mott–Schottky analysis revealed that the film formed on Fe–20Cr–1.1N contained higher Cr6+ and lower Cr3+ vacancy concentrations compared with that on Fe–20Cr alloy. All of these results were associated with the enhanced protectiveness of the film on Fe–20Cr–1.1N. [Copyright &y& Elsevier]

Studies human TFIIB, an important factor in the transcription of proteins-coding genes by ribonucleic acid (RNA) polymerase II. Contents of TFIIB; Structure of the TFIIB domain; Compares the structure of TFIIB to the crystal structure of TFIIBc; Indications of an analysis of backbone N relaxation; Suggestions of the results of the study.

A correction to the article "The Cancer Cell Line Encyclopedia enables predictive modeling," that was published in the previous issue.