Query: Towards Efficient Cryptographic Group Access Control Systems

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Identity-Based Broadcast Encryption with Constant Size Ciphertexts and Private Keys <=SEP=> This paper describes the first identity-based broadcast encryption scheme (IBBE) with constant size ciphertexts and private keys. In our scheme, the public key is of size linear in the maximal size m of the set of receivers, which is smaller than the number of possible users (identities) in the system. Compared with a recent broadcast encryption system introduced by Boneh, Gentry and Waters (BGW), our system has comparable properties, but with a better efficiency: the public key is shorter than in BGW. Moreover, the total number of possible users in the system does not have to be fixed in the setup.

Unobservable Communication over Fully Untrusted Infrastructure <=SEP=> Keeping communication private has become increasingly important in an era of mass surveillance and statesponsored attacks. While hiding the contents of a conversation has well-known solutions, hiding the associated metadata (participants, duration, etc.) remains a challenge, especially if one cannot trust ISPs or proxy servers. This paper describes a communication system called Pung that provably hides all content and metadata while withstanding global adversaries. Pung is a key-value store where clients deposit and retrieve messages without anyone— including Pung’s servers—learning of the existence of a conversation. Pung is based on private information retrieval, which we make more practical for our setting with new techniques. These include a private multi-retrieval scheme, an application of the power of two choices, and batch codes. These extensions allow Pung to handle 103× more users than prior systems with a similar threat model.

IRON: Functional Encryption using Intel SGX <=SEP=> Functional encryption (FE) is an extremely powerful cryptographic mechanism that lets an authorized entity compute on encrypted data, and learn the results in the clear. However, all current cryptographic instantiations for general FE are too impractical to be implemented. We construct IRON, a provably secure, and practical FE system using Intel's recent Software Guard Extensions (SGX). We show that IRON can be applied to complex functionalities, and even for simple functions, outperforms the best known cryptographic schemes. We argue security by modeling FE in the context of hardware elements, and prove that IRON satisfies the security model.

SCONE: Secure Linux Containers with Intel SGX <=SEP=> Sergei Arnautov, Bohdan Trach, Franz Gregor, Thomas Knauth, Andre Martin, Christian Priebe, Joshua Lind, Divya Muthukumaran, Dan O’Keeffe, Mark L Stillwell, David Goltzsche, David Eyers, Rüdiger Kapitza, Peter Pietzuch, and Christof Fetzer Fakultät Informatik, TU Dresden, christof.fetzer@tu-dresden.de Dept. of Computing, Imperial College London, prp@imperial.ac.uk Informatik, TU Braunschweig, rrkapitz@ibr.cs.tu-bs.de Dept. of Computer Science, University of Otago, dme@cs.otago.ac.nz

Sanctum: Minimal Hardware Extensions for Strong Software Isolation <=SEP=> Sanctum offers the same promise as Intel’s Software Guard Extensions (SGX), namely strong provable isolation of software modules running concurrently and sharing resources, but protects against an important class of additional software attacks that infer private information from a program’s memory access patterns. Sanctum shuns unnecessary complexity, leading to a simpler security analysis. We follow a principled approach to eliminating entire attack surfaces through isolation, rather than plugging attack-specific privacy leaks. Most of Sanctum’s logic is implemented in trusted software, which does not perform cryptographic operations using keys, and is easier to analyze than SGX’s opaque microcode, which does. Our prototype targets a Rocket RISC-V core, an open implementation that allows any researcher to reason about its security properties. Sanctum’s extensions can be adapted to other processor cores, because we do not change any major CPU building block. Instead, we add hardware at the interfaces between generic building blocks, without impacting cycle time. Sanctum demonstrates that strong software isolation is achievable with a surprisingly small set of minimally invasive hardware changes, and a very reasonable overhead.

