Restricted Input Research Articles

Realizability of free spaces of curves

The free space diagram is a popular tool to compute the well-known Fréchet distance. As the Fréchet distance is used in many different fields, many variants have been established to cover the specific needs of these applications. Often the question arises whether a certain pattern in the free space diagram is “realizable”, i.e., whether there exists a pair of polygonal chains whose free space diagram corresponds to it. The answer to this question may help in deciding the computational complexity of these distance measures, as well as allowing to design more efficient algorithms for restricted input classes that avoid certain free space patterns. Therefore we study the inverse problem: Given a potential free space diagram, do there exist curves that generate this diagram?Our problem of interest is closely tied to the classic Distance Geometry problem. We settle the complexity of Distance Geometry in R>2, showing ∃R-hardness. We use this to show that for curves in R≥2 the realizability problem is ∃R-complete, both for continuous and discrete Fréchet distances. We prove that the continuous case in R1 is only weakly NP-hard, and we provide a pseudo-polynomial time algorithm and show that it is fixed-parameter tractable. Interestingly, for the discrete case in R1 we show that the problem becomes solvable in polynomial time.

The impact of export controls on international trade: Evidence from the Japan–Korea trade dispute in semiconductor industry

In July 2019, the Japanese government announced export controls to South Korea of three chemical inputs essential in semiconductor production. This paper investigates the short- to middle-run effect of the Japan–Korea export controls on the trade patterns of the restricted and other related products of the semiconductor industry. The results show that the export controls caused a large decline in Japanese exports to South Korea of one of the three restricted inputs, hydrogen fluoride, but not in the other two restricted inputs, photoresist and fluorinated polyimide. Second, South Korea reallocated the sourcing of the restricted chemical inputs from Japan to other economies such as Belgium, the U.S., and Taiwan. Third, there was negative spillover effect on the South Korean imports of semiconductor manufacturing equipments, which is used complementarily with the restricted inputs in the semiconductor production. These results suggest a potential role of export controls in sourcing patterns and production relocation in the semiconductor industry.

Interpretable ensemble machine learning models for predicting the shear capacity of UHPC joints

Precast ultra-high-performance concrete (UHPC) structures (PUSs) have gained increasing research and application interest in civil engineering owing to the combination of advanced construction materials and methods. UHPC joints are critical parts of PUSs; thus, an accurate prediction of their shear capacity (SC) is essential to ensure structural safety and reliability. However, existing equations for predicting SC have limited accuracy and applicability owing to their simplified assumptions and restricted input parameters. To address these challenges, this study used machine learning (ML) approaches to develop a unified and accurate predictive model for various types of UHPC joints. A well-curated database containing 218 UHPC joints with diverse types and configurations was established. Six ensemble algorithms and four traditional algorithms were employed to develop predictive models, and eight existing equations were compared for performance evaluation. Both correlation-based and SHAP-based feature selection methods were used to optimize the model accuracy. The ensemble algorithms demonstrated better performance than the traditional individual algorithms, with the gradient boosting machine (GBM) model ranked as the best ML model for SC. The ML model outperformed existing equations in all evaluated metrics, demonstrating its accuracy and robustness. Furthermore, Shapley Additive exPlanations (SHAP) analysis was employed to interpret the ML model, thereby providing insights into influential features and their relationships. These findings demonstrate the advantages of ML methods in predicting the SC of UHPC joints and provide valuable guidance for the structural design and research on PUSs.

Suppression of chaos in a partially driven recurrent neural network

The dynamics of recurrent neural networks (RNNs), and particularly their response to inputs, play a critical role in information processing. In many applications of RNNs, only a specific subset of the neurons generally receive inputs. However, it remains to be theoretically clarified how the restriction of the input to a specific subset of neurons affects the network dynamics. Considering RNNs with such restricted input, we investigate how the proportion, p, of the neurons receiving inputs (the “input neurons”) and the strength of the input signals affect the dynamics by analytically deriving the conditional maximum Lyapunov exponent. Our results show that for sufficiently large p, the maximum Lyapunov exponent decreases monotonically as a function of the input strength, indicating the suppression of chaos, but if p is smaller than a critical threshold, pc, even significantly amplified inputs cannot suppress spontaneous chaotic dynamics. Furthermore, although the value of pc is seemingly dependent on several model parameters, such as the sparseness and strength of recurrent connections, it is proved to be intrinsically determined solely by the strength of chaos in spontaneous activity of the RNN. This is to say, despite changes in these model parameters, it is possible to represent the value of pc as a common invariant function by appropriately scaling these parameters to yield the same strength of spontaneous chaos. Our study suggests that if p is above pc, we can bring the neural network to the edge of chaos, thereby maximizing its information processing capacity, by amplifying inputs. Published by the American Physical Society 2024

