Query: Image-Guided Nanopositioning Scheme for SEM

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Real-time rigid-body visual tracking in a scanning electron microscope <=SEP=> Robotics continues to provide researchers with an increasing ability to interact with objects at the nano scale. As micro- and nanorobotic technologies mature, more interest is given to computer-assisted or automated approaches to manipulation at these scales. Although actuators are currently available that enable displacements resolutions in the subnanometer range, improvements in feedback technologies have not kept pace. Thus, many actuators that are capable of performing nanometer displacements are limited in automated tasks by the lack of suitable feedback mechanisms. This paper proposes the use of a rigid-model-based method for end effector tracking in a scanning electron microscope to aid in enabling more precise automated manipulations and measurements. These models allow the system to leverage domain-specific knowledge to increase performance in a challenging tracking environment.

Extension of phase correlation to subpixel registration <=SEP=> In this paper, we have derived analytic expressions for the phase correlation of downsampled images. We have shown that for downsampled images the signal power in the phase correlation is not concentrated in a single peak, but rather in several coherent peaks mostly adjacent to each other. These coherent peaks correspond to the polyphase transform of a filtered unit impulse centered at the point of registration. The analytic results provide a closed-form solution to subpixel translation estimation, and are used for detailed error analysis. Excellent results have been obtained for subpixel translation estimation of images of different nature and across different spectral bands.

Determining Optical Flow <=SEP=> Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences. The algorithm is robust in that it can handle image sequences that are quantized rather coarsely in space and time. It is also insensitive to quantization of brightness levels and additive noise. Examples are included where the assumption of smoothness is violated at singular points or along lines in the image.

Improving resolution by image registration <=SEP=> Image resolution can be improved when the relative displacements in image sequences are known accurately, and some knowledge of the imaging process is available. The proposed approach is similar to back-projection used in tomography. Examples of improved image resolution are given for gray-level and color images, when the unknown image displacements are computed from the image sequence.

A Comparison of Affine Region Detectors <=SEP=> The paper gives a snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions. Six types of detectors are included: detectors based on affine normalization around Harris  (Mikolajczyk and  Schmid, 2002; Schaffalitzky and  Zisserman, 2002) and Hessian points  (Mikolajczyk and  Schmid, 2002), a detector of ‘maximally stable extremal regions', proposed by Matas et al. (2002); an edge-based region detector  (Tuytelaars and Van Gool, 1999) and a detector based on intensity extrema (Tuytelaars and Van Gool, 2000), and a detector of ‘salient regions', proposed by Kadir, Zisserman and Brady (2004). The performance is measured against changes in viewpoint, scale, illumination, defocus and image compression. The objective of this paper is also to establish a reference test set of images and performance software, so that future detectors can be evaluated in the same framework.

PROCEDURAL GENERATION OF IMAGINATIVE TREES USING A SPACE COLONIZATION ALGORITHM <=SEP=> The modeling of trees is challenging due to their complex branching structures. Three different ways to generate trees are using real world data for reconstruction, interactive modeling methods and modeling with procedural or rule-based systems. Procedural content generation is the idea of using algorithms to automate content creation processes, and it is useful in plant modeling since it can generate a wide variety of plants that can adapt and react to the environment and changing conditions. This thesis focuses on and extends a procedural tree generation technique that uses a space colonization algorithm to model the tree branches’ competition for space, and shifts the previous works’ focus from realism to fantasy. The technique satisfied the idea of using interaction between the tree’s internal and external factors to determine its final shape, by letting the designer control the where and the how of the tree’s growth process. The implementation resulted in a tree generation application where the user’s imagination decides the limit of what can be produced, and if that limit is reached can the application be used to randomly generate a wide variety of trees and tree-like structures. A motivation for many researchers in the procedural content generation area is how it can be used to augment human imagination. The result of this thesis can be used for that, by stepping away from the restrictions of realism, and with ease let the user generate widely diverse trees, that are not necessarily realistic but, in most cases, adapts to the idea of a tree.

