Types Of Constraints Research Articles

Nutritionally adequate and environmentally respectful diets are possible for different diet groups: an optimized study from the NutriNet-Santé cohort

Research has shown that vegetarian diets have a low environmental impact, but few studies have examined the environmental impacts and nutritional adequacy of these diets together, even though vegetarian diets can lead to nutritional issues. Our objective was to optimize and compare 6 types of diets with varying degrees of plant foods (lacto-, ovolacto-, and pescovegetarian diets and diets with low, medium, and high meat content) under nutritional constraints. Consumption data in 30,000 participants were derived from the French NutriNet-Santé cohort using an FFQ. Diets were optimized by a nonlinear algorithm minimizing the diet deviation while meeting multiple constraints at both the individual and population levels: nonincrease of the cost and environmental impacts (as partial ReCiPe accounting for greenhouse gas emissions, cumulative energy demand, and land occupation, distinguishing production methods: organic and conventional), under epidemiologic, nutritional (based on nutrient reference values), and acceptability (according to the diet type) constraints. Optimized diets were successfully identified for each diet type, except that it was impossible to meet the EPA (20:5n-3) + DHA (22:6n-3) requirements in lacto- and ovolactovegetarians. In all cases, meat consumption was redistributed or reduced and the consumption of legumes (including soy-based products), whole grains, and vegetables were increased, whereas some food groups, such as potatoes, fruit juices, and alcoholic beverages, were entirely removed from the diets. The lower environmental impacts (as well as individual indicators) observed for vegetarians could be attained even when nutritional references were reached except for long-chain n-3 (omega-3) fatty acids. A low-meat diet could be considered as a target for the general population in the context of sustainable transitions, although all diets tested can be overall nutritionally adequate (except for n-3 fatty acids) when planned appropriately.This trial was registered at clinicaltrials.gov as NCT03335644.

Lexical Frequency Effects on the Acquisition of Syntactic Properties in Heritage Spanish: A Study on Unaccusative and Unergative Predicates

Abstract This study investigates the acquisition of subject placement in unergative and unaccusative predicates among heritage speakers of Spanish by addressing two constraints ruling the distribution of subject placement (i.e., verb type and focus) as well as by examining whether lexical frequency impacts their acquisition. Sixty-three heritage speakers and twenty-five Spanish-dominant bilinguals completed an oral elicited production task and an acceptability judgment task. Results show that verb type constraints in the heritage speakers are shaped by their proficiency and self-reported lexical frequency: more proficient heritage speakers rated VS sentences higher than their lower-proficient counterparts, and more instances of VS sentences were produced with more frequent unaccusative verbs. Focus constraints only impact the heritage speakers’ acceptability: in narrow focus contexts, heritage speakers rate SV higher than VS sentences. Lexical frequency effects in unaccusative predicates are argued to be the result of spell-outs favoring compatible linear orders as a way to reduce computational costs.

A Hybrid Traffic Scheduling Strategy for Time-Sensitive Networking

The traffic scheduling mechanism in Time-Sensitive Networking (TSN) is the key to guaranteeing the deterministic transmission of traffic. However, when time-sensitive traffic and non-time-sensitive traffic are transmitted together, traffic scheduling conflicts are easy to occur in TSN. As a result, the deterministic transmission of time-sensitive traffic will be disrupted, and non-time-sensitive traffic may be preempted for a long time. To optimize the performance of multi-type hybrid traffic scheduling in TSN, we firstly establish a collaborative scheduling framework that incorporates Time Aware Shaping (TAS) and Cyclic Queuing and Forwarding (CQF) mechanisms. We then design a traffic shaping method in this framework based on Least Laxity First (LLF), which considers traffic characteristics to dynamically arrange the time slot injection sequence for different types of traffic. Finally, the traffic schedulability is evaluated based on the scheduling constraints of different types of traffic. Compared with the existing scheduling strategies, the proposed hybrid traffic scheduling strategy can schedule more non-time-sensitive traffic and achieve better delay performance of rate-constrained traffic in different hybrid traffic scenarios. When the number of flows is 100, the time slot injection ratio is increased by 24.3% compared with the LLF_TAS method.

