From microscope to micropixels: A rapid review of artificial intelligence for the peripheral blood film.

Artificial intelligence (AI) and its application in classification of blood cells in the peripheral blood film is an evolving field in haematology. We performed a rapid review of the literature on AI and peripheral blood films, evaluating the condition studied, image datasets, machine learning models, training set size, testing set size and accuracy. A total of 283 studies were identified, encompassing 6 broad domains: malaria (n = 95), leukemia (n = 81), leukocytes (n = 72), mixed (n = 25), erythrocytes (n = 15) or Myelodysplastic syndrome (MDS) (n = 1). These publications have demonstrated high self-reported mean accuracy rates across various studies (95.5% for malaria, 96.0% for leukemia, 94.4% for leukocytes, 95.2% for mixed studies and 91.2% for erythrocytes), with an overall mean accuracy of 95.1%. Despite the high accuracy, the challenges toward real world translational usage of these AI trained models include the need for well-validated multicentre data, data standardisation, and studies on less common cell types and non-malarial blood-borne parasites.

Ultrafast Near-Ideal Phase-Change Memristive Physical Unclonable Functions Driven by Amorphous State Variations.

There is an ever-increasing demand for next-generation devices that do not require passwords and are impervious to cloning. For traditional hardware security solutions in edge computing devices, inherent limitations are addressed by physical unclonable functions (PUF). However, realizing efficient roots of trust for resource constrained hardware remains extremely challenging, despite excellent demonstrations with conventional silicon circuits and archetypal oxide memristor-based crossbars. An attractive, down-scalable approach to design efficient cryptographic hardware is to harness memristive materials with a large-degree-of-randomness in materials state variations, but this strategy is still not well understood. Here, the utilization of high-degree-of-randomness amorphous (A) state variations associated with different operating conditions via thermal fluctuation effects is demonstrated, as well as an integrated framework for in memory computing and next generation security primitives, viz., APUF, for achieving secure key generation and device authentication. Near ideal uniformity and uniqueness without additional initial writing overheads in weak memristive A-PUF is achieved. In-memory computing empowers a strong exclusive OR (XOR-) and-repeat A PUF construction to avoid machine learning attacks, while rapid crystallization processes enable large-sized-key reconfigurability. These findings pave the way for achieving a broadly applicable security primitive for enhancing antipiracy of integrated systems and product authentication in supply chains.

Activating Silent Synapses in Sulfurized Indium Selenide for Neuromorphic Computing.

The transformation from silent to functional synapses is accompanied by the evolutionary process of human brain development and is essential to hardware implementation of the evolutionary artificial neural network but remains a challenge for mimicking silent to functional synapse activation. Here, we developed a simple approach to successfully realize activation of silent to functional synapses by controlled sulfurization of chemical vapor deposition-grown indium selenide crystals. The underlying mechanism is attributed to the migration of sulfur anions introduced by sulfurization. One of our most important findings is that the functional synaptic behaviors can be modulated by the degree of sulfurization and temperature. In addition, the essential synaptic behaviors including potentiation/depression, paired-pulse facilitation, and spike-rate-dependent plasticity are successfully implemented in the partially sulfurized functional synaptic device. The developed simple approach of introducing sulfur anions in layered selenide opens an effective new avenue to realize activation of silent synapses for application in evolutionary artificial neural networks.

Tunable Resistive Switching Enabled by Malleable Redox Reaction in the Nano-Vacuum Gap.

