Powerful Class Research Articles

Machine Learning for Sparse Nonlinear Modeling and Control

Machine learning is rapidly advancing nearly every field of science and engineering, and control theory is no exception. In particular, it has shown incredible promise for handling several of the main challenges facing modern dynamics and control, including complexity, unmodeled dynamics, strong nonlinearity, and hidden variables. However, machine learning models are often expensive to train and deploy, fail to generalize beyond the training data, and suffer from a lack of explainability, interpretability, and guarantees, all of which limit their use in real-world and safety-critical control applications. Sparse nonlinear modeling and control techniques are a powerful class of machine learning that promote parsimony through sparse optimization, providing data-efficient models that are more interpretable and generalizable and have proven effective for control. In this review, we explore the use of sparse optimization in the context of machine learning to develop compact models and controllers that are easy to train, require significantly less data, and make low-latency predictions. In particular, we focus on applications in model predictive control and reinforcement learning, two of the foundational algorithms in control theory.

Vector AutoRegressive Moving Average Models: A Review.

Vector AutoRegressive Moving Average (VARMA) models form a powerful and general model class for analyzing dynamics among multiple time series. While VARMA models encompass the Vector AutoRegressive (VAR) models, their popularity in empirical applications is dominated by the latter. Can this phenomenon be explained fully by the simplicity of VAR models? Perhaps many users of VAR models have not fully appreciated what VARMA models can provide. The goal of this review is to provide a comprehensive resource for researchers and practitioners seeking insights into the advantages and capabilities of VARMA models. We start by reviewing the identification challenges inherent to VARMA models thereby encompassing classical and modern identification schemes and we continue along the same lines regarding estimation, specification, and diagnosis of VARMA models. We then highlight the practical utility of VARMA models in terms of Granger Causality analysis, forecasting and structural analysis as well as recent advances and extensions of VARMA models to further facilitate their adoption in practice. Finally, we discuss some interesting future research directions where VARMA models can fulfill their potentials in applications as compared to their subclass of VAR models.

The Impact of Consumption Downgrading on Housing Purchase Decision of Chinese Middle Class: An Analysis Based on the Life Cycle Theory and Marriage Market Theory

In the face of the trend of consumption downgrading, the purchasing decisions of the middle class regarding real estate have undergone new changes. Existing literature on the housing market and purchasing behavior of the middle class under the trend of consumption downgrading suggests that the purchasing power of the middle class has generally decreased, with many unable to bear the costs of mortgage loans and other housing-related financial burdens. As a result, the number of people purchasing real estate in the future may decrease. Based on existing research and practical needs, this paper summarizes the trends of previous studies through a literature review, pointing out their shortcomings. The paper further explores the decision-making process of the middle class in the context of consumption downgrading and analyzes the reasons for this situation using the Life Cycle Theory and Marriage Market Theory. The goal of this research is to provide policy recommendations for policymakers and reasonable suggestions for middle-class housing consumption decisions. The analysis shows that the middle class tends to rent housing or purchase small-sized properties, and that owning property is an important factor in the marriage market, especially from adulthood to the marriage stage.

Fast Quantum Control of Cavities Using an Improved Protocol without Coherent Errors

The selective number-dependent arbitrary phase gates form a powerful class of quantum gates, imparting arbitrarily chosen phases to the Fock states of a cavity. However, for short pulses, coherent errors limit the performance. Here, we demonstrate in theory and experiment that such errors can be completely suppressed, provided that the pulse times exceed a specific limit. The resulting shorter gate times also reduce incoherent errors. Our approach needs only a small number of frequency components, the resulting pulses can be interpreted easily, and it is compatible with fault-tolerant schemes. Published by the American Physical Society 2024

The Landmark Series: Cancer Vaccines for Solid Tumors.