Consensus photometric stereo <=SEP=> This paper describes a photometric stereo method that works with a wide range of surface reflectances. Unlike previous approaches that assume simple parametric models such as Lambertian reflectance, the only assumption that we make is that the reflectance has three properties; monotonicity, visibility, and isotropy with respect to the cosine of light direction and surface orientation. In fact, these properties are observed in many non-Lambertian diffuse reflectances. We also show that the monotonicity and isotropy properties hold specular lobes with respect to the cosine of the surface orientation and the bisector between the light direction and view direction. Each of these three properties independently gives a possible solution space of the surface orientation. By taking the intersection of the solution spaces, our method determines the surface orientation in a consensus manner. Our method naturally avoids the need for radiometrically calibrating cameras because the radiometric response function preserves these three properties. The effectiveness of the proposed method is demonstrated using various simulated and real-world scenes that contain a variety of diffuse and specular surfaces.

A computer vision-aided motion sensing algorithm for mobile robot's indoor navigation <=SEP=> This paper presents the design and analysis of a computer vision-aided motion sensing algorithm for wheeled mobile robot's indoor navigation. The algorithm is realized using two vision cameras attached on a wheeled mobile robot. The first camera is positioned at front-looking direction while the second camera is positioned at downward-looking direction. An algorithm is developed to process the images acquired from the cameras to yield the mobile robot's positions and orientations. The proposed algorithm is implemented on a wheeled mobile robot for real-world effectiveness testing. Results are compared and shown the accuracy of the proposed algorithm. At the end of the paper, an artificial landmark approach is introduced to improve the navigation efficiency. Future work involved implementing the proposed artificial landmark for indoor navigation applications with minimized accumulated errors.

ANALYTIC PROBLEMS FOR ELLIPTIC CURVES <=SEP=> We consider some problems of analytic number theory for elliptic curves which can be considered as analogues of classical questions around the distribution of primes in arithmetic progressions to large moduli, and to the question of twin primes. This leads to some local results on the distribution of the group structures of elliptic curves defined over a prime finite field, exhibiting an interesting dichotomy for the occurence of the possible groups.

Classification of text documents supervised by domain ontologies <=SEP=> The research objective is to establish an approach for supporting the classification of text documents referring to a specified domain. The focus is on the preliminary topic assignment to the documents used for training the model. The method implements domain ontology as background knowledge. The idea consists in extracting the preliminary topics for training the classifier by means of unsupervised machine learning on a text corpus and further alignment of the document vectors to concepts of the ontology. The results obtained by classification of new documents supervised by e-governance ontology with several machine learning algorithms showed sufficient match of their content to the ontology concepts. A conclusion is drawn that the approach can support the automatic extraction of documents relevant to any domain described by ontology.

Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm <=SEP=> In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification.

Function and regulation in MAPK signaling pathways: lessons learned from the yeast Saccharomyces cerevisiae. <=SEP=> Signaling pathways that activate different mitogen-activated protein kinases (MAPKs) elicit many of the responses that are evoked in cells by changes in certain environmental conditions and upon exposure to a variety of hormonal and other stimuli. These pathways were first elucidated in the unicellular eukaryote Saccharomyces cerevisiae (budding yeast). Studies of MAPK pathways in this organism continue to be especially informative in revealing the molecular mechanisms by which MAPK cascades operate, propagate signals, modulate cellular processes, and are controlled by regulatory factors both internal to and external to the pathways. Here we highlight recent advances and new insights about MAPK-based signaling that have been made through studies in yeast, which provide lessons directly applicable to, and that enhance our understanding of, MAPK-mediated signaling in mammalian cells.

The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function <=SEP=> The ALS Functional Rating Scale (ALSFRS) is a validated rating instrument for monitoring the progression of disability in patients with amyotrophic lateral sclerosis (ALS). One weakness of the ALSFRS as originally designed was that it granted disproportionate weighting to limb and bulbar, as compared to respiratory, dysfunction. We have now validated a revised version of the ALSFRS, which incorporates additional assessments of dyspnea, orthopnea, and the need for ventilatory support. The Revised ALSFRS (ALSFRS-R) retains the properties of the original scale and shows strong internal consistency and construct validity. ALSFRS-R scores correlate significantly with quality of life as measured by the Sickness Impact Profile, indicating that the quality of function is a strong determinant of quality of life in ALS.

