Generating Giant and Tunable Nonlinearity in a Macroscopic Mechanical Resonator from Chemical Bonding Force

Nonlinearity in macroscopic mechanical system plays a crucial role in a wide variety of applications, including signal transduction and processing, synchronization, and building logical devices. However, it is difficult to generate nonlinearity due to the fact that macroscopic mechanical systems follow the Hooke's law and response linearly to external force, unless strong drive is used. Here we propose and experimentally realize a record-high nonlinear response in macroscopic mechanical system by exploring the anharmonicity in deforming a single chemical bond. We then demonstrate the tunability of nonlinear response by precisely controlling the chemical bonding interaction, and realize a cubic elastic constant of \mathversion{bold}$2 \times 10^{18}~{\rm N}/{\rm m^3}$, many orders of magnitude larger in strength than reported previously. This enables us to observe vibrational bistate transitions of the resonator driven by the weak Brownian thermal noise at 6~K. This method can be flexibly applied to a variety of mechanical systems to improve nonlinear responses, and can be used, with further improvements, to explore macroscopic quantum mechanics

Scalar Quantization as Sparse Least Square Optimization

Quantization can be used to form new vectors/matrices with shared values close to the original. In recent years, the popularity of scalar quantization for value-sharing applications has been soaring as it has been found huge utilities in reducing the complexity of neural networks. Existing clustering-based quantization techniques, while being well-developed, have multiple drawbacks including the dependency of the random seed, empty or out-of-the-range clusters, and high time complexity for a large number of clusters. To overcome these problems, in this paper, the problem of scalar quantization is examined from a new perspective, namely sparse least square optimization. Specifically, inspired by the property of sparse least square regression, several quantization algorithms based on $l_1$ least square are proposed. In addition, similar schemes with $l_1 + l_2$ and $l_0$ regularization are proposed. Furthermore, to compute quantization results with a given amount of values/clusters, this paper designed an iterative method and a clustering-based method, and both of them are built on sparse least square. The paper shows that the latter method is mathematically equivalent to an improved version of k-means clustering-based quantization algorithm, although the two algorithms originated from different intuitions. The algorithms proposed were tested with three types of data and their computational performances, including information loss, time consumption, and the distribution of the values of the sparse vectors, were compared and analyzed. The paper offers a new perspective to probe the area of quantization, and the algorithms proposed can outperform existing methods especially under some bit-width reduction scenarios, when the required post-quantization resolution (number of values) is not significantly lower than the original number

Optical Quantum Sensing for Agnostic Environments via Deep Learning

Optical quantum sensing promises measurement precision beyond classical sensors termed the Heisenberg limit (HL). However, conventional methodologies often rely on prior knowledge of the target system to achieve HL, presenting challenges in practical applications. Addressing this limitation, we introduce an innovative Deep Learning-based Quantum Sensing scheme (DQS), enabling optical quantum sensors to attain HL in agnostic environments. DQS incorporates two essential components: a Graph Neural Network (GNN) predictor and a trigonometric interpolation algorithm. Operating within a data-driven paradigm, DQS utilizes the GNN predictor, trained on offline data, to unveil the intrinsic relationships between the optical setups employed in preparing the probe state and the resulting quantum Fisher information (QFI) after interaction with the agnostic environment. This distilled knowledge facilitates the identification of optimal optical setups associated with maximal QFI. Subsequently, DQS employs a trigonometric interpolation algorithm to recover the unknown parameter estimates for the identified optical setups. Extensive experiments are conducted to investigate the performance of DQS under different settings up to eight photons. Our findings not only offer a new lens through which to accelerate optical quantum sensing tasks but also catalyze future research integrating deep learning and quantum mechanics

An Architecture of Deterministic Quantum Central Processing Unit

We present an architecture of QCPU(Quantum Central Processing Unit), based on the discrete quantum gate set, that can be programmed to approximate any n-qubit computation in a deterministic fashion. It can be built efficiently to implement computations with any required accuracy. QCPU makes it possible to implement universal quantum computation with a fixed, general purpose hardware. Thus the complexity of the quantum computation can be put into the software rather than the hardware.Comment: 4 pages, 4 figures, 1 tabl

The Transient Supercooling Enhancement For A Pulsed Thermoelectric Cooler (TEC)