A station location-allocation optimization problem in star-star pipeline network layout

The oil and gas field surface gathering pipeline network is an important hub for transporting oil and gas products outward, and its optimal design is crucial to reducing the construction cost of the pipeline system. Star-star topology is often applied to the layout of surface gathering pipeline networks in oil and gas fields. However, the optimization of star-star topology is often carried out under the constraint of the fixed station type, which may easily lead to a waste of station capacity and increase investment or make the optimization results meaningless. In this article, a mixed integer linear programming mathematical model for the overall optimization of star-star pipeline network layout considering the station location-allocation problem is constructed in discrete space with coupled station type constraints, and the branch-and-bound method is used to obtain the global optimal solution. Three cases verify the feasibility and effectiveness of the model and algorithm. The results show that the maximum avoided capacity waste is 20 × 104 m3/d and the maximum investment can be reduced by 8961.6 × 104 Yuan, which shows that the proposed model can effectively improve the economy and rationality of the pipeline system, and has important guiding significance for the construction of oil and gas fields.

Optimal scheduling of a wind energy dominated distribution network via a deep reinforcement learning approach

With the development of clean energy systems, large-scale renewable energy is being connected to the traditional distribution network, which also brings new challenges to the reliable and economic scheduling of the power grid. To address these challenges, this paper proposes an intelligent scheduling strategy for a wind energy dominated distribution network, which aims to reduce the fluctuation caused by the wind energy. First, the energy scheduling model and objective function of the distribution network system are established and the constraints of various types of components are considered. Then, deep reinforcement learning is introduced to realize real-time decision in distribution network to solve the problem of fluctuation caused by the uncertain wind power output. The energy scheduling method is developed into a Markov decision process based on deep deterministic policy gradient (DDPG) algorithm. Finally, the simulation is verified on the IEEE14 node system. The results verify that the proposed approach can effectively reduce power fluctuations in the distribution network. The superiority of the adopted DDPG algorithm is demonstrated by comparing with the deep Q network algorithm.

Knowledge graph embedding by relational rotation and complex convolution for link prediction

Knowledge graphs are organized as triplets to represent facts from the real world and play an important role in various intelligent information systems. Because knowledge graphs are frequently constructed using manual or semi-automatic methods, they often miss connections between entities. Link prediction was created to solve this problem. Many recent state-of-the-art studies, such as those introducing the RotatE and RotatHS models, have advocated for rotation transformations with entity and relation embeddings in complex vector spaces. However, using only rotation planes means that these models do not have the expressive power of models based on neural networks, such as the ConvE and the ConvR models. As a result, link prediction performance suffers. To address these shortcomings, this paper proposes the ConvRot model, which integrates a 2D convolution. Specifically, we perform convolution on embeddings of entities and relations to obtain support vector embeddings. These vectors are then integrated into an element-wise rotation from the head entity to the tail entity using the Hadamard product, enabling the model to capture local interactions among entities and relations through the neural network while still ensuring intuitiveness through a roto-transformation in the link prediction. In addition, we present two strategies for designing the complex convolution module and show their effects on model performance. The proposed method is evaluated on standard benchmark datasets and achieves significantly improved results on MRR and Hits@K (K = 1, 3, 10). Overall, our model’s link prediction performance is superior by approximately 5–7 %. Moreover, the ConvRot model is also considered separately on many relation types, such as one-to-one, one-to-many, many-to-one, and many-to-many. Finally, we prove that type constraints can help increase the model’s overall performance, especially on complex and large datasets.

Novel rare mutation in a conserved site of PTPRB causes human hypoplastic left heart syndrome.