Designing and implementing an integrated technological pedagogical science knowledge framework for science teachers professional development <=SEP=> This paper reports on the design and the implementation of the Technological Pedagogical Science Knowledge (TPASK), a new model for science teachers professional development built on an integrated framework determined by the Technological Pedagogical Content Knowledge (TPACK) model and the authentic learning approach. The TPASK curriculum dimensions and the related course sessions are also elaborated and applied in the context of a teacher trainers’ preparation program aiming at ICT integration in science classroom practice. A brief description of the project, its accomplishments, and perceptions of the participants, through the lens of TPASK professional development model, are presented. This is followed by the presentation of the evaluation results on the impact of the program which demonstrates that science teachers reported meaningful TPASK knowledge and increased willingness to adopt and apply this framework in their instruction. Finally, we draw on the need to expand TPACK by incorporating a fourth dimension, the Educational Context within Pedagogy, Content and Technology mutually interact, in order to address future policy models concerning teacher preparation to integrate ICT in education. 2010 Elsevier Ltd. All rights reserved.

Improv Chat: Second Response Generation for Chatbot <=SEP=> Existing research on response generation for chatbot focuses on First Response Generation which aims to teach the chatbot to say the first response (e.g. a sentence) appropriate to the conversation context (e.g. the user’s query). In this paper, we introduce a new task Second Response Generation, termed as Improv chat, which aims to teach the chatbot to say the second response after saying the first response with respect the conversation context, so as to lighten the burden on the user to keep the conversation going. Specifically, we propose a general learning based framework and develop a retrieval based system which can generate the second responses with the users’ query and the chatbot’s first response as input. We present the approach to building the conversation corpus for Improv chat from public forums and social networks, as well as the neural networks based models for response matching and ranking. We include the preliminary experiments and results in this paper. This work could be further advanced with better deep matching models for retrieval base systems or generative models for generation based systems as well as extensive evaluations in real-life applications.

Multilabel Classification via Co-Evolutionary Multilabel Hypernetwork <=SEP=> Multilabel classification is prevalent in many real-world applications where data instances may be associated with multiple labels simultaneously. In multilabel classification, exploiting label correlations is an essential but nontrivial task. Most of the existing multilabel learning algorithms are either ineffective or computationally demanding and less scalable in exploiting label correlations. In this paper, we propose a co-evolutionary multilabel hypernetwork (Co-MLHN) as an attempt to exploit label correlations in an effective and efficient way. To this end, we firstly convert the traditional hypernetwork into a multilabel hypernetwork (MLHN) where label correlations are explicitly represented. We then propose a co-evolutionary learning algorithm to learn an integrated classification model for all labels. The proposed Co-MLHN exploits arbitrary order label correlations and has linear computational complexity with respect to the number of labels. Empirical studies on a broad range of multilabel data sets demonstrate that Co-MLHN achieves competitive results against state-of-the-art multilabel learning algorithms, in terms of both classification performance and scalability with respect to the number of labels.

Robust Appointment Scheduling <=SEP=> We consider the problem of appointment scheduling in a robus t optimization framework. The appointment scheduling problem arises in many service operations, for e xample health care. For each job, we are given its minimum and maximum possible execution times. The objective is to find an appointment schedule for which the cost in the worst case scenario of the realization of the processi ng times of the jobs is minimized. We present a global balancing heuristic, which gives an easy to compute closed f orm optimal schedule when the underage costs of the jobs are non-decreasing. In addition, for the case where we h av the flexibility of changing the order of execution of the jobs, we give simple heuristics to find a near-optimal seq uence of the jobs. Working paper, Operations Research Center, MIT. Last updat ed March 3, 2012. Amazon.com, 333 Boren Avenue N, Seattle WA 98101, USA. Email : mshashi@alum.mit.edu. TU Berlin, Matheon, Berlin, Germany. Email: sebastian.stiller@tu-berlin.de.