Immunotherapy has become an integral part of the treatment for solid tumors. Cancer vaccines represent a potentially powerful class of immunotherapeutic agents to drive antitumor immunity. Cancer vaccine development involves selecting immunogenic target antigens expressed by tumor cells that can be effectively delivered for uptake by antigen-presenting cells to generate a robust adaptive immune response against tumor. While numerous cancer vaccines have been shown to produce antigen-specific immune responses, translating promising results of immunogenicity from early-phase trials into durable clinical benefit in larger randomized trials has remained elusive. Recent findings support new enthusiasm for several cancer vaccine approaches for solid tumors. This review will discuss landmark historic clinical trials in cancer vaccine development and strategies to optimize cancer vaccines to achieve improved clinical efficacy.

The Emerging of 2D Layer‐Blocked Frameworks

Blocked copolymers are a versatile and powerful class of materials, and their properties can be modified by carefully designing the molecular architecture. Their ability to self‐assemble into nanoscale structures and the multifunctionality make them invaluable in advanced materials science, nanotechnology, and biomedical engineering. Compared to traditional blocked copolymer morphologies such as linear, cyclic, and branched, 2D layer‐blocked frameworks provide materials with clear structure, permanent porosity, favorable chemical and thermal stability, larger surface area, ultra‐high carrier mobility, efficient charge separation and migration, and excellent photoexcitation response. In this concept article, we summarized the design concepts and state‐of‐the‐art strategies for the construction of 2D layer‐blocked frameworks and reviewed the development of some 2D‐like blocked frameworks and their applications in semiconductor, catalysis, energy storage, etc. Finally, we also outlook for the challenges of these materials in preparation and applications across different research fields.

Monitoring Animal Populations With Cameras Using Open, Multistate, N-Mixture Models.

Remote cameras have become a mainstream tool for studying wildlife populations. For species whose developmental stages or states are identifiable in photographs, there are opportunities for tracking population changes and estimating demographic rates. Recent developments in hierarchical models allow for the estimation of ecological states and rates over time for unmarked animals whose states are known. However, this powerful class of models has been underutilized because they are computationally intensive, and model outputs can be difficult to interpret. Here, we use simulation to show how camera data can be analyzed with multistate, Dail-Madsen (hereafter multistate DM) models to estimate abundance, survival, and recruitment. We evaluated four commonly encountered scenarios arising from camera trap data (low and high abundance and 25% and 50% missing data) each with 18 different sample size combinations (camera sites = 40, 250; surveys = 4, 8, and 12; and years = 2, 5, 10) and evaluated the bias and precision of abundance, survival, and recruitment estimates. We also analyzed our empirical camera data on moose (Alces alces) with multistate DM models and compared inference with telemetry studies from the same time and region to assess the accuracy of camera studies to track moose populations. Most scenarios recovered the known parameters from our simulated data with higher accuracy and increased precision for scenarios with more sites, surveys, and/or years. Large amounts of missing data and fewer camera sites, especially at higher abundances, reduced accuracy, and precision of survival and recruitment. Our empirical analysis provided biologically realistic estimates of moose survival and recruitment and recovered the pattern of moose abundance across the region. Multistate DM models can be used for estimating demographic parameters from camera data when developmental states are clearly identifiable. We discuss several avenues for future research and caveats for using multistate DM models for large-scale population monitoring.

Prospects the Democracy in Ukraine - Based on Changes in Economic Conditions Since the Russian Invasion

The article aims to clarify the deep relevance and dependence of economic basis and democracy level, which includes how does the former play a role in the formation, the maintenance and the future development of the latter. Economic prosperity, the leader of production activities, power of the middle class, diversification of Economic sector and trade liberalization is the object focus on in this study. It will focus on noteworthy oneUkraine. The aggression from Russia has caused huge macroeconomic changes in Ukraine. Therefore, this article examines the current economic influence of such drastic external factors as a military invasion. This paper will combine the theory of how the economic situation affects the level of democracy with specific economic data and practical research in Ukraine. Then look at the positive and negative aspects, some changes can improve the level of democracy, while others will harm the level of democracy in Ukraine.