Multipolar Photoconductive Antennas for THz Emission Driven by a Dual-Frequency Laser Based on Transverse Modes

Continuous-wave tunable photonics-based THz sources present limited output power due to the restricted input optical power accepted by photomixers, along with reduced radiation resulting from low paraxial field amplitude. Here, we investigate multipolar antenna designs to increase the available continuous-wave THz output power by incorporating more photomixers. For this purpose, the spatial structures of the optical and THz E-fields are designed to enhance THz power and radiation in the far field. Simulations of 2 to 4 dipole antennas are conducted, demonstrating an improvement in antenna gain compared to standard dipole antennas. This is in addition to a potential increase in THz power and radiation for photomixing applications. Such work also paves the way for functionalizing the spatial structure of THz light for advanced applications.

Google Earth Engine for Advanced Land Cover Analysis from Landsat‐8 Data with Spectral and Topographic Insights

The primary goal of this research is to see how effective cloud‐based computing services such as Google Earth Engine (GEE) platform are at classifying multitemporal satellite images and producing high‐quality land cover maps for the target year of 2020, with the possibility of using it on a larger‐scale area such as metropolitan Melbourne as a test site. To create high‐quality land cover maps, the GEE is utilized to analyze a total of 80 Landsat‐8 images. The support vector machine (SVM) approach is used to classify the images. Moreover, we use spectral bands, spectral indices, and topographic parameters to improve classification and address the limitations of existing approaches for classification with restricted input variables. Furthermore, we apply a postprocessing strategy to increase the model’s performance by removing the salt‐and‐pepper noise created by misclassified pixels in supervised classification results. The results demonstrate that given all parameters, the SVM approach achieves an overall accuracy (OA) and kappa accuracy of 88.47% and 85.34%, respectively. Following the implementation of the postprocessing technique, the OA and kappa improve to 92.90% and 90.99%, respectively. The results indicate that Landsat‐8 multitemporal data, spectral indices, topographic components, and postprocessing techniques are all important in land cover mapping. Therefore, the use of freely accessible GEE technology and multitemporal Landsat‐8 data ensures that decision makers have the resources they need to track land cover throughout the year.

Obstacle avoidance flocking motion in multi-agent systems with limited sensing radius and heterogeneous input constraints

This paper addresses the leaderless and leader-followers flocking motion problems of multi-agent systems in a workspace with obstacles. We consider inertial agents with equally limited communication radius and heterogeneous input constraints. Using the notion of proximity graphs, we provide distributed controllers to induce the desired flocking behaviour. Unlike previous results, our controller designs do not require predicted states or a target determination scheme and allow edge deletions. Also, by taking advantage of the group's heterogeneity, our controllers allow agents with less restrictive input constraints to compensate for the control effort that their less capable neighbours cannot provide. We illustrate the effectiveness of our proposals through numerical simulations.

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

In recent years, fuel cell (FC) technology has seen a promising increase in its proportion in stationary power production. Several pilot projects are in operation across the world, with the number of running hours steadily rising, either as stand-alone units or as part of integrated gas turbine–electric energy plants. FCs are a potential energy source with great efficiency and zero emissions. To ensure the best performance, they normally function within a confined temperature and humidity range; nevertheless, this makes the system difficult to regulate, resulting in defects and hastened deterioration. For diagnosis, there are two primary approaches: restricted input information, which gives an unobtrusive, rapid yet restricted examination, and advanced characterization, which provides a more accurate diagnosis but frequently necessitates invasive or delayed tests. Artificial Intelligence (AI) algorithms have shown considerable promise in providing accurate diagnoses with quick data collecting. This work focuses on software models that allow the user to evaluate many different possibilities in the shortest amount of time and is a vital method for proper and dynamic analysis of such entities. The artificial neural network, genetic algorithm, particle swarm optimization, random forest, support vector machine, and extreme learning machine are common AI approaches discussed in this review. This article examines the modern practice and provides recommendations for future machine learning methodologies in fuel cell diagnostic applications. In this study, these six AI tools are specifically explained with results for a better understanding of the fuel cell diagnosis. The conclusion suggests that these approaches are not only a popular and beneficial tool for simulating the nature of an FC system, but they are also appropriate for optimizing the operational parameters necessary for an ideal FC device. Finally, observations and ideas for future research, enhancements, and investigations are offered.