The ASTOOT Approach to Testing Object-Oriented Programs <=SEP=> This article describes a new approach to the unit testing of object-oriented programs, a set of tools based on this approach, and two case studies. In this approach, each test case consists of a tuple of sequences of messages, along with tags indicating whether these sequences should put objects of the class under test into equivalent states and/or return objects that are in equivalent states. Tests are executed by sending the sequences to objects of the class under test, then invoking a user-supplied equivalence-checking mechanism. This approach allows for substantial automation of many aspects of testing, including test case generation, test driver generation, test execution, and test checking. Experimental prototypes of tools for test generation and test execution are described. The test generation tool requires the availability of an algebraic specification of the abstract data type being tested, but the test execution tool can be used when no formal specification is available. Using the test execution tools, case studies involving execution of tens of thousands of test cases, with various sequence lengths, parameters, and combinations of operations were performed. The relationships among likelihood of detecting an error and sequence length, range of parameters, and relative frequency of various operations were investigated for priority queue and sorted-list implementations having subtle errors. In each case, long sequences tended to be more likely to detect the error, provided that the range of parameters was sufficiently large and likelihood of detecting an error tended to increase up to a threshold value as the parameter range increased.

Confirmatory factor analysis of the WAIS-IV/WMS-IV. <=SEP=> The Wechsler Adult Intelligence Scale-fourth edition (WAIS-IV) and the Wechsler Memory Scale-fourth edition (WMS-IV) were co-developed to be used individually or as a combined battery of tests. The independent factor structure of each of the tests has been identified; however, the combined factor structure has yet to be determined. Confirmatory factor analysis was applied to the WAIS-IV/WMS-IV Adult battery (i.e., age 16-69 years) co-norming sample (n = 900) to test 13 measurement models. The results indicated that two models fit the data equally well. One model is a seven-factor solution without a hierarchical general ability factor: Verbal Comprehension, Perceptual Reasoning, Processing Speed, Auditory Working Memory, Visual Working Memory, Auditory Memory, and Visual Memory. The second model is a five-factor model composed of Verbal Comprehension, Perceptual Reasoning, Processing Speed, Working Memory, and Memory with a hierarchical general ability factor. Interpretative implications for each model are discussed.

A compact planar feed structure for Ka-band satcom-on-the-move tracking antennas <=SEP=> The increasing interest in bi-directional mobile high data rate satellite communications in Ka-band necessitates the development of dedicated antenna tracking systems and feeds. In this paper we describe a compact feed structure based on printed circuit boards for a mobile satellite communications ground terminal with a Cassegrain reflector antenna. The novel structure provides a dual circular polarisation communication mode as well as the TM01 mode for multimode monopulse tracking. This coupler, based on carefully matched transitions from grounded coplanar lines to circular waveguides, is operational at 20GHz and 30 GHz, to cover the downlink and the uplink frequency ranges in Ka-band. This work contributes to the development of a satellite terminal for land-mobile communications in disaster scenarios.

Dysbiosis of the gut microbiota in disease <=SEP=> There is growing evidence that dysbiosis of the gut microbiota is associated with the pathogenesis of both intestinal and extra-intestinal disorders. Intestinal disorders include inflammatory bowel disease, irritable bowel syndrome (IBS), and coeliac disease, while extra-intestinal disorders include allergy, asthma, metabolic syndrome, cardiovascular disease, and obesity.