Normalizing Basis Functions: Approximate Stationary Models for Large Spatial Data

ABSTRACTIn geostatistics, traditional spatial models often rely on the Gaussian process (GP) to fit stationary covariances to data. It is well known that this approach becomes computationally infeasible when dealing with large data volumes, necessitating the use of approximate methods. A powerful class of methods approximate the GP as a sum of basis functions with random coefficients. Although this technique offers computational efficiency, it does not inherently guarantee a stationary covariance. To mitigate this issue, the basis functions can be ‘normalized’ to maintain a constant marginal variance, avoiding unwanted artefacts and edge effects. This allows for the fitting of nearly stationary models to large, potentially non‐stationary datasets, providing a rigorous base to extend to more complex problems. Unfortunately, the process of normalizing these basis functions is computationally demanding. To address this, we introduce two fast and accurate algorithms to the normalization step, allowing for efficient prediction on fine grids. The practical value of these algorithms is showcased in the context of a spatial analysis on a large dataset, where significant computational speedups are achieved. While implementation and testing are done specifically within the LatticeKrig framework, these algorithms can be adapted to other basis function methods operating on regular grids.

American Capitalism and the Decline of Debtors’ Prison in the 19th Century

This paper examines the dynamics of capitalism and the penitentiary system through an analysis of the decline of debt imprisonment in 19th-century United States. It argues that class transformations in the early to mid-1800s created a context for social experiments with new forms of punishment. The penitentiary systems that formed developed logics that contingently fit with the rising mode of production. The decline of debt imprisonment, in this context, was much in part the result of transformations in class power and class structure.

Fluid flow and heat transfer of a novel passive cooling system for gearless wind turbines with a power range of 3–12 MW

Today, the gearless horizontal axis wind turbines are mainstream in wind energy industry. High demands of electric power led to bigger systems and active cooling reduces the overall efficiency of the turbines. Passive cooling systems have been examined for the first time for a gearless wind energy generator with power range of 3–12 MW. With further developed heat conductors, it is possible to operate a wind generator in a larger power class with passive cooling components. This is accompanied by enormous cost savings due to elimination of costs for active cooling elements such as the use of fans, pumps, and heat exchangers. An additional factor is the significant reduction in necessary maintenance due to the minimized incidence of corrosion and wear. Design of the cooling fins and an ideal position of the generator within the housing of the wind turbine has been objectives. Mandatory is that maximum temperatures of the generator fins should stay under 155 °C, which could be reached with several designs for different heat exchanger geometries.

Class inequality, power, and trust in private companies: evidence from Latin America

Class inequality, power, and trust in private companies: evidence from Latin America

A Design Review for Biomedical Wireless Power Transfer Systems with a Three-Coil Inductive Link through a Case Study for NICU Applications

This paper outlines a design approach for biomedical wireless power transfer systems with a focus on three-coil inductive links for neonatal intensive care unit applications. The relevant literature has been explored to support the design approach, equations, simulation results, and the process of experimental analysis. The paper begins with a brief overview of various power amplifier classes, followed by an in-depth examination of the most common power amplifiers used in biomedical wireless power transfer systems. Among the traditional linear and switching amplifier classes, class-D and class-E switching amplifiers are highlighted for their enhanced efficiency and straightforward implementation in biomedical contexts. The impact of load variation on these systems is also discussed. This paper then explores the basic concepts and essential equations governing inductive links, comparing two-coil and multi-coil configurations. In the following, the paper discusses foundational coil parameters and provides theoretical and experimental analysis of both two-coil and multi-coil inductive links through step-by-step measurement techniques using lab equipment and addressing the relevant challenges. Finally, a case study for neonatal intensive care unit applications is presented, showcasing a wireless power transfer system operating at 13.56 MHz for powering a wearable device on a patient lying on a mattress. An inductive link with a transmitter coil embedded in a mattress is designed to supply power to a load at distances ranging from 4 cm to 12 cm, simulating the mattress-to-chest distance of an infant. the experimental results of a three-coil inductive link equipped with a Class-E power amplifier are reported, demonstrating power transfer efficiency ranging from 75% to 25% and power delivery to a 500 Ω-load varying from 340 mW to 25 mW over various distances.

