A 28 nm 368 fJ/cycle, 0.43%/V Supply Sensitivity, FLL based RC Oscillator Featuring Positive TC Only Resistors and ΣM Based Trimming

This Brief presents a process-scaling-friendly frequency-locked-loop (FLL)-based RC oscillator. It features an R-R-C frequency-to-voltage converter that entails resistors with only the same-sign temperature coefficients. Together with a low-leakage switched-capacitor resistor and a delta-sigma-modulator-based trimming, our 71.8-MHz RC oscillator in 28-nm CMOS achieves a frequency inaccuracy of 77.6 ppm/0C, a 0.43%/V supply sensitivity, and an 11-psrms period jitter. The energy efficiency is 368 fJ/cycle

Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction

A central problem in computational biophysics is protein structure prediction, i.e., finding the optimal folding of a given amino acid sequence. This problem has been studied in a classical abstract model, the HP model, where the protein is modeled as a sequence of H (hydrophobic) and P (polar) amino acids on a lattice. The objective is to find conformations maximizing H-H contacts. It is known that even in this reduced setting, the problem is intractable (NP-hard). In this work, we apply deep reinforcement learning (DRL) to the two-dimensional HP model. We can obtain the conformations of best known energies for benchmark HP sequences with lengths from 20 to 50. Our DRL is based on a deep Q-network (DQN). We find that a DQN based on long short-term memory (LSTM) architecture greatly enhances the RL learning ability and significantly improves the search process. DRL can sample the state space efficiently, without the need of manual heuristics. Experimentally we show that it can find multiple distinct best-known solutions per trial. This study demonstrates the effectiveness of deep reinforcement learning in the HP model for protein folding.Comment: Published at Physica A: Statistical Mechanics and its Applications, available online 7 December 2022. Extended abstract accepted by the Machine Learning and the Physical Sciences workshop, NeurIPS 202

Mapping spatial and temporal distribution information of plantations in Guangxi from 2000 to 2020

Plantations are formed entirely by artificial planting which are different from natural forests. The rapid expansion of plantation forestry has brought about a series of ecological and environmental problems. Timely and accurate information on the distribution of plantation resources and continuous monitoring of the dynamic changes in plantations are of great significance. However, plantations have similar spectral and texture characteristics with natural forests. In addition, cloud and rain greatly affected the image quality of large area mapping. Here, we tested the possibility of applying Continuous Change Detection and Classification to distinguish plantations from natural forests and described the spatiotemporal dynamic changes of plantations. We adopted the Continuous Change Detection and Classification algorithm and used all available Landsat images from 2000 to 2020 to map annual plantation forest distribution in Guangxi Zhuang Autonomous Region, China and analyzed their spatial and temporal dynamic changes. The overall accuracy of the plantation extraction is 88.77%. Plantations in Guangxi increased significantly in the past 20 years, from 2.37 × 106 ha to 5.11 × 106 ha. Guangxi is expanding new plantation land every year, with the largest expansion area in 2009 of about 2.58 × 105 ha. Over the past 20 years, plantations in Guangxi have clearly shown a tendency to expand from the southeast to the northwest, transformed from natural forests and farmland. 30% of plantations have experienced at least one logging-and-replanting rotation event. Logging rotation events more intensively occur in areas with dense plantation forests. Our study proves that using fitting coefficients from Continuous Change Detection and Classification algorithm is effective to extract plantations and mitigating the adverse effects of clouds and rain on optical images in a large scale, which provides a fast and effective method for long-time and large-area plantation identification and spatiotemporal distribution information extraction, and strong data support and decision reference for plantation investigation, monitoring and management

A simple, reliable and robust reinforcement method for the fabrication of (RE)–Ba–Cu–O bulk superconductors

Abstract: Bulk high temperature superconductors (HTS) based on the rare-earth barium cuprates [(RE)BCO] have the potential to be applied in a variety of engineering and technological applications such as trapped field magnets, rotating electrical machines, magnetic bearings and flywheel energy storage systems. The key materials figure of merit for most practical applications of bulk superconductors is simply the product of the maximum current density that can be supported, which correlates directly with the maximum achievable trapped magnetic field, and the physical length scale over which the current flows. Unfortunately, however, bulk (RE)BCO superconductors exhibit relatively poor mechanical properties due to their inherent ceramic nature. Consequently, the performance of these materials as trapped field magnets is limited significantly by their tensile strength, rather than critical current and size, given that the relatively large Lorentz forces produced in the generation of large magnetic fields can lead to catastrophic mechanical failure. In the present work, we describe a simple, but effective and reliable reinforcement methodology to enhance the mechanical properties of (RE)BCO bulk superconductors by incorporating hybrid SiC fibres consisting of a tungsten core with SiC cladding within the bulk microstructure. An improvement in tensile strength by up to 40% has been achieved via this process and, significantly, without compromising the superconducting performance of the bulk material