Optimization Design of Planar Circle Coil for Limited-Size Wireless Power Transfer System

Power Transfer Efficiency (PTE) and Transferred Power (TP) are two crucial indicators of the wireless power transfer (WPT) system. However, in most compensatory topologies, the ideal coils design parameters of PTE and TP are inconsistent, implying that optimizing for one indicator would make another indicator less effective. As a result, in this article, the Thompson sampling efficient multi-objective optimization (TSEMO) algorithm is used to simultaneously optimize PTE and TP in the S-S compensation topology. Through the co-calculation of MATLAB and COMSOL, the coils can be optimized in the environment of magnetic material (ferrite) and conductive material (aluminum). Coils larger than the underwater vehicle’s size are deleted during the data interaction between MATLAB and COMSOL to guarantee that the optimal PTE and TP can be attained within the constrained area. Due to the open standard external software packages between MATLAB and COMSOL, the entire optimization process may be automated. The results show that for WPT systems with restricted input voltage, such as the battery, this design strategy can achieve better TP at the expense of lower PTE, which is highly beneficial in improving the system’s overall performance.

State and output feedback local control schemes for nonlinear discrete-time 2-D Roesser systems under saturation, quantization and slope restricted input

Control theory of one dimensional (1-D) systems is not directly applicable to two dimensional (2-D) systems due to involvement of complex dynamics in space and time. This paper deals with the control of nonlinear Roesser systems by employing feedback strategies with multiple constraints on the input signal. The input constraints are slope restriction, quantization, and generalized nonlinearities related to actuator overflow. Modeling deficiencies are also handled with the generalized actuator nonlinearities in a robust fashion. Novel results for state and output feedback topologies are furnished. Local stability criteria are developed for both feedback topologies by considering intrinsic nonlinearity as one-sided Lipschitz and generalized nonlinearity in a bounded sector. To the best of our knowledge, the outcomes of present paper are novel for nonlinear Roesser discrete-time systems while considering such nested restrictions on input signal. The results are verified by applying the proposed method on different 2-D practical systems.

Tectonic and Orbital Imprints in the Redox History of Japan Sea Since the Pliocene

AbstractOxygen content in the deep ocean plays a vital role in biogeochemical processes and has significant impacts on the global carbon cycle. The Japan Sea is a semiclosed basin with only shallow water connection to the Western North Pacific, and its redox history has been sensitively affected by tectonic and climatic changes in the past. Studies of paleo‐redox changes in the Japan Sea focused on the tectonic and orbital scales since the Pliocene remain scarce to date. Here, we present two high‐resolution paleo‐redox records during the last 4 Ma at IODP Sites U1425 and U1430 drilled in the Japan Sea. Our authigenic U (uranium) and U/Al records suggest remarkable changes of Japan Sea redox history from relatively oxic to periodic oxic‐anoxic conditions at ∼1.7 Ma. This was mainly caused by the restricted input of North Pacific oxygen‐rich water due to the uplift of northeastern Japan during late Pliocene and early Pleistocene, and opening of the Tsushima Strait and the periodic intrusion of Tsushima Warm Current following sea level change during glacial‐interglacial cycles since ∼1.7 Ma. Orbital changes of Japan Sea redox history suggest the existence of a long eccentricity cycle of 400 ka associated with East Asian summer monsoon rainfall evolution throughout the last 4 Ma. Changes of the amplitude of sea level and inflow of Tsushima Warm Current combined with the East Asian monsoon evolution produced eccentricity and obliquity cycles in Japan Sea redox environment after ∼1.7 Ma, as well as a transition from relatively oxic to anoxic conditions during glacials before and after Middle Pleistocene Transition.

Pathways of Development in Child Heritage Speakers' Use of Spanish Demonstratives

Scholars have argued that restricted language input slows language acquisition, but the pathway of development may remain the same. The current study investigated the influence of amount of Spanish input on Spanish demonstrative usage among 19 U.S. child heritage speakers, ages 3;4–8;7. Demonstratives are among the first grammatical features to emerge in children’s language, but we know little about their acquisition by heritage speakers. Previous research shows that monolingual Spanish-speaking children rely heavily on este/esta ‘this’ andonly later learn to vary between demonstrative forms, using este/esta primarily for proximal referents and ese/esa ‘that’ for distal referents. As such, we hypothesized that child heritage speakers who experience restricted Spanish input would rely on este/esta for a prolonged period of time. 586 demonstratives were elicited during a puzzle completion task and were coded for referent location (proximal, distal). Contrary to our hypothesis, less Spanish spoken at home negatively correlated with proportion of este/esta-usage. Children exposed to abundant Spanish in the home patterned like adult monolingual Spanish speakers, producing este/esta for proximal referents and ese/esa for distal referents. By contrast, children who experienced restricted input in Spanish produced mostly ese/esa, regardless of spatial location of the referent. The results suggest that restricted input in Spanish yields a developmental trajectory with overgeneralization of ese/esa rather than este/esta. Importantly, we argue thatthe identification of two groups of child heritage speakers who exhibit different developmental pathways lends itself to differentiated instruction in the heritage language classroom.