Hypoplastic left heart syndrome (HLHS) is a rare but fatal birth defect in which the left side of the heart is underdeveloped. HLHS accounts for 2% to 4% of congenital heart anomalies. Whole genome sequencing (WGS) was conducted for a family trio consisting of a proband and his parents. A homozygous rare variant was detected in the PTPRB (Protein Tyrosine Phosphatase Receptor Type B) gene of the proband by functional annotation and co-segregation analysis. Sanger sequencing was used to confirm genotypes of the variant. The in silico prediction tools, including Mutation Taster, SpliceAI, and CADD, were used to predict the impact of the mutation. The allele frequencies across populations were compared based on multiple databases, including "1000 genomes" and "gnomAD". We used two vectors (pcMINI and pcDNA3.1) to generate a minigene construct to validate the mutational effect at the transcriptional level. Family-based WGS analyses showed that only a homozygous splice acceptor variant (NC_000012.12: g.70636068T>G, NM_001109754.4: c.56-2A>C, NG_029940.2: g.6373A>C) at the exon-intron border of PTPRB gene associates with HLHS. This variant is also within the region with the enhancer activity based on UCSC genome annotation. Genotyping and Sanger sequencing revealed that the proband's parents are heterozygous for this variant. Evolutionary conservation analysis revealed that the site (NC_000012.12: g.70636068) is extremely conserved across species, supporting the evolutionary functional constraints of the ancestral wild type (T). In silico tools universally predicted a deleterious or disease-causing impact of the mutation from T to G. The mutation was not found in the 1000 genomes and gnomAD databases, which indicates that this mutation is very rare in most human populations. A splicing assay indicated that the mutated minigene caused aberrant splicing of mRNA, in which a 3bp missing in the second exon resulted in the deletion of one amino acid (NP_001103224.1:p.Glu19del) compared to the normal protein of PRPTB (also the VE-PTP). Structure prediction revealed that the deletion occurred within the C-region of the signal peptide of VE-PTP, suggesting signal peptide-related defects as a potential mechanism for the HLHS cellular pathogeny. We report a rare homozygous variant with splicing error in PTPRB associated with HLHS. Previous model species studies revealed conserved functions of PTPRB in cardiovascular and heart development in mice and zebrafish. Our study is the first report to show the association between PTPRB and HLHS in humans.

Open-world link prediction via type-constraint embedding and hybrid attention for knowledge reuse of AI chip design

AI chips play a vital role in real-time AI applications. AI chip design requires vast knowledge reuse in integrated circuits and neural networks, and this knowledge continues to grow at a rapid rate, leading to a heavy burden on designers. Automated methods are urgently needed to handle the massive and rapidly growing literature. In the realm of traditional EDA, deep learning can produce excellent designs by learning from a large number of design examples. However, in front-end design such as algorithm and architecture design, a considerable amount of knowledge exists in the fast-growing literature. The use of this knowledge is still in a primitive state of manual processing. Therefore, this paper leverages the knowledge graph and the link prediction to organize and reuse AI chip design knowledge (ACDK). First, a knowledge graph is built to organize ACDK in triple format. Second, an open-world link prediction model named TC-THA is proposed, which aims at the closed-world limitation, the lack of type constraint, and the deep inner-association learning weakness of the related methods. TC-THA converts the type constraint of ACDK triples into vectors for better training and inference. It also uses a hybrid attention mechanism for the sufficient semantic interaction among inner-triple information, which is used to capture the deep internal associations of knowledge for the superior ACDK reuse. Experiments show that the TC-THA outperforms other methods and achieves the best MRR and H@10 score of 0.34 and 0.51 in ACDK link prediction. A case study of ACDK reuse shows that, for the given problems, the TC-THA predicts highly feasible solutions, which can be confirmed in the latest AI chip studies.

Simple Models Outperform More Complex Big-Leaf Models of Daily Transpiration in Forested Biomes.

