Design of Event-Triggered Fault-Tolerant Control for Stochastic Systems with Time-Delays

This paper proposes two novel, event-triggered fault-tolerant control strategies for a class of stochastic systems with state delays. The plant is disturbed by a Gaussian process, actuator faults, and unknown disturbances. First, a special case about fault signals that are coupled to the unknown disturbances is discussed, and then a fault-tolerant strategy is designed based on an event condition on system states. Subsequently, a send-on-delta transmission framework is established to deal with the problem of fault-tolerant control strategy against fault signals separated from the external disturbances. Two criteria are provided to design feedback controllers in order to guarantee that the systems are exponentially mean-square stable, and the corresponding H∞-norm disturbance attenuation levels are achieved. Two theorems were obtained by synthesizing the feedback control gains and the desired event conditions in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are provided to illustrate the effectiveness of the proposed theoretical results

Towards provably efficient quantum algorithms for large-scale machine-learning models

Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as $\mathcal{O}(T^2 \times \text{polylog}(n))$, where $n$ is the size of the models and $T$ is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that fault-tolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems.Comment: 7+30 pages, 3+5 figure

Towards provably efficient quantum algorithms for large-scale machine-learning models

Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as O(T2 x polylog(n)), where n is the size of the models and T is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that fault-tolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems

Failure of early mycological clearance in HIV-negative cryptococcal meningitis

BACKGROUND: Negative cerebrospinal fluid (CSF) cultures at 2 weeks after antifungal treatment (early mycological clearance [EMC]) should be a treatment goal of cryptococcal meningitis (CM). However, EMC in human immunodeficiency virus (HIV)-negative patients with CM is poorly understood. METHODS: We conducted a retrospective review of medical records and 1-year follow-up of 141 HIV-negative patients with CM with an initial positive CSF culture for RESULTS: Of 141 patients, 28 (19.9%) had EMC failure. The 1-year mortality rate was 5.7% (8/141). Multivariate analysis showed that non-amphotericin B (AmB)-based regimens, baseline log CONCLUSIONS: EMC failure in HIV-negative CM is attributed to non-AmB-based therapy and is associated with lo

Endocytic Adaptor Protein HIP1R Controls Intracellular Trafficking of Epidermal Growth Factor Receptor in Neuronal Dendritic Development

Huntington-interacting protein 1-related protein (HIP1R) was identified on the basis of its structural homology with HIP1. Based on its domain structure, HIP1R is a putative endocytosis-related protein. Our previous study had shown that knockdown of HIP1R induces a dramatic decrease of dendritic growth and branching in cultured rat hippocampal neurons. However, the underlying mechanism remains elucidative. In this study, we found that knockdown of HIP1R impaired the endocytosis of activated epidermal growth factor receptor (EGFR) and the consequent activation of the downstream ERK and Akt proteins. Meanwhile, it blocked the EGF-induced dendritic outgrowth. We also showed that the HIP1R fragment, amino acids 633–822 (HIP1R633–822), interacted with EGFR and revealed a dominant negative effect in disrupting the HIP1R-EGFR interaction-mediated neuronal development. Collectively, these results reveal a novel mechanism that HIP1R plays a critical role in neurite initiation and dendritic branching in cultured hippocampal neurons via mediating the endocytosis of EGFR and downstream signaling

Inhibition of HDAC activity directly reprograms murine embryonic stem cells to trophoblast stem cells

Embryonic stem cells (ESCs) can differentiate into all cell types of the embryonic germ layers. ESCs can also generate totipotent 2C-like cells and trophectodermal cells. However, these latter transitions occur at low frequency due to epigenetic barriers, the nature of which is not fully understood. Here, we show that treating mouse ESCs with sodium butyrate (NaB) increases the population of 2C-like cells and enables direct reprogramming of ESCs into trophoblast stem cells (TSCs) without a transition through a 2C-like state. Mechanistically, NaB inhibits histone deacetylase activities in the LSD1-HDAC1/2 corepressor complex. This increases acetylation levels in the regulatory regions of both 2C- and TSC-specific genes, promoting their expression. In addition, NaB-treated cells acquire the capacity to generate blastocyst-like structures that can develop beyond the implantation stage in vitro and form deciduae in vivo. These results identify how epigenetics restrict the totipotent and trophectoderm fate in mouse ESCs.</p

A 4.8-kW high-efficiency 1050-nm monolithic fiber laser amplifier employing a pump-sharing structure

The power scaling of ytterbium-doped fiber (YDF) lasers emitting at the wavelength range of 1030 nm–1060 nm has been limited by amplified spontaneous emission (ASE), stimulated Raman scattering (SRS) effect, and transverse mode instability (TMI). These effects pose challenges in achieving a high-output power laser within the range of 1030 nm–1060 nm while maintaining a high signal-to-noise ratio. Based on a counter-pumped fiber laser amplifier utilizing our self-developed ytterbium-doped fiber, we have successfully showcased a 4.8-kW laser output at 1050 nm, accompanied by an 85.3% slope efficiency and nearly diffraction-limited beam quality. By effectively applying ASE and TMI, and controlling the Raman Stokes at ∼17 dB below the primary signal wavelength, we have achieved optimal performance at the maximum power level. This high efficiency has been attained through a pump-sharing structure combined with cost-effective, non-wavelength-stabilized 976-nm laser diodes

Pain-causing stinging nettle toxins target TMEM233 to modulate NaV1.7 function

Voltage-gated sodium (NaV) channels are critical regulators of neuronal excitability and are targeted by many toxins that directly interact with the pore-forming α subunit, typically via extracellular loops of the voltage-sensing domains, or residues forming part of the pore domain. Excelsatoxin A (ExTxA), a pain-causing knottin peptide from the Australian stinging tree Dendrocnide excelsa, is the first reported plant-derived NaV channel modulating peptide toxin. Here we show that TMEM233, a member of the dispanin family of transmembrane proteins expressed in sensory neurons, is essential for pharmacological activity of ExTxA at NaV channels, and that co-expression of TMEM233 modulates the gating properties of NaV1.7. These findings identify TMEM233 as a previously unknown NaV1.7-interacting protein, position TMEM233 and the dispanins as accessory proteins that are indispensable for toxin-mediated effects on NaV channel gating, and provide important insights into the function of NaV channels in sensory neurons