The DEXMART hand: Mechatronic design and experimental evaluation of synergy-based control for human-like grasping <=SEP=> This paper summarizes recent activities carried out for the development of an innovative anthropomorphic robotic hand called the DEXMART Hand. The main goal of this research is to face the problems that affect current robotic hands by introducing suitable design solutions aimed at achieving simplification and cost reduction while possibly enhancing robustness and performance. While certain aspects of the DEXMART Hand development have been presented in previous papers, this paper is the first to give a comprehensive description of the final hand version and its use to replicate humanlike grasping. In this paper, particular emphasis is placed on the kinematics of the fingers and of the thumb, the wrist architecture, the dimensioning of the actuation system, and the final implementation of the position, force and tactile sensors. The paper focuses also on how these solutions have been integrated into the mechanical structure of this innovative robotic hand to enable precise force and displacement control of the whole system. Another important aspect is the lack of suitable control tools that severely limits the development of robotic hand applications. To address this issue, a new method for the observation of human hand behavior during interaction with common day-to-day objects by means of a 3D computer vision system is presented in this work together with a strategy for mapping human hand postures to the robotic hand. A simple control strategy based on postural synergies has been used to reduce the complexity of the grasp planning problem. As a preliminary evaluation of the DEXMART Hand’s capabilities, this approach has been adopted in this paper to simplify and speed up the transfer of human actions to the robotic hand, showing its effectiveness in reproducing human-like grasping.

Thin dual-resonant stacked shorted patch antenna for mobile communications <=SEP=> Introduction: Short-circuited microstrip patch antennas, or PIFAs, can be used as directive internal handset antennas in various communication systems. They provide advantages over traditional external whip and helix antennas in terms of increased total efficiency (when the handset is near the user’s head), decreased radiation towards the user, and increased mechanical reliability. The main disadvantage is their narrow impedance bandwidth compared to the volume occupied by the antenna structure. Typical requirements for directive internal antennas designed for cellular handsets (with a thickness ≤ 20mm) are antenna thickness ≤ 5mm and bandwidth ≥ 10%. It is difficult to design such a small, directive, low-profile handset antenna with high radiation efficiency and an impedance bandwidth greater than 10%. Small antenna design is always a compromise between size, bandwidth, and efficiency. Often, the price for improved performance is increased complexity. If the total dimensions of a patch antenna element are fixed, there are two effective ways to enhance its bandwidth: either dissipative loading [1], which reduces the radiation efficiency, or the addition of more resonators into the antenna structure (matching networks or parasitic elements). The latter method may increase the manufacturing complexity of the antenna. In this Letter, a thin dual-resonant stacked shorted patch antenna is presented. Previously, stacked short-circuited patch antenna elements have been reported in [2, 3]. More recently, similar elements have also been discussed in [4, 5]. The presented antenna is dual-resonant and has a very low profile. The thickness is only one fifth of that of the antenna reported in [5], whereas the surface area reserved for the patches is approximately equal. The small size of the antenna presented in this Letter makes it relatively easy to fit inside a cellular handset. The bandwidth is sufficient for many communication systems. With a common short circuit element which connects both patches to the ground plane, the presented antenna is the simplest example of a stacked shorted microstrip patch antenna. No extra tuning or shorting posts inside the patch area are needed, which makes its manufacturing easier.

DeepCas: An End-to-end Predictor of Information Cascades <=SEP=> Information cascades, effectively facilitated by most social network platforms, are recognized as a major factor in almost every social success and disaster in these networks. Can cascades be predicted? While many believe that they are inherently unpredictable, recent work has shown that some key properties of information cascades, such as size, growth, and shape, can be predicted by a machine learning algorithm that combines many features. These predictors all depend on a bag of hand-crafting features to represent the cascade network and the global network structures. Such features, always carefully and sometimes mysteriously designed, are not easy to extend or to generalize to a different platform or domain. Inspired by the recent successes of deep learning in multiple data mining tasks, we investigate whether an end-to-end deep learning approach could effectively predict the future size of cascades. Such a method automatically learns the representation of individual cascade graphs in the context of the global network structure, without hand-crafted features or heuristics. We find that node embeddings fall short of predictive power, and it is critical to learn the representation of a cascade graph as a whole. We present algorithms that learn the representation of cascade graphs in an end-to-end manner, which significantly improve the performance of cascade prediction over strong baselines including feature based methods, node embedding methods, and graph kernel methods. Our results also provide interesting implications for cascade prediction in general.

Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: Inverse relationships during postadolescent brain maturation. <=SEP=> Recent in vivo structural imaging studies have shown spatial and temporal patterns of brain maturation between childhood, adolescence, and young adulthood that are generally consistent with postmortem studies of cellular maturational events such as increased myelination and synaptic pruning. In this study, we conducted detailed spatial and temporal analyses of growth and gray matter density at the cortical surface of the brain in a group of 35 normally developing children, adolescents, and young adults. To accomplish this, we used high-resolution magnetic resonance imaging and novel computational image analysis techniques. For the first time, in this report we have mapped the continued postadolescent brain growth that occurs primarily in the dorsal aspects of the frontal lobe bilaterally and in the posterior temporo-occipital junction bilaterally. Notably, maps of the spatial distribution of postadolescent cortical gray matter density reduction are highly consistent with maps of the spatial distribution of postadolescent brain growth, showing an inverse relationship between cortical gray matter density reduction and brain growth primarily in the superior frontal regions that control executive cognitive functioning. Inverse relationships are not as robust in the posterior temporo-occipital junction where gray matter density reduction is much less prominent despite late brain growth in these regions between adolescence and adulthood. Overall brain growth is not significant between childhood and adolescence, but close spatial relationships between gray matter density reduction and brain growth are observed in the dorsal parietal and frontal cortex. These results suggest that progressive cellular maturational events, such as increased myelination, may play as prominent a role during the postadolescent years as regressive events, such as synaptic pruning, in determining the ultimate density of mature frontal lobe cortical gray matter.

Broadband antenna design using different 3D printing technologies and metallization processes <=SEP=> The purpose of this paper is to provide a comprehensive evaluation of how plastic materials, different metallization process, and thickness of the metal can influence the performance of 3D printed broadband antenna structures. A set of antennas were manufactured using Fused Deposition Modeling technology and Polyjet technology. Three different plastic materials ABS, PLA and Vero were employed in the tests. The antenna structures were metallized using three common metallization processes: vacuum metallization, electroplating, and conductive paint. In this project, the broadband performances of the metallized plastic antennas are compared to an original metal antenna by measuring VSWR, radiation patterns (H- and E-Planes), and gain. Measurements show that the performances of these plastic structures, regardless of production method, are very similar to the original metal antenna.

Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences <=SEP=> We present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. We solicit and verify questions and answers for this challenge through a 4-step crowdsourcing experiment. Our challenge dataset contains ∼6k questions for +800 paragraphs across 7 different domains (elementary school science, news, travel guides, fiction stories, etc) bringing in linguistic diversity to the texts and to the questions wordings. On a subset of our dataset, we found human solvers to achieve an F1-score of 86.4%. We analyze a range of baselines, including a recent state-of-art reading comprehension system, and demonstrate the difficulty of this challenge, despite a high human performance. The dataset is the first to study multi-sentence inference at scale, with an open-ended set of question types that requires reasoning skills.

Kd-Jump: a Path-Preserving Stackless Traversal for Faster Isosurface Raytracing on GPUs <=SEP=> Stackless traversal techniques are often used to circumvent memory bottlenecks by avoiding a stack and replacing return traversal with extra computation. This paper addresses whether the stackless traversal approaches are useful on newer hardware and technology (such as CUDA). To this end, we present a novel stackless approach for implicit kd-trees, which exploits the benefits of index-based node traversal, without incurring extra node visitation. This approach, which we term Kd-Jump, enables the traversal to immediately return to the next valid node, like a stack, without incurring extra node visitation (kd-restart). Also, Kd-Jump does not require global memory (stack) at all and only requires a small matrix in fast constant-memory. We report that Kd-Jump outperforms a stack by 10 to 20% and kd-restar t by 100%. We also present a Hybrid Kd-Jump, which utilizes a volume stepper for leaf testing and a run-time depth threshold to define where kd-tree traversal stops and volume-stepping occurs. By using both methods, we gain the benefits of empty space removal, fast texture-caching and realtime ability to determine the best threshold for current isosurface and view direction.