Once TEC excitated by a high-voltage pulse, there exists a transient thermoelectric supercooling effect, which can be enhanced by keeping on increasing the Peltier cooling effect to compensate for the negative self-heating from the Joule heating effect and Fourier heat conduction effect. After superimposing an additional voltage pulse over a steady-state reference value in a short time scale, abrupt temperature drop will be produced by several more degrees below the steady-state cold junction temperature, and even against the earlier arrival of excessively Joule heating-dominated heat accumulation at cold junctions. Most previous work mainly focused on the minimum supercooling temperature or the maximum supercooling capacity achievable for a conventional thermoelectric module based on bismuth-telluride alloys. Nevertheless, three key process control parameters, with respect to the system response time for the supercooling temperature, the holding time of the supercooling state and the recovery time back to the reference steady state, were almost overlooked. In this work, analytical solutions on the optimization of pulse shapes upon the thermal-electrical conversion mechanism were investigated, for exploring the dynamic behaviours of the main thermoelectric effects respectively on the transient response for the cold junction temperature drop and the supercooling enhancement degree during pulsed operation. Furthermore, by the combinatorial optimization of the above process control parameters and pulse shapes, the optimal characteristic parameters for TE devices pulsed with supercooling are derived. The results indicate that, the monotonically increasing quarter-wave pulse shapes (especially the quarter-sine voltage excitation), combined with the optimized pulse amplitude of 2.5 times and pulse duration of 10s, show a greater advantage to achieve high sensitivity and stability, and require less energy to reach the minimum temperature. Also, it has contributed to an increase on the effective FIGure-of-merit ratio offrom the previous value of 1.76 to a maximum of 2.06 (namely improved by 17% ), as well as smaller temperature differences between hot and cold junctions. The discussions can be served as a theoretical basis for a pulsed TEC to improve the additional supercooling effect, and also prevent extensive heating of the material after the minimum temperature is reached, which may be attractive for compact thermal system to come up to the localized cooling level of high power packaging

Integrative genomic analyses reveal clinically relevant long non-coding RNA in human cancer

Despite growing appreciations of the importance of long non-coding RNA (lncRNA) in normal physiology and disease, our knowledge of cancer-related lncRNA remains limited. By repurposing microarray probes, we constructed the expression profile of 10,207 lncRNA genes in approximately 1,300 tumors over four different cancer types. Through integrative analysis of the lncRNA expression profiles with clinical outcome and somatic copy number alteration (SCNA), we identified lncRNA that are associated with cancer subtypes and clinical prognosis, and predicted those that are potential drivers of cancer progression. We validated our predictions by experimentally confirming prostate cancer cell growth dependence on two novel lncRNA. Our analysis provided a resource of clinically relevant lncRNA for development of lncRNA biomarkers and identification of lncRNA therapeutic targets. It also demonstrated the power of integrating publically available genomic datasets and clinical information for discovering disease associated lncRNA

Comparison of lower extremity atherosclerosis in diabetic and non-diabetic patients using multidetector computed tomography

BACKGROUND: Lower extremity atherosclerosis (LEA) is among the most serious diabetic complications and leads to non-traumatic amputations. The recently developed dual-source CT (DSCT) and 320- multidetector computed tomography (MDCT) may help to detect plaques more precisely. The aim of our study was to evaluate the differences in LEA between diabetic and non-diabetic patients using MDCT angiography. METHODS: DSCT and 320-MDCT angiographies of the lower extremities were performed in 161 patients (60 diabetic and 101 non-diabetic). The plaque type, distribution, shape and obstructive natures were compared. RESULTS: Compared with non-diabetic patients, diabetic patients had higher peripheral neuropathy, history of cerebrovasuclar infarction and hypertension rates. A total of 2898 vascular segments were included in the analysis. Plaque and stenosis were detected in 681 segments in 60 diabetic patients (63.1%) and 854 segments in 101 non-diabetic patients (46.9%; p <0.05). Regarding these plaques, diabetic patients had a higher incidence of mixed plaques (34.2% vs. 27.1% for non-diabetic patients). An increased moderate stenosis rate and decreased occlusion rate were observed in diabetic patients relative to non-diabetic patients (35.8% vs. 28.3%; and 6.6% vs. 11.4%; respectively). In diabetic patients, 362 (53.2%) plaques were detected in the distal lower leg segments, whereas in non-diabetic patients, 551 (64.5%) plaques were found in the proximal upper leg segments. The type IV plaque shape, in which the full lumen was involved, was detected more frequently in diabetic patients than in non-diabetic patients (13.1% vs. 8.2%). CONCLUSION: Diabetes is associated with a higher incidence of plaque, increased incidence of mixed plaques, moderate stenosis and localisation primarily in the distal lower leg segments. The advanced and non-invasive MDCT could be used for routine preoperative evaluations of LEA