Effects of Long-Range Interactions on Magnetic Excitations and Phase Transition on a Magnetically Frustrated Square Lattice

We investigate the effects of long-range interaction on the magnetic excitations and the competition between magnetic phases on a frustrated square lattice. Applying the spin wave theory and assisted with symmetry analysis, we obtain analytical expression for spin wave spectrum of competing Neel and (pi, 0) stripe states of systems containing any-order long-range interactions. In the specific case of long-range interactions with power-law decay, we found surprisingly that staggered long-range interaction suppresses quantum fluctuation and enlarges the ordered moment, especially in the Neel state, and thus extends its phase boundary to the stripe state. Our findings only illustrate the rich possibilities of the roles of long-range interactions, and advocate future investigations in other magnetic systems with different structures of interactions.Comment: 9 pages, 9 figure

Forward Intensity Model Monitoring Using Multivariate Exponential Weighted Moving Average Scheme

We propose a parameter monitoring method for the forward intensity model – the default probability prediction model of the Credit Research Initiative (CRI). We review the relative statistical process control scheme in the field of engineering. Based on this, we propose a new Multivariate Exponential Weighted Moving Average (MEWMA) scheme to monitor the forward intensity model monthly. This new chart might be applied to identify and diagnose the out-of-control (OC) parameters in real time as the data updating, which reduces the cost of recalculating all parameters and improve the operational and calculational efficiency of the default prediction models in practical application

Targeting and exploitation of tumor-associated neutrophils to enhance immunotherapy and drug delivery for cancer treatment

Neutrophils, the most abundant leukocytes in human blood, are essential fighter immune cells against microbial infection. Based on the finding that neutrophils can either restrict or promote cancer progression, tumor-associated neutrophils (TAN) are classified into anti-tumor N1 and pro-tumor N2 subsets. One of the major mechanisms underlying the tumor-promoting function of N2-TANs is suppression of adaptive immune cells, in particular, cytotoxic T lymphocytes. Currently, no established methodologies are available that can unequivocally distinguish immunosuppressive TANs and granulocytic/polymorphonuclear myeloid-derived suppressor cells (G/PMN-MDSC). In view of the critical role of PMN-MDSCs in immune evasion and resistance to cancer immunotherapy, as established from data obtained with diverse cancer models, therapeutic strategies targeting these cells have been actively developed to enhance the efficacy of immunotherapy. Here, we have reviewed the available literature on strategies targeting PMN-MDSCs and summarized the findings into four categories: (1) depletion of existing PMN-MDSCs, (2) blockade of the development of PMN-MDSCs, (3) blockade of PMN-MDSC recruitment, (4) inhibition of immunosuppressive function. Owing to their high mobility to inflamed organs and ability to trespass the blood-brain barrier, neutrophils are outstanding candidate carriers in nanoparticle-based therapies. Another attractive application of neutrophils in cancer therapy is the use of neutrophil membrane-derived nanovesicles as a surrogate of extracellular vesicles for more efficient and scalable drug delivery. In the second part of the review, we have highlighted recent advances in the field of neutrophil-based cancer drug delivery. Overall, we believe that neutrophil-based therapeutics are a rapidly growing area of cancer therapy with significant potential benefits

A 7.3-μ W 13-ENOB 98-dB SFDR Noise-Shaping SAR ADC With Duty-Cycled Amplifier and Mismatch Error Shaping

This article presents a second-order noise-shaping successive-approximation-register (SAR) analog-to-digital converter (ADC) that employs a duty-cycled amplifier and digital-predicted mismatch error shaping (MES). The loop filter is composed of an active amplifier and two cascaded passive integrators to provide a theoretical 30-dB in-band noise attenuation. The amplifier achieves 18\times gain in a power-efficient way thanks to its inverter-based topology and duty-cycled operation. The capacitor mismatch in the digital-to-analog converter (DAC) array is mitigated by first-order MES. A two-level digital prediction scheme is adopted with MES to avoid input range loss. Fabricated in 65-nm CMOS technology, the prototype achieves 80-dB peak signal-to-noise-and-distortion-ratio (SNDR) and 98-dB peak spurious-free-dynamic-range (SFDR) in a 31.25-kHz bandwidth with 16\times oversampling ratio (OSR), leading to a Schreier figure-of-merit (FoM) of 176.3 dB and a Walden FoM of 14.3 fJ/conversion-step.</p

Highly-Accurate Electricity Load Estimation via Knowledge Aggregation

Mid-term and long-term electric energy demand prediction is essential for the planning and operations of the smart grid system. Mainly in countries where the power system operates in a deregulated environment. Traditional forecasting models fail to incorporate external knowledge while modern data-driven ignore the interpretation of the model, and the load series can be influenced by many complex factors making it difficult to cope with the highly unstable and nonlinear power load series. To address the forecasting problem, we propose a more accurate district level load prediction model Based on domain knowledge and the idea of decomposition and ensemble. Its main idea is three-fold: a) According to the non-stationary characteristics of load time series with obvious cyclicality and periodicity, decompose into series with actual economic meaning and then carry out load analysis and forecast. 2) Kernel Principal Component Analysis(KPCA) is applied to extract the principal components of the weather and calendar rule feature sets to realize data dimensionality reduction. 3) Give full play to the advantages of various models based on the domain knowledge and propose a hybrid model(XASXG) based on Autoregressive Integrated Moving Average model(ARIMA), support vector regression(SVR) and Extreme gradient boosting model(XGBoost). With such designs, it accurately forecasts the electricity demand in spite of their highly unstable characteristic. We compared our method with nine benchmark methods, including classical statistical models as well as state-of-the-art models based on machine learning, on the real time series of monthly electricity demand in four Chinese cities. The empirical study shows that the proposed hybrid model is superior to all competitors in terms of accuracy and prediction bias