Focused Trajectory Planning for autonomous on-road driving <=SEP=> On-road motion planning for autonomous vehicles is in general a challenging problem. Past efforts have proposed solutions for urban and highway environments individually. We identify the key advantages/shortcomings of prior solutions, and propose a novel two-step motion planning system that addresses both urban and highway driving in a single framework. Reference Trajectory Planning (I) makes use of dense lattice sampling and optimization techniques to generate an easy-to-tune and human-like reference trajectory accounting for road geometry, obstacles and high-level directives. By focused sampling around the reference trajectory, Tracking Trajectory Planning (II) generates, evaluates and selects parametric trajectories that further satisfy kinodynamic constraints for execution. The described method retains most of the performance advantages of an exhaustive spatiotemporal planner while significantly reducing computation.

Pattern Recognition for Downhole Dynamometer Card in Oil Rod Pump System using Artificial Neural Networks <=SEP=> This paper presents the development of an Artificial Neural Network system for Dynamometer Card pattern recognition in oil well rod pump systems. It covers the establishment of pattern classes and a set of standards for training and validation, the study of descriptors which allow the design and the implementation of features extractor, training, analysis and finally the validation and performance test with

Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment <=SEP=> We consider the task of predicting lexical entailment using distributional vectors. We focus experiments on one previous classifier which was shown to only learn to detect prototypicality of a word pair. Analysis shows that the model single-mindedly learns to detect Hearst Patterns, which are well known to be predictive of lexical relations. We present a new model which exploits this Hearst Detector functionality, matching or outperforming prior work on multiple data sets.

A Subsequence Interleaving Model for Sequential Pattern Mining <=SEP=> Recent sequential pattern mining methods have used the minimum description length (MDL) principle to define an encoding scheme which describes an algorithm for mining the most compressing patterns in a database. We present a novel subsequence interleaving model based on a probabilistic model of the sequence database, which allows us to search for the most compressing set of patterns without designing a specific encoding scheme. Our proposed algorithm is able to efficiently mine the most relevant sequential patterns and rank them using an associated measure of interestingness. The efficient inference in our model is a direct result of our use of a structural expectation-maximization framework, in which the expectation-step takes the form of a submodular optimization problem subject to a coverage constraint. We show on both synthetic and real world datasets that our model mines a set of sequential patterns with low spuriousness and redundancy, high interpretability and usefulness in real-world applications. Furthermore, we demonstrate that the quality of the patterns from our approach is comparable to, if not better than, existing state of the art sequential pattern mining algorithms.

Tracing Data Lineage Using Schema Transformation Pathways <=SEP=> With the increasing amount and diversity of information available on the Internet, there has been a huge growth in information systems that need to integrate data from distributed, heterogeneous data sources. Tracing the lineage of the integrated data is one of the current problems being addressed in data warehouse research. In this paper, we propose a new approach for tracing data linage based on schema transformation pathways. We show how the individual transformation steps in a transformation pathway can be used to trace the derivation of the integrated data in a step-wise fashion, thus simplifying the lineage tracing process.

Towards a theory of supply chain management : the constructs and measurements <=SEP=> Rising international cooperation, vertical disintegration, along with a focus on core activities have led to the notion that firms are links in a networked supply chain. This novel perspective has created the challenge of designing and managing a network of interdependent relationships developed and fostered through strategic collaboration. Although research interests in supply chain management (SCM) are growing, no research has been directed towards a systematic development of SCM instruments. This study identifies and consolidates various supply chain initiatives and factors to develop key SCM constructs conducive to advancing the field. To this end, we analyzed over 400 articles and synthesized the large, fragmented body of work dispersed across many disciplines. The result of this study, through successive stages of measurement analysis and refinement, is a set of reliable, valid, and unidimensional measurements that can be subsequently used in different contexts to refine or extend conceptualization and measurements or to test various theoretical models, paving the way for theory building in SCM. © 2004 Elsevier B.V. All rights reserved.