Transpiration makes up the bulk of total evaporation in forested environments yet remains challenging to predict at landscape-to-global scales. We harnessed independent estimates of daily transpiration derived from co-located sap flow and eddy-covariance measurement systems and applied the triple collocation technique to evaluate predictions from big leaf models requiring no calibration. In total, four models in 608 unique configurations were evaluated at 21 forested sites spanning a wide diversity of biophysical attributes and environmental backgrounds. We found that simpler models that neither explicitly represented aerodynamic forcing nor canopy conductance achieved higher accuracy and signal-to-noise levels when optimally configured (rRMSE=20%; R 2=0.89). Irrespective of model type, optimal configurations were those making use of key plant functional type dependent parameters, daily LAI, and constraints based on atmospheric moisture demand over soil moisture supply. Our findings have implications for more informed water resource management based on hydrological modeling and remote sensing.

Bayesian Sample Size Planning Tool for Phase I Dose-Finding Trials.

Through Bayesian inference, we propose a method called BayeSize as a reference tool for investigators to assess the sample size and its associated scientific property for phase I clinical trials. BayeSize applies the concept of effect size in dose finding, assuming that the maximum tolerated dose can be identified on the basis of an interval surrounding its true value because of statistical uncertainty. Leveraging a decision framework that involves composite hypotheses, BayeSize uses two types of priors, the fitting prior (for model fitting) and sampling prior (for data generation), to conduct sample size calculation under the constraints of statistical power and type I error. Simulation results showed that BayeSize can provide reliable sample size estimation under the constraints of type I/II error rates. BayeSize could facilitate phase I trial planning by providing appropriate sample size estimation. Look-up tables and R Shiny app are provided for practical applications.

Heterogeneous Network Embedding Based on Random Walks of Type and Inner Constraint

In heterogeneous networks, random walks based on meta-paths require prior knowledge and lack flexibility. On the other hand, random walks based on non-meta-paths only consider the number of node types, but not the influence of schema and topology between node types in real networks. To solve these problems, this paper proposes a novel model HNE-RWTIC (Heterogeneous Network Embedding Based on Random Walks of Type and Inner Constraint). Firstly, to realize flexible walks, we design a Type strategy, which is a node type selection strategy based on the co-occurrence probability of node types. Secondly, to achieve the uniformity of node sampling, we design an Inner strategy, which is a node selection strategy based on the adjacency relationship between nodes. The Type and Inner strategy can realize the random walks based on meta-paths, the flexibility of the walks, and can sample the node types and nodes uniformly in proportion. Thirdly, based on the above strategy, a transition probability model is constructed; then, we obtain the nodes’ embedding based on the random walks and Skip-Gram. Finally, in the classification and clustering tasks, we conducted a thorough empirical evaluation of our method on three real heterogeneous networks. Experimental results show that HNE-RWTIC outperforms state-of-the-art approaches. In the classification task, in DBLP, AMiner-Top, and Yelp, the values of Micro-F1 and Macro-F1 of HNE-RWTIC are the highest: 2.25% and 2.43%, 0.85% and 0.99%, 3.77% and 5.02% higher than those of five other algorithms, respectively. In the clustering task, in DBLP, AMiner-Top, and Yelp networks, the NMI value is increased by 19.12%, 6.91%, and 0.04% at most, respectively.

ProtoE: Enhancing Knowledge Graph Completion Models with Unsupervised Type Representation Learning

Knowledge graph completion (KGC) models are a feasible approach for manipulating facts in knowledge graphs. However, the lack of entity types in current KGC models results in inaccurate link prediction results. Most existing type-aware KGC models require entity type annotations, which are not always available and expensive to obtain. We propose ProtoE, an unsupervised method for learning implicit type and type constraint representations. ProtoE enhances type-agnostic KGC models by relation-specific prototype embeddings. Our method does not rely on entity type annotations to capture the type and type constraints of entities. Unlike existing unsupervised type representation learning methods, which have only a single representation for entity-type and relation-type constraints, our method can capture multiple type constraints in relations. Experimental results show that our method can improve the performance of both bilinear and translational KGC models in the link prediction task.