Advancing Generalizable Remote Physiological Measurement through the Integration of Explicit and Implicit Prior Knowledge

Remote photoplethysmography (rPPG) is a promising technology that captures physiological signals from face videos, with potential applications in medical health, emotional computing, and biosecurity recognition. The demand for rPPG tasks has expanded from demonstrating good performance on intra-dataset testing to cross-dataset testing (i.e., domain generalization). However, most existing methods have overlooked the prior knowledge of rPPG, resulting in poor generalization ability. In this paper, we propose a novel framework that simultaneously utilizes explicit and implicit prior knowledge in the rPPG task. Specifically, we systematically analyze the causes of noise sources (e.g., different camera, lighting, skin types, and movement) across different domains and incorporate these prior knowledge into the network. Additionally, we leverage a two-branch network to disentangle the physiological feature distribution from noises through implicit label correlation. Our extensive experiments demonstrate that the proposed method not only outperforms state-of-the-art methods on RGB cross-dataset evaluation but also generalizes well from RGB datasets to NIR datasets. The code is available at https://github.com/keke-nice/Greip

van der Waals Bonded Co/h-BN Contacts to Ultrathin Black Phosphorus Devices

Due to the chemical inertness of 2D hexagonal-Boron Nitride (h-BN), few atomic-layer h-BN is often used to encapsulate air-sensitive 2D crystals such as Black Phosphorus (BP). However, the effects of h-BN on Schottky barrier height, doping and contact resistance are not well known. Here, we investigate these effects by fabricating h-BN encapsulated BP transistors with cobalt (Co) contacts. In sharp contrast to directly Co contacted p-type BP devices, we observe strong n-type conduction upon insertion of the h-BN at the Co/BP interface. First principles calculations show that this difference arises from the much larger interface dipole at the Co/h-BN interface compared to the Co/BP interface, which reduces the work function of the Co/h-BN contact. The Co/h-BN contacts exhibit low contact resistances (~ 4.5 k-ohm), and are Schottky barrier free. This allows us to probe high electron mobilities (4,200 cm2/Vs) and observe insulator-metal transitions even under two-terminal measurement geometry

rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement

Remote photoplethysmography (rPPG) is an important technique for perceiving human vital signs, which has received extensive attention. For a long time, researchers have focused on supervised methods that rely on large amounts of labeled data. These methods are limited by the requirement for large amounts of data and the difficulty of acquiring ground truth physiological signals. To address these issues, several self-supervised methods based on contrastive learning have been proposed. However, they focus on the contrastive learning between samples, which neglect the inherent self-similar prior in physiological signals and seem to have a limited ability to cope with noisy. In this paper, a linear self-supervised reconstruction task was designed for extracting the inherent self-similar prior in physiological signals. Besides, a specific noise-insensitive strategy was explored for reducing the interference of motion and illumination. The proposed framework in this paper, namely rPPG-MAE, demonstrates excellent performance even on the challenging VIPL-HR dataset. We also evaluate the proposed method on two public datasets, namely PURE and UBFC-rPPG. The results show that our method not only outperforms existing self-supervised methods but also exceeds the state-of-the-art (SOTA) supervised methods. One important observation is that the quality of the dataset seems more important than the size in self-supervised pre-training of rPPG. The source code is released at https://github.com/linuxsino/rPPG-MAE

Ultra Low Power Event-Driven Sensor Interfaces

This paper reviews several examples of ultra low power sensor interfaces for IoT applications. In such applications, the sensing operation is often performed at a relatively low frequency, and sometimes it is heavily duty-cycled, or it should be triggered by particular events or thresholds. The paper reviews why dynamic sensor interface architectures are a good choice in this context, and gives several design examples that can operate dynamically and that can be triggered by a single clock pulse. Suitable ADC design strategies are explained, and two exemplary sensor interfaces are described: a capacitive sensor interface, and a resistor-based temperature sensor interface including analog correction techniques. Both designs are reviewed and the main features in terms of efficiency and performance are discussed.</p

Proposing a New Research Framework for Loan Allocation Strategies in P2P Lending

One of the frontier Web 2.0 applications is online peer-to-peer (P2P) lending marketplace, where individual lenders and borrowers can virtually meet for loan transactions. From a lender’s perspective, she not only wants to lower investment risk but also to gain as much return as possible. However, P2P lenders possess the inherent problem of information asymmetry that they don’t really know if a borrower has capability to pay the loan or is truthfully willing to pay it in due time, leading them to a disadvantaged situation when making the decision of lending money to the borrower. This study intends to consider the loan allocation as an optimization research problem using the research framework based upon modern portfolio theory with the aim of helping lenders achieve the two goals of gaining high return and lowering risk at the same time. The expected results of this research are twofold: 1) compared to a logistic regression based credit scoring method, we expect to make more profits for lenders with risk level unchanged, and 2) compared to a linear regression based profit scoring method, we expect to lower risk without lowering return. Our proposed new model could offer insights into how individual lenders can optimize their loan allocation strategies when considering return and risk simultaneously