Sieve: Cryptographically Enforced Access Control for User Data in Untrusted Clouds <=SEP=> Modern web services rob users of low-level control over cloud storage; a user’s single logical data set is scattered across multiple storage silos whose access controls are set by the web services, not users. The result is that users lack the ultimate authority to determine how their data is shared with other web services. In this thesis, we introduce Sieve, a new architecture for selectively exposing user data to third party web services in a provably secure manner. Sieve starts with a user-centric storage model: each user uploads encrypted data to a single cloud store, and by default, only the user knows the decryption keys. Given this storage model, Sieve defines an infrastructure to support rich, legacy web applications. Using attribute-based encryption, Sieve allows users to define intuitive, understandable access policies that are cryptographically enforceable. Using key homomorphism, Sieve can re-encrypt user data on storage providers in situ, revoking decryption keys from web services without revealing new ones to the storage provider. Using secret sharing and two-factor authentication, Sieve protects against the loss of user devices like smartphones and laptops. The result is that users can enjoy rich, legacy web applications, while benefitting from cryptographically strong controls over what data the services can access. Thesis Supervisor: Nickolai Zeldovich Title: Associate Professor Thesis Supervisor: James Mickens Title: Associate Professor

Segmentation of Urban Areas Using Road Networks <=SEP=> Region-based analysis is fundamental and crucial in many geospatialrelated applications and research themes, such as trajectory analysis, human mobility study and urban planning. In this paper, we report on an image-processing-based approach to segment urban areas into regions by road networks. Here, each segmented region is bounded by the high-level road segments, covering some neighborhoods and low-level streets. Typically, road segments are classified into different levels (e.g., highways and expressways are usually high-level roads), providing us with a more natural and semantic segmentation of urban spaces than the grid-based partition method. We show that through simple morphological operators, an urban road network can be efficiently segmented into regions. In addition, we present a case study in trajectory mining to demonstrate the usability of the proposed segmentation method.

Collaborative Deep Learning Across Multiple Data Centers <=SEP=> Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible to transfer all data to a centralized data center due to not only bandwidth limitation but also the constraints of privacy regulations. Model averaging is a conventional choice for data parallelized training, but its ineffectiveness is claimed by previous studies as deep neural networks are often non-convex. In this paper, we argue that model averaging can be effective in the decentralized environment by using two strategies, namely, the cyclical learning rate and the increased number of epochs for local model training. With the two strategies, we show that model averaging can provide competitive performance in the decentralized mode compared to the data-centralized one. In a practical environment with multiple data centers, we conduct extensive experiments using state-of-the-art deep network architectures on different types of data. Results demonstrate the effectiveness and robustness of the proposed method.

An improved box-counting method for image fractal dimension estimation <=SEP=> Article history: Received 6 September 2007 Received in revised form 14 January 2009 Accepted 2 March 2009

BanditSum: Extractive Summarization as a Contextual Bandit <=SEP=> In this work, we propose a novel method for training neural networks to perform singledocument extractive summarization without heuristically-generated extractive labels. We call our approach BANDITSUM as it treats extractive summarization as a contextual bandit (CB) problem, where the model receives a document to summarize (the context), and chooses a sequence of sentences to include in the summary (the action). A policy gradient reinforcement learning algorithm is used to train the model to select sequences of sentences that maximize ROUGE score. We perform a series of experiments demonstrating that BANDITSUM is able to achieve ROUGE scores that are better than or comparable to the state-of-the-art for extractive summarization, and converges using significantly fewer update steps than competing approaches. In addition, we show empirically that BANDITSUM performs significantly better than competing approaches when good summary sentences appear late in the source document.

Introduction to the special section on educational data mining <=SEP=> Educational Data Mining (EDM) is an emerging multidisciplinary research area, in which methods and techniques for exploring data originating from various educational information systems have been developed. EDM is both a learning science, as well as a rich application area for data mining, due to the growing availability of educational data. EDM contributes to the study of how students learn, and the settings in which they learn. It enables data-driven decision making for improving the current educational practice and learning material. We present a brief overview of EDM and introduce four selected EDM papers representing a crosscut of different application areas for data mining in education.

Polynomial method for Procedural Terrain Generation <=SEP=> A systematic fractal brownian motion approach is proposed for generating coherent noise, aiming at procedurally generating realistic terrain and textures. Two models are tested and compared to Perlin noise method for two-dimensional height map generation. A fractal analysis is performed in order to compare fractal behaviour of generated data to real terrain coastlines from the point of view of fractal dimension. Performance analysis show that one of the described schemes requires half as many primitive operations than Perlin noise while producing data of equivalent quality.