Reverse Sensitivity Analysis for Risk Modelling

We consider the problem where a modeller conducts sensitivity analysis of a model consisting of random input factors, a corresponding random output of interest, and a baseline probability measure. The modeller seeks to understand how the model (the distribution of the input factors as well as the output) changes under a stress on the output’s distribution. Specifically, for a stress on the output random variable, we derive the unique stressed distribution of the output that is closest in the Wasserstein distance to the baseline output’s distribution and satisfies the stress. We further derive the stressed model, including the stressed distribution of the inputs, which can be calculated in a numerically efficient way from a set of baseline Monte Carlo samples and which is implemented in the R package SWIM on CRAN. The proposed reverse sensitivity analysis framework is model-free and allows for stresses on the output such as (a) the mean and variance, (b) any distortion risk measure including the Value-at-Risk and Expected-Shortfall, and (c) expected utility type constraints, thus making the reverse sensitivity analysis framework suitable for risk models.

Resource and Environment Constraints and Promotion Strategies of Rural Vitality: An Empirical Analysis of Rural Revitalization Model Towns

In response to the challenges of rural chronic poverty caused by insufficient self-development ability, establishing a development model conducive to poverty alleviation and stimulating rural development potential have become core tasks of deepening rural revitalization. Resources and environment play an important role in invigorating rural vitality. Based on symbiosis theory, we have constructed a new conceptual framework to analyze dynamic performance of rural areas in population, industry and facilities, and dynamic constraints in resources and environment. Using an improved TOPSIS evaluation method and panel Tobit model, we have selected 106 rural revitalization model towns from Shandong province as research samples, empirically analyzed the performance of rural vitality and its resources and environment constraints, explored differences of rural vitality performance and vitality constraints of different village types, and put forward long-term mechanisms to maintain rural vitality. Results show that from 2012 to 2019, rural vitality of rural revitalization model towns in Shandong province was gradually improved, among which industrial development had contributed the most; agriculture-tourism towns are more dynamic than industrialized agriculture towns and characteristic agricultural towns. Through analysis of resources and environment constraints on rural vitality, we found that resources had the greatest impact on rural vitality, and the short-term constraints of total amount restriction on rural vitality were not obvious; binding force of ecological environment on rural vitality changed from weak to strong and increasingly became the bottleneck restricting rural vitality. We have further proposed a long-term mechanism to stimulate rural vitality from the aspects of promoting comprehensive utilization of rural residential land, strengthening concept of ecological priority and green development, taking the road of urban–rural integration, and promoting integrated development of rural industries.

Revenue and Energy Efficiency-Driven Delay-Constrained Computing Task Offloading and Resource Allocation in a Vehicular Edge Computing Network: A Deep Reinforcement Learning Approach

For in-vehicle application, task type and vehicle state information, i.e., vehicle speed, bear a significant impact on the task delay requirement. However, the joint impact of task type and vehicle speed on the task delay constraint has not been studied, and this lack of study may cause a mismatch between the requirement of the task delay and allocated computation and wireless resources. In this article, we propose a joint task type and vehicle speed-aware task offloading and resource allocation strategy to decrease the vehicle’s energy cost for executing tasks and increase the revenue of the vehicle for processing tasks within the delay constraint. First, we establish the joint task type and vehicle speed-aware delay constraint model. Then, the delay, energy cost, and revenue for task execution in the vehicular edge computing (VEC) server, local terminal, and terminals of other vehicles are calculated. Based on the energy cost and revenue from task execution, the utility function of the vehicle is acquired. Next, we formulate a joint optimization of task offloading and resource allocation to maximize the utility level of the vehicles subject to the constraints of task delay, computation resources, and wireless resources. To obtain a near-optimal solution of the formulated problem, a joint offloading and resource allocation based on the multiagent deep deterministic policy gradient (JORA-MADDPG) algorithm is proposed to maximize the utility level of vehicles. Simulation results show that our algorithm can achieve superior performance in task completion delay, vehicles’ energy cost, and processing revenue.