Determinant- and derivative-free quantum Monte Carlo within the stochastic representation of wavefunctions

Abstract Describing the ground states of continuous, real-space quantum many-body systems, like atoms and molecules, is a significant computational challenge with applications throughout the physical sciences. Recent progress was made by variational methods based on machine learning (ML) ansatzes. However, since these approaches are based on energy minimization, ansatzes must be twice differentiable. This (a) precludes the use of many powerful classes of ML models; and (b) makes the enforcement of bosonic, fermionic, and other symmetries costly. Furthermore, (c) the optimization procedure is often unstable unless it is done by imaginary time propagation, which is often impractically expensive in modern ML models with many parameters. The stochastic representation of wavefunctions (SRW), introduced in (Atanasova et al 2023 Nat. Commun. 14 3601), is a recent approach to overcoming (c). SRW enables imaginary time propagation at scale, and makes some headway towards the solution of problem (b), but remains limited by problem (a). Here, we argue that combining SRW with path integral techniques leads to a new formulation that overcomes all three problems simultaneously. As a demonstration, we apply the approach to generalized ‘Hooke’s atoms’: interacting particles in harmonic wells. We benchmark our results against state-of-the-art data where possible, and use it to investigate the crossover between the Fermi liquid and the Wigner molecule within closed-shell systems. Our results shed new light on the competition between interaction-driven symmetry breaking and kinetic-energy-driven delocalization.

Texture Analysis and Classification using Local Binary Patterns and Statistical Features

Texture analysis and classification are crucial in many applications such as medical applications, satellite imagery analysis, and so on. This paper presents a new technique that incorporates the LBP with the HOG to improve the texture analysis and classification. LBP is famous for finding out the local texture descriptors by comparing the pixel intensities it deals with and can challenge light variations and image transformations. On the other hand, there is HOG which emphasizes edge and gradient information to detect the orientation and shape of a texture in a given image. While LBP describes the local micro-patterns HOG gives the perspective of the global gradient orientation. The combined use of local and global texture information is helpful in the presented problem when prioritizing the recognition of complex texture patterns that cannot be deciphered by a single algorithm. To demonstrate the efficiency of the proposed hybrid method, several experiments are performed on several texture databases. According to the findings, the proposed LBP-HOG strategy proves more beneficial than basic LBP and HOG techniques concerning the classification rate and computing time. The combination method shows improved performance in terms of agreement with the reference of fine and coarse level of texture descriptions along with providing large-scale description of the texture which results in the improved discriminatory power of the texture classes.

Bioanalysis of antisense oligonucleotides: Techniques, challenges, regulatory considerations, and future perspectives

Antisense oligonucleotides (ASOs) are a powerful class of therapeutics designed to target RNA sequences and regulate gene expression, offering promising treatment avenues for various genetic disorders, neurodegenerative diseases, cancers, and viral infections. The bioanalysis of ASOs presents unique challenges due to their distinct chemical and biological characteristics, requiring specialized analytical methods and regulatory oversight. This review explores the latest methodologies in ASO bioanalysis, assesses the current regulatory environment, and considers future directions, particularly in the context of critical and emerging technologies (CETs) relevant to national security strategies. ASOs, classified under biotechnology, play a crucial role in gene expression modulation and protein regulation. By integrating insights from CETs, this review emphasizes the strategic significance of ASO bioanalysis in driving advancements in medical science and contributing to national security.

Toward the confident deployment of real‐world reinforcement learning agents

AbstractIntelligent learning agents must be able to learn from experience so as to accomplish tasks that require more ability than could be initially programmed. Reinforcement learning (RL) has emerged as a potentially powerful class of solution methods to create agents that learn from trial‐and‐error interaction with the world. Despite many prominent success stories, a number of challenges often stand between the use of RL in real‐world problems. As part of the AAAI New Faculty Highlight Program, in this article, I will describe the work that my group is doing at the University of Wisconsin—Madison with the intent to remove barriers to the use of RL in practice. Specifically, I will describe recent work that aims to give practitioners confidence in learned behaviors, methods to increase the data efficiency of RL, and work on “challenge” domains that stress RL algorithms beyond current testbeds.