Deep Q learning-based traffic signal control algorithms: Model development and evaluation with field data

To contend traffic congestion on urban networks, existing studies have made great efforts to develop traffic-responsive signal timing algorithms in the last decade. More recently, as an alternative to conventional model-based algorithms, machine learning-based methods have been tested on traffic light timing problems and show promising potentials. However, many researchers and practitioners still questioned the feasibility and applicability of adopting machine learning techniques in the ATSC domain. One of the reasons is that these methods assumed flawless detectors and heavily relied on simulators for training and evaluations. To address such a critical concern, this article customizes a Deep Q-learning Learning (DQL) method to optimize traffic signal timings at urban intersections, where the partial observations from identity-based detectors are inputs, and the green splits are outputs. A simulation-free data-driven prediction model is also developed to train the DQL with reduced computational time. Then the machine learning-based methods are evaluated on a real-world case with Automatic Number-Plate Recognition (ANPR) data. Experiments show the proposed data-driven model can predict the traffic state in limited computational time, and the DQL algorithm is 3.9% better than the field experiment performance from the adaptive control system, SCOOT, and 22% better than the time-of-day plan by SYNCHRO. The results indicate the DQL methods can only yield marginal improvement with restrictive input and output settings in congested traffic flow in comparison to the conventional adaptive method.

State feedback H ∞ control for a class of affine nonlinear singular systems: Input restricting approach

Here, a new method for solving the H∞ control problem for a class of affine nonlinear singular systems is presented. The main idea is to restrict the control inputs and initial states to the sets of admissible inputs and consistent initial values, respectively. Consequently, the impulse-freeness of the system response is guaranteed. In this regard, a novel concept of “distance from the admissible inputs set” is introduced. Based on this concept, a set of sufficient conditions for the H∞ control problem's solvability and the corresponding H∞ controller is provided. The adopted approach can also be employed to solve the H∞ control problem for indefinite nonlinear singular systems as well as non-singular nonlinear systems with a restricted inputs set. Sufficient conditions for the solvability of and corresponding solution to these problems are also presented. Finally, the practicality of this method is illustrated using a numerical example.

On constructions of weightwise perfectly balanced Boolean functions

The recent FLIP cipher is an encryption scheme described by Meaux et al. at the conference EUROCRYPT 2016. It is based on a new stream cipher model called the filter permutator and tries to minimize some parameters (including the multiplicative depth). In the filter permutator, the input to the Boolean function has constant Hamming weight equal to the weight of the secret key. As a consequence, Boolean functions satisfying good cryptographic criteria when restricted to the set of vectors with constant Hamming weight play an important role in the FLIP stream cipher. Carlet et al. have shown that for Boolean functions with restricted input, balancedness and nonlinearity parameters continue to play an important role with respect to the corresponding attacks on the framework of FLIP ciphers. In particular, Boolean functions which are uniformly distributed over ${\mathbb {F}}_{2}$ on $E_{n,k}=\{x{\in \mathbb {F}_{2}^{n}}\mid \text {wt}(x)=k\}$ for every 0 < k < n are called weightwise perfectly balanced (WPB) functions, where wt(x) denotes the Hamming weight of x. In this paper, we firstly propose two methods of constructing weightwise perfectly balanced Boolean functions in 2k variables (where k is a positive integer) by modifying the support of linear and quadratic functions. Furthermore, we derive a construction of n-variable weightwise almost perfectly balanced Boolean functions for any positive integer n.

The lower bound of the weightwise nonlinearity profile of a class of weightwise perfectly balanced functions

Boolean functions satisfying good cryptographic criteria when restricted to the set of vectors with constant Hamming weight play an important role in the recent FLIP stream cipher. Although the nonlinearity of Boolean functions can be calculated rapidly by Walsh transform, the nonlinearity of the Boolean functions with restricted input seems to be very difficult to calculate. In this paper, a class of weightwise perfectly balanced Boolean functions with very simple algebraic normal form is proposed. Then, the cryptographic properties of the weightwise perfectly balanced Boolean functions are discussed. Especially, the lower bound of the weightwise nonlinearity of these 2m-variable functions is given for any positive integer m.

