Review of Recently Progress on Neural Electronics and Memcomputing Applications in Intrinsic SiOx-Based Resistive Switching Memory

In this chapter, we focus on the recent process on memcomputing (memristor + computing) in intrinsic SiOx-based resistive switching memory (ReRAM or called memristor). In the first section of the chapter, we investigate neuromorphic computing by mimicking the synaptic behaviors in integrating one-diode and one-resistive switching element (1D-1R) architecture. The power consumption can be minimized further in synaptic functions because sneak-path current has been suppressed and the capability for spike-induced synaptic behaviors has been demonstrated, representing critical milestones and achievements for the application of conventional SiOx-based materials in future advanced neuromorphic computing. In the next section of chapter, we will discuss an implementation technique of implication operations for logic-in-memory computation by using a SiOx-based memristor. The implication function and its truth table have been implemented with the unipolar or nonpolar operation scheme. Furthermore, a circuit with 1D-1R architecture with a 4 × 4 crossbar array has been demonstrated, which realizes the functionality of a one-bit full adder as same as CMOS logic circuits with lower design area requirement. This chapter suggests that a simple, robust approach to realize memcomputing chips is quite compatible with large-scale CMOS manufacturing technology by using an intrinsic SiOx-based memristor

Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection

Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification. Nevertheless, such methods usually require laborious object-level annotations (i.e., object labels and bounding boxes) for effective learning of the object-level visual features. In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations. Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs. The WSD model is the teacher model and the classification model is the student model. After this cross-task knowledge distillation, the performance of the classification model is significantly improved and the efficiency is maintained since the WSD model can be safely discarded in the test phase. Extensive experiments on two large-scale datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior performances over the state-of-the-art methods on both performance and efficiency.Comment: accepted by ACM Multimedia 2018, 9 pages, 4 figures, 5 table

CMOS and memristive hardware for neuromorphic computing

The ever-increasing processing power demands of digital computers cannot continue to be fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing, which takes inspiration from the highly parallel, low power, high speed, and noise-tolerant computing capabilities of the brain, may provide such a shift. To that end, various aspects of the brain, from its basic building blocks, such as neurons and synapses, to its massively parallel in-memory computing networks have been being studied by the huge neuroscience community. Concurrently, many researchers from across academia and industry have been studying materials, devices, circuits, and systems, to implement some of the functions of networks of neurons and synapses to develop bio-inspired (neuromorphic) computing platforms

Past and future decline of tropical pelagic biodiversity

Author's accepted version (postprint).This is an Accepted Manuscript of an article published by the National Academy of Sciences in PNAS on 26/05/2020.Available online: https://www.pnas.org/content/pnas/117/23/12891.full.pdfA major research question concerning global pelagic biodiversity remains unanswered: when did the apparent tropical biodiversity depression (i.e., bimodality of latitudinal diversity gradient [LDG]) begin? The bimodal LDG may be a consequence of recent ocean warming or of deep-time evolutionary speciation and extinction processes. Using rich fossil datasets of planktonic foraminifers, we show here that a unimodal (or only weakly bimodal) diversity gradient, with a plateau in the tropics, occurred during the last ice age and has since then developed into a bimodal gradient through species distribution shifts driven by postglacial ocean warming. The bimodal LDG likely emerged before the Anthropocene and industrialization, and perhaps ∼15,000 y ago, indicating a strong environmental control of tropical diversity even before the start of anthropogenic warming. However, our model projections suggest that future anthropogenic warming further diminishes tropical pelagic diversity to a level not seen in millions of years.acceptedVersio

Control of dephasing in spin qubits during coherent transport in silicon

One of the key pathways towards scalability of spin-based quantum computing systems lies in achieving long-range interactions between electrons and increasing their inter-connectivity. Coherent spin transport is one of the most promising strategies to achieve this architectural advantage. Experimental results have previously demonstrated high fidelity transportation of spin qubits between two quantum dots in silicon and identified possible sources of error. In this theoretical study, we investigate these errors and analyze the impact of tunnel coupling, magnetic field and spin-orbit effects on the spin transfer process. The interplay between these effects gives rise to double dot configurations that include regimes of enhanced decoherence that should be avoided for quantum information processing. These conclusions permit us to extrapolate previous experimental conclusions and rationalize the future design of large scale quantum processors.Comment: 18 pages, 9 figure

Family history and risk of bladder cancer:an analysis accounting for first- and second-degree relatives