Comparative Study and Analysis of Social Structure and Cultural Values in Chinese and Korean Movies

A comparative study of social structure and cultural values in Chinese and Korean films reveals the unique perspectives and profound differences between the two countries in reflecting social reality and conveying cultural values in their films. Chinese movies tend to depict harmonious and intimate family relationships when showcasing family structure, emphasizing the importance of the family as an individual's emotional support and spiritual sustenance. Korean movies, on the other hand, focus more on showcasing the complexity and conflicts within families, reflecting social reality and the depth of human nature through the entanglements among family members. This difference reflects the different emphasis on family values and social values between China and South Korea. In terms of social class, Chinese movies often focus on the survival status and struggle process of the lower class society, showcasing social injustice and contradictions by telling stories of ordinary people. This kind of attention reflects the humanistic care of Chinese films towards vulnerable groups and their profound reflection on social issues. Korean movies, on the other hand, tend to reveal and criticize the power and corruption of the upper class, reflecting the dark side of society by depicting social phenomena such as collusion between politics and business, and power and money transactions. This difference reflects the different orientations of social class concepts and social responsibility between China and South Korea.

Hierarchical Partitioning Forecaster

In this work we consider a new family of algorithms for sequential prediction, Hierarchical Partitioning Forecasters (HPFs). Our goal is to provide appealing theoretical - regret guarantees on a powerful model class - and practical - empirical performance comparable to deep networks - properties at the same time. We built upon three principles: hierarchically partitioning the feature space into sub-spaces, blending forecasters specialized to each sub-space and learning HPFs via local online learning applied to these individual forecasters. Following these principles allows us to obtain regret guarantees, where Constant Partitioning Forecasters (CPFs) serve as competitor. A CPF partitions the feature space into sub-spaces and predicts with a fixed forecaster per sub-space. Fixing a hierarchical partition H and considering any CPF with a partition that can be constructed using elements of H we provide two guarantees: first, a generic one that unveils how local online learning determines regret of learning the entire HPF online; second, a concrete instance that considers HPF with linear forecasters (LHPF) and exp-concave losses where we obtain O(klogT) regret for sequences of length T where k is a measure of complexity for the competing CPF. Finally, we provide experiments that compare LHPF to various baselines, including state of the art deep learning models, in precipitation nowcasting. Our results indicate that LHPF is competitive in various settings.

HIRI-ViT: Scaling Vision Transformer With High Resolution Inputs.

The hybrid deep models of Vision Transformer (ViT) and Convolution Neural Network (CNN) have emerged as a powerful class of backbones for vision tasks. Scaling up the input resolution of such hybrid backbones naturally strengthes model capacity, but inevitably suffers from heavy computational cost that scales quadratically. Instead, we present a new hybrid backbone with HIgh-Resolution Inputs (namely HIRI-ViT), that upgrades prevalent four-stage ViT to five-stage ViT tailored for high-resolution inputs. HIRI-ViT is built upon the seminal idea of decomposing the typical CNN operations into two parallel CNN branches in a cost-efficient manner. One high-resolution branch directly takes primary high-resolution features as inputs, but uses less convolution operations. The other low-resolution branch first performs down-sampling and then utilizes more convolution operations over such low-resolution features. Experiments on both recognition task (ImageNet-1K dataset) and dense prediction tasks (COCO and ADE20 K datasets) demonstrate the superiority of HIRI-ViT. More remarkably, under comparable computational cost ( ∼5.0 GFLOPs), HIRI-ViT achieves to-date the best published Top-1 accuracy of 84.3% on ImageNet with 448×448 inputs, which absolutely improves 83.4% of iFormer-S by 0.9% with 224×224 inputs.