Enhanced two-loop model predictive control design for linear uncertain systems

Model predictive controllers (MPC) with the two-loop scheme are successful approaches practically and can be classified into two main categories, tube-based MPC and MPC-based reference governors (RG). In this paper, an enhanced two-loop MPC design is proposed for a pre-stabilized system with the bounded uncertainty subject to the input and state constraints. The proposed method offers less conservatism than the tube-based MPC methods by enlarging the restricted input constraint. Contrary to the MPC-based RGs, the investigated method improves tracking performance of the pre-stabilized system while satisfying the constraints. Additionally, the robust global asymptotic stability of the closed-loop system is guaranteed in a novel procedure with terminal constraint relaxation. Simulation of the proposed method on a servo system shows its effectiveness in comparison to the others.

Neural Network Study for 1+1d-Complex Scalar Field Theory

Modern Deep Learning techniques are employed in the context of two dimensional lattice complex scalar field theory, which has a non-trivial phase diagram at nonzero temperature and chemical potential. We demonstrated that deep neural networks can identify the phase transition in a semi-supervised manner, and can also discover hidden correlations beyond conventional analysis in decoding phase transition information with restricted input. We further showed that the network can efficiently learn the physical observables with limited training samples, which thus render an effective non-linear regression method in capturing the physical observables. Finally we explored generating new configurations with generative adversarial network (GAN), where we found the GAN can automatically capture the implicit local constraint for the physical configurations and also the underlying physical distribution.

Collective coupling between pairing and rotational degrees of freedom within a simple model

A simple model to study the collective coupling between pairing and rotational degrees of freedom in well-deformed even-even nuclei is proposed. It relies on the description of the effects of pairing correlations on the rotational motion in terms of intrinsic vortical currents. As a result, an expression of the rotational energy within a band is provided as a polynomial of order three in the square of the angular velocity. The coefficients of this polynomial have a well-defined analytical form and their values are determined from merely three experimental pieces of data: the energy of the first ${2}^{+}$ state, the ground-state charge quadrupole moment as deduced from $B(E2,{2}^{+}\ensuremath{\rightarrow}{0}^{+})$ measurements, and a quantity deduced from the odd-even mass differences in neighboring odd nuclei. This model is tested in 24 deformed nuclei chosen across the rare-earth and actinide regions. In spite of the very restricted input of data, and moreover which is limited to nuclear properties at zero or very low excitation energies, the agreement with the data within the yrast line is in many cases, especially in actinide nuclei, excellent up to angular momenta of the order of $30\ensuremath{\hbar}$ or more. Of course, such an approach is by construction unable to reproduce physical effects which do not result from this Coriolis antipairing (CAP) type of collective quenching of pairing correlations. This is especially the case in the rare-earth region, where a backbending effect is often observed. In such cases our model may be considered as a baseline in order to disentangle the effect of the CAP collective mode from those of other existing spectroscopic phenomena.

Yiddish–Lithuanian bilingualism: Incomplete acquisition, change through contacts, or both?

Aims and Objectives/Purposes/Research Questions: Studies on incomplete first language (L1) acquisition emphasize restricted input, the low prestige of heritage/immigrant/minority languages, and age of acquisition as significant factors contributing to changes in L1. However, it is not always clear whether it is possible to distinguish results of incomplete acquisition and contact-induced language change. This article deals with two Yiddish–Lithuanian bilinguals who acquired both languages at home (recorded in 2010 and 2011). The focus of the article is the absence of the Yiddish past tense auxiliary in both informants and the replacement of Yiddish discourse-pragmatic words by their Lithuanian or English equivalents in the speech of the second informant. Design/Methodology/Approach: Qualitative analysis of the speech of two Yiddish–Lithuanian bilinguals. Data and Analysis: Two sets of recordings analyzed for the past tense use and other features mentioned in Yiddish attrition studies. Findings/Conclusions: Restricted input is to be considered as a factor in any case. However, it is argued that phenomena reported in the heritage language literature are often the same as in the contact linguistic literature: impact on non-core morphosyntax, prosody, and word order are usually mentioned as primary candidates of contact-induced structural change. Based on purely linguistic phenomena, it is not possible to distinguish between the results of acquisition under the conditions of limited input and in other contact situations where limited input is not necessarily the case. Many features of the informants’ Yiddish are a result of Lithuanian impact. Originality: Yiddish–Lithuanian early bilingualism is extremely rare nowadays. The data and analysis contribute to a general understanding of the interplay between contact-induced language change and limited input. Significance/Implications: Unlike what is often presumed, it is not always possible to make comparisons to monolinguals or balanced bilinguals because monolingual speakers of Yiddish do not exist.