Pontryagin’s Maximum Principle for Distributed Optimal Control of Two Dimensional Tidal Dynamics System with State Constraints of Integral Type

Pontryagin’s Maximum Principle for Distributed Optimal Control of Two Dimensional Tidal Dynamics System with State Constraints of Integral Type

Pursuit-evasion differential games with Gr-constraints on controls

In the paper, a pursuit-evasion differential game is considered when controls of the players are subject to differential constraints in the form of Grönwall's integral inequality. The strategy of parallel pursuit (briefly, $\Pi$-strategy) was introduced and used by L.A. Petrosyan to solve simple pursuit problems under phase constraints on the states of the players in the case when control functions of both players are chosen from the class $L_\infty$. In the present work, the $\Pi$-strategy is constructed for a simple pursuit problem in the cases when control functions of both players are chosen from different classes of the Grönwall type constraints, and sufficient conditions of capture and optimal capture time are found in these cases. To solve the evasion problem, we suggest a control function for the Evader and find sufficient conditions of evasion. In addition, an attainability domain of the players is constructed and its conditions of embedding in respect to time are given. Results of this work continue and extend the works of R. Isaacs, L.A. Petrosyan, B.N. Pshenichnyi, A.A. Chirii, A.A. Azamov and other researchers, including the authors.

Tackling Nonconvex Collision Avoidance Constraints for Optimal Trajectory Planning Using Saturation Functions

The problem of optimal trajectory planning with nonconvex collision avoidance constraints is discussed in this paper. The trajectory planning algorithm aims to generate a feasible trajectory with specified performance indexes to reduce collisions of aerospace vehicles in the presence of second-order smooth obstacles. The algorithm design that manages such mission scenarios is challenging because of the stringent avoidance constraints of various nonconvex types, especially considering the requirements of efficient computation. This problem is addressed within the sequential convex programming (SCP) framework by utilizing the characteristics of saturation functions. The introduction of the saturation function is found to significantly improve the convergence of the SCP algorithm by overcoming the drawbacks of potential interruptions from the iteration process and controlling the search directions for successive solutions. Theoretical analyses are performed to guarantee that the original constraints are satisfied and to ensure the iterative solution searches in the directions of the true optimal solution. Under multiple randomly generated test environments, the applications of the proposed approach in highly constrained aircraft path planning problems and spacecraft rendezvous operations are numerically simulated by comparing two representative conventional SCP-based methods to demonstrate the effectiveness, efficiency, and convergence of the proposed methodology.

Signatures of a Surviving Helium-star Companion in Type Ia Supernovae and Constraints on the Progenitor Companion of SN 2011fe

Abstract Single-degenerate (SD) binary systems composed of a white dwarf and a nondegenerate helium (He)-star companion have been proposed as the potential progenitors of Type Ia supernovae (SNe Ia). The He-star companions are expected to survive the SN Ia explosion in this SD progenitor model. In the present work, we map the surviving He-star companion models computed from our previous three-dimensional hydrodynamical simulations of ejecta–companion interaction into the one-dimensional stellar evolution code Modules for Experiments in Stellar Astrophysics to follow their long-term evolution to make predictions of their post-impact observational properties, which can be helpful in searches for such surviving He-star companions in future observations. By comparing with the very-late-epoch light curve of the best observed SN Ia, SN 2011fe, we find that our surviving He-star companions become significantly more luminous than SN 2011fe about 1000 days after maximum light. This suggests that an He star is very unlikely to be a companion to the progenitor of SN 2011fe.

Complex Embedding with Type Constraints for Link Prediction.

Large-scale knowledge graphs not only store entities and relations but also provide ontology-based information about them. Type constraints that exist in this information are of great importance for link prediction. In this paper, we proposed a novel complex embedding method, CHolE, in which complex circular correlation was introduced to extend the classic real-valued compositional representation HolE to complex domains, and type constraints were integrated into complex representational embeddings for improving link prediction. The proposed model consisted of two functional components, the type constraint model and the relation learning model, to form type constraints such as modulus constraints and acquire the relatedness between entities accurately by capturing rich interactions in the modulus and phase angles of complex embeddings. Experimental results on benchmark datasets showed that CHolE outperformed previous state-of-the-art methods, and the impartment of type constraints improved its performance on link prediction effectively.