Neuromorphic computing has emerged as a highly promising alternative to conventional computing. The key to constructing a large-scale neural network in hardware for neuromorphic computing is to develop artificial neurons with leaky integrate-and-fire behavior and artificial synapses with synaptic plasticity using nanodevices. So far, these two basic computing elements have been built in separate devices using different materials and technologies, which poses a significant challenge to system design and manufacturing. In this work, we designed a resistive device embedded with an innovative nano-vacuum gap between a bottom electrode and a mixed-ionic-electronic-conductor (MIEC) layer. Through redox reaction on the MIEC surface, metallic filaments dynamically grew within the nano-vacuum gap. The nano-vacuum gap provided an additional control factor for controlling the evolution dynamics of metallic filaments by tuning the electron tunneling efficiency, in analogy to a pseudo-three-terminal device, resulting in tunable switching behavior in various forms from volatile to nonvolatile switching in a single device. Our device demonstrated cross-functions, in particular, tunable neuronal firing and synaptic plasticity on demand, providing seamless integration for building large-scale artificial neural networks for neuromorphic computing.

Review of tandem repeat search tools: a systematic approach to evaluating algorithmic performance.

The prevalence of tandem repeats in eukaryotic genomes and their association with a number of genetic diseases has raised considerable interest in locating these repeats. Over the last 10-15 years, numerous tools have been developed for searching tandem repeats, but differences in the search algorithms adopted and difficulties with parameter settings have confounded many users resulting in widely varying results. In this review, we have systematically separated the algorithmic aspect of the search tools from the influence of the parameter settings. We hope that this will give a better understanding of how the tools differ in algorithmic performance, their inherent constraints and how one should approach in evaluating and selecting them.

Enabling universal memory by overcoming the contradictory speed and stability nature of phase-change materials.

The quest for universal memory is driving the rapid development of memories with superior all-round capabilities in non-volatility, high speed, high endurance and low power. Phase-change materials are highly promising in this respect. However, their contradictory speed and stability properties present a key challenge towards this ambition. We reveal that as the device size decreases, the phase-change mechanism changes from the material inherent crystallization mechanism (either nucleation- or growth-dominated), to the hetero-crystallization mechanism, which resulted in a significant increase in PCRAM speeds. Reducing the grain size can further increase the speed of phase-change. Such grain size effect on speed becomes increasingly significant at smaller device sizes. Together with the nano-thermal and electrical effects, fast phase-change, good stability and high endurance can be achieved. These findings lead to a feasible solution to achieve a universal memory.

Ultrafast switching in nanoscale phase-change random access memory with superlattice-like structures.

Phase-change random access memory cells with superlattice-like (SLL) GeTe/Sb(2)Te(3) were demonstrated to have excellent scaling performance in terms of switching speed and operating voltage. In this study, the correlations between the cell size, switching speed and operating voltage of the SLL cells were identified and investigated. We found that small SLL cells can achieve faster switching speed and lower operating voltage compared to the large SLL cells. Fast amorphization and crystallization of 300 ps and 1 ns were achieved in the 40 nm SLL cells, respectively, both significantly faster than those observed in the Ge(2)Sb(2)Te(5) (GST) cells of the same cell size. 40 nm SLL cells were found to switch with low amorphization voltage of 0.9 V when pulse-widths of 5 ns were employed, which is much lower than the 1.6 V required by the GST cells of the same cell size. These effects can be attributed to the fast heterogeneous crystallization, low thermal conductivity and high resistivity of the SLL structures. Nanoscale PCRAM with SLL structure promises applications in high speed and low power memory devices.

Multispeed rewritable optical-recording method with an initialization-free phase-change disk.

A new method of multispeed rewritable optical recording is presented. An initialization-free phase-change optical disk is proposed as a candidate for multispeed rewritable optical recording. The simulated results of the initialization-free disk at different linear velocities show that the cooling rate increases from approximately 18.69% to 37.96%. A model that combines the crystallization acceleration effect due to the additional layers and the rapid cooling rate due to the initialization-free disk structure is proposed as the physical mechanism of the multispeed recording method with an initialization-free disk. The dynamic optical-recording properties of the initialization-free DVD-RAM disk at different recording speeds shows that the initialization-free phase-change optical-recording disk is compatible with a broad range of recording speeds from 3.49 to 12.21 m/s.