The expression and role of protein kinase C (PKC) epsilon in clear cell renal cell carcinoma

Protein kinase C epsilon (PKCε), an oncogene overexpressed in several human cancers, is involved in cell proliferation, migration, invasion, and survival. However, its roles in clear cell renal cell carcinoma (RCC) are unclear. This study aimed to investigate the functions of PKCε in RCC, especially in clear cell RCC, to determine the possibility of using it as a therapeutic target. By immunohistochemistry, we found that the expression of PKCε was up-regulated in RCCs and was associated with tumor Fuhrman grade and T stage in clear cell RCCs. Clone formation, wound healing, and Borden assays showed that down-regulating PKCε by RNA interference resulted in inhibition of the growth, migration, and invasion of clear cell RCC cell line 769P and, more importantly, sensitized cells to chemotherapeutic drugs as indicated by enhanced activity of caspase-3 in PKCε siRNA-transfected cells. These results indicate that the overexpression of PKCε is associated with an aggressive phenotype of clear cell RCC and may be a potential therapeutic target for this disease

Symbolic Discovery of Optimization Algorithms

We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient search techniques to explore an infinite and sparse program space. To bridge the large generalization gap between proxy and target tasks, we also introduce program selection and simplification strategies. Our method discovers a simple and effective optimization algorithm, $\textbf{Lion}$ (\textit{Evo\textbf{L}$ved S$\textbf{i}$gn M$\textbf{o}$me$\textbf{n}tum}). It is more memory-efficient than Adam as it only keeps track of the momentum. Different from adaptive optimizers, its update has the same magnitude for each parameter calculated through the sign operation. We compare Lion with widely used optimizers, such as Adam and Adafactor, for training a variety of models on different tasks. On image classification, Lion boosts the accuracy of ViT by up to 2% on ImageNet and saves up to 5x the pre-training compute on JFT. On vision-language contrastive learning, we achieve 88.3% $\textit{zero-shot}$ and 91.1% $\textit{fine-tuning}$ accuracy on ImageNet, surpassing the previous best results by 2% and 0.1%, respectively. On diffusion models, Lion outperforms Adam by achieving a better FID score and reducing the training compute by up to 2.3x. For autoregressive, masked language modeling, and fine-tuning, Lion exhibits a similar or better performance compared to Adam. Our analysis of Lion reveals that its performance gain grows with the training batch size. It also requires a smaller learning rate than Adam due to the larger norm of the update produced by the sign function. Additionally, we examine the limitations of Lion and identify scenarios where its improvements are small or not statistically significant. The implementation of Lion is publicly available.Comment: 30 pages, update the tuning instruction

System level diagnosis and wafer testing

This thesis consists of two relate but self-sustaining parts.In Part I a new diagnosability measure, t/ $-$ 1-diagnosability, is proposed for interconnected systems. This new diagnosability assures that all faulty units, except for at most one, can be correctly identified in one step, as long as the total number of faulty units does not exceed t. The class of t/ $-$ 1-diagnosable systems is fully characterized. A polynomial algorithm is presented for determining the degree of t/ $-$ 1-diagnosability for any given system. A polynomial diagnosis algorithm is also given for any t/ $-$ 1-diagnosable systems. It is shown that the degree of t/ $-$ 1-diagnosability could be twice as large as the degree of t-diagnosability for a given system.In Part II a probabilistic diagnosis algorithm is presented for constant degree structures such as grids. It is shown that almost all faulty units can be correctly identified under a binomial failure distribution even when the probability of failure is rather high. The performance is very insensitive to yield variations under a negative binomial failure distribution. The application of this algorithm to the production testing of chips and wafers is explored. A simple test structure is provided for wafer testing, which utilizes the test access port of each die to facilitate comparison testing. The diagnosis algorithm is localized and incorporated into the test structure to determine the status of each die. The scheme is unique in that it is shown to work well when faults are clustered and even when the yield is low