Fully Convolutional Attention Networks for Fine-Grained Recognition <=SEP=> Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations such as poses. A key to address this problem is to localize discriminative parts to extract pose-invariant features. However, ground-truth part annotations can be expensive to acquire. Moreover, it is hard to define parts for many fine-grained classes. This work introduces Fully Convolutional Attention Networks (FCANs), a reinforcement learning framework to optimally glimpse local discriminative regions adaptive to different fine-grained domains. Compared to previous methods, our approach enjoys three advantages: 1) the weakly-supervised reinforcement learning procedure requires no expensive part annotations; 2) the fully-convolutional architecture speeds up both training and testing; 3) the greedy reward strategy accelerates the convergence of the learning. We demonstrate the effectiveness of our method with extensive experiments on four challenging fine-grained benchmark datasets, including CUB-200-2011, Stanford Dogs, Stanford Cars and Food-101.

Consumption of ultra-processed foods and likely impact on human health. Evidence from Canada. <=SEP=> OBJECTIVE
To investigate consumption of ultra-processed products in Canada and to assess their association with dietary quality.


DESIGN
Application of a classification of foodstuffs based on the nature, extent and purpose of food processing to data from a national household food budget survey. Foods are classified as unprocessed/minimally processed foods (Group 1), processed culinary ingredients (Group 2) or ultra-processed products (Group 3).


SETTING
All provinces and territories of Canada, 2001.


SUBJECTS
Households (n 5643).


RESULTS
Food purchases provided a mean per capita energy availability of 8908 (se 81) kJ/d (2129 (se 19) kcal/d). Over 61·7 % of dietary energy came from ultra-processed products (Group 3), 25·6 % from Group 1 and 12·7 % from Group 2. The overall diet exceeded WHO upper limits for fat, saturated fat, free sugars and Na density, with less fibre than recommended. It also exceeded the average energy density target of the World Cancer Research Fund/American Institute for Cancer Research. Group 3 products taken together are more fatty, sugary, salty and energy-dense than a combination of Group 1 and Group 2 items. Only the 20 % lowest consumers of ultra-processed products (who consumed 33·2 % of energy from these products) were anywhere near reaching all nutrient goals for the prevention of obesity and chronic non-communicable diseases.


CONCLUSIONS
The 2001 Canadian diet was dominated by ultra-processed products. As a group, these products are unhealthy. The present analysis indicates that any substantial improvement of the diet would involve much lower consumption of ultra-processed products and much higher consumption of meals and dishes prepared from minimally processed foods and processed culinary ingredients.

Diagnostically relevant facial gestalt information from ordinary photos <=SEP=> Craniofacial characteristics are highly informative for clinical geneticists when diagnosing genetic diseases. As a first step towards the high-throughput diagnosis of ultra-rare developmental diseases we introduce an automatic approach that implements recent developments in computer vision. This algorithm extracts phenotypic information from ordinary non-clinical photographs and, using machine learning, models human facial dysmorphisms in a multidimensional 'Clinical Face Phenotype Space'. The space locates patients in the context of known syndromes and thereby facilitates the generation of diagnostic hypotheses. Consequently, the approach will aid clinicians by greatly narrowing (by 27.6-fold) the search space of potential diagnoses for patients with suspected developmental disorders. Furthermore, this Clinical Face Phenotype Space allows the clustering of patients by phenotype even when no known syndrome diagnosis exists, thereby aiding disease identification. We demonstrate that this approach provides a novel method for inferring causative genetic variants from clinical sequencing data through functional genetic pathway comparisons.DOI: http://dx.doi.org/10.7554/eLife.02020.001.

Effect of Control-Loops Interactions on Power Stability Limits of VSC Integrated to AC System <=SEP=> This paper investigates the effect of control-loops interactions on power stability limits of the voltage-source converter (VSC) as connected to an ac system. The focus is put on the physical mechanism of the control-loops interactions in the VSC, revealing that interactions among the control loops result in the production of an additional loop. The open-loop gain of the additional loop is employed to quantify the severity of the control-loop interactions. Furthermore, the power current sensitivity, closely related to control-loops interactions, is applied to estimate the maximum transferrable power of the VSC connected to an ac grid. On that basis, stability analysis results show that interactions between dc-link voltage control and phase-locked loop restrict the power angle to about 51° for stable operation with no dynamic reactive power supported. Conversely, the system is capable of approaching the ac-side maximum power transfer limits with alternating voltage control included. Simulations in MATLAB/Simulink are conducted to validate the stability analysis.