Although evidence suggests that a positive family history of bladder cancer in first-degree relatives is an important risk factor for bladder cancer occurrence, results remain unclear. The influence of family history of non-bladder cancers and more distant relatives on bladder cancer risk is inconsistent. This research therefore, aims to increase the understanding of the association between family history and bladder cancer risk based on worldwide case-control studies. In total 4,327 cases and 8,948 non-cases were included. Pooled odds ratios (ORs), with corresponding 95% confidence intervals (CIs), were obtained using multilevel logistic regression models, adjusted by age, sex, ethnicity, smoking status, and smoking pack-years. The results show bladder cancer risk increased by having a first- or second-degree relative affected with bladder cancer (OR 2.72, 95%CI 1.55-4.77 and OR 1.71, 95%CI 1.22-2.40, respectively), and non-urologic cancers (OR 1.61, 95%CI 1.19-2.18). Moreover, bladder cancer risk increased by number of cancers affected first-degree relatives (for 1 and >1 first-degree relatives: OR 1.42, 95% CI 1.02-2.04; OR 2.67, 95% CI 1.84-3.86, respectively). Our findings highlight an increased bladder cancer risk for a positive bladder cancer family history in first- and second-degree relatives, and indicate a possible greater effect for an increment of numbers of affected relatives

Bounds to electron spin qubit variability for scalable CMOS architectures

Spins of electrons in CMOS quantum dots combine exquisite quantum properties and scalable fabrication. In the age of quantum technology, however, the metrics that crowned Si/SiO2 as the microelectronics standard need to be reassessed with respect to their impact upon qubit performance. We chart the spin qubit variability due to the unavoidable atomic-scale roughness of the Si/SiO$_2$ interface, compiling experiments in 12 devices, and developing theoretical tools to analyse these results. Atomistic tight binding and path integral Monte Carlo methods are adapted for describing fluctuations in devices with millions of atoms by directly analysing their wavefunctions and electron paths instead of their energy spectra. We correlate the effect of roughness with the variability in qubit position, deformation, valley splitting, valley phase, spin-orbit coupling and exchange coupling. These variabilities are found to be bounded and lie within the tolerances for scalable architectures for quantum computing as long as robust control methods are incorporated.Comment: 20 pages, 8 figure

Generating MHV super-vertices in light-cone gauge

We constructe the $\mathcal{N}=1$ SYM lagrangian in light-cone gauge using chiral superfields instead of the standard vector superfield approach and derive the MHV lagrangian. The canonical transformations of the gauge field and gaugino fields are summarised by the transformation condition of chiral superfields. We show that $\mathcal{N}=1$ MHV super-vertices can be described by a formula similar to that of the $\mathcal{N}=4$ MHV super-amplitude. In the discussions we briefly remark on how to derive Nair's formula for $\mathcal{N}=4$ SYM theory directly from light-cone lagrangian.Comment: 25 pages, 7 figures, JHEP3 style; v2: references added, some typos corrected; Clarification on the condition used to remove one Grassmann variabl

Note on Cyclic Sum and Combination Sum of Color-ordered Gluon Amplitudes

Continuing our previous study \cite{Du:2011se} of permutation sum of color ordered tree amplitudes of gluons, in this note, we prove the large-$z$ behavior of their cyclic sum and the combination of cyclic and permutation sums under BCFW deformation. Unlike the permutation sum, the study of cyclic sum and the combination of cyclic and permutation sums is much more difficult. By using the generalized Bern-Carrasco-Johansson (BCJ) relation, we have proved the boundary behavior of cyclic sum with nonadjacent BCFW deformation. The proof of cyclic sum with adjacent BCFW deformation is a little bit simpler, where only Kleiss-Kuijf (KK) relations are needed. Finally we have presented a new observation for partial-ordered permutation sum and applied it to prove the boundary behavior of combination sum with cyclic and permutation.Comment: 21 pages, improved versio

Consistency of high-fidelity two-qubit operations in silicon

The consistency of entangling operations between qubits is essential for the performance of multi-qubit systems, and is a crucial factor in achieving fault-tolerant quantum processors. Solid-state platforms are particularly exposed to inconsistency due to the materials-induced variability of performance between qubits and the instability of gate fidelities over time. Here we quantify this consistency for spin qubits, tying it to its physical origins, while demonstrating sustained and repeatable operation of two-qubit gates with fidelities above 99% in the technologically important silicon metal-oxide-semiconductor (SiMOS) quantum dot platform. We undertake a detailed study of the stability of these operations by analysing errors and fidelities in multiple devices through numerous trials and extended periods of operation. Adopting three different characterisation methods, we measure entangling gate fidelities ranging from 96.8% to 99.8%. Our analysis tools also identify physical causes of qubit degradation and offer ways to maintain performance within tolerance. Furthermore, we investigate the impact of qubit design, feedback systems, and robust gates on implementing scalable, high-fidelity control strategies. These results highlight both the capabilities and challenges for the scaling up of spin-based qubits into full-scale quantum processors