Learning in and about complex systems <=SEP=> Change is accelerating, and the complexity of the systems in which we live is growing. Increasingly change is the result of humanity itself. As complexity grows so do the unanticipated side effects of human action, further increasing complexity in a vicious cycle. Many scholars call for the development of 'systems thinking' to improve our ability to manage wisely. But how do people learn in and about complex dynamic systems? Learning is a feedback process in which our decisions alter the real world, we receive information feedback about the world, and using the new information we revise the decisions we make and the mental models that motivate those decisions. Unfortunately, in the world of social action various impediments slow or prevent these learning feedbacks from functioning, allowing erroneous and harmful behaviors and beliefs to persist. The barriers to learning include the dynamic complexity of the systems themselves, inadequate and ambiguous outcome feedback, systematic 'misperceptions of feedback' where our cognitive maps omit important feedback processes, delays, stocks and flows, and nonlinearities that characterize complex systems, inability to simulate mentally the dynamics of our cognitive maps, poor interpersonal and organizational inquiry skills, and poor scientific reasoning skills. To be successful methods to enhance learning about complex systems must address all these impediments. Effective methods for learning in and about complex dynamic systems must include (1) tools to elicit participant knowledge, articulate and reframe perceptions, and create maps of the feedback structure of a problem from those perceptions; (2) simulation tools and management flight simulators to assess the dynamics of those maps and test new policies; and (3) methods to improve scientific reasoning skills, strengthen group process and overcome defensive routines for individuals and teams.

Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices <=SEP=> Memristive nanodevices can feature a compact multilevel nonvolatile memory function, but are prone to device variability. We propose a novel neural network-based computing paradigm, which exploits their specific physics, and which has virtual immunity to their variability. Memristive devices are used as synapses in a spiking neural network performing unsupervised learning. They learn using a simplified and customized “spike timing dependent plasticity” rule. In the network, neurons’ threshold is adjusted following a homeostasis-type rule. We perform system level simulations with an experimentally verified model of the memristive devices’ behavior. They show, on the textbook case of character recognition, that performance can compare with traditional supervised networks of similar complexity. They also show that the system can retain functionality with extreme variations of various memristive devices’ parameters (a relative standard dispersion of more than 50% is tolerated on all device parameters), thanks to the robustness of the scheme, its unsupervised nature, and the capability of homeostasis. Additionally the network can adjust to stimuli presented with different coding schemes, is particularly robust to read disturb effects and does not require unrealistic control on the devices’ conductance. These results open the way for a novel design approach for ultraadaptive electronic systems.

AlGaN/GaN current aperture vertical electron transistors <=SEP=> Describes AlGaN/GaN current aperture vertical electron transistor (CAVET) structures. A CAVET consists of a source region separated from a drain region by an insulating layer containing a narrow aperture which is filled with conducting material. A device mesa is formed by reactive ion etching, and source contacts are deposited on either side of the aperture. The drain metal contacts the n-doped region below the aperture. Electrons flow from the source contacts through the aperture into the n-type base region and are collected at the drain. A Schottky gate, located directly above the aperture, is used to modulate the current passing through the aperture. In a CAVET, because the virtual drain (or pinched off region) is located underneath the gate, charge does not accumulate at the gate edge, so no large fields near the gate edge are present. Instead, our simulations show that the high field region in a CAVET is buried in the bulk. The CAVET therefore has the potential to support large source-drain voltages, since surface related breakdown is eliminated.

End-to-End Channel Capacity of a Wireless Sensor Network Under Reachback <=SEP=> Many wireless sensor network applications will impose a many-to-one traffic flow pattern. This phenomena in communication networks is referred to as reachback. The higher volume of traffic in traffic hotspots, which usually form around the base station, limits the number of sensors that can participate in the formation of a wireless sensor network with a fixed per sensor data rate. To alleviate the effects of reachback, spatial correlation of sensor data has enabled the use of Slepian-Wolf coding as a compression technique to reduce the volume of traffic in sensor networks. Within a cluster, Slepian-Wolf coding is used to compress the sensor readings. Clusterheads then transmit the compressed sensor readings over the overlay network which is formed by all clusterheads. We develop an expression for the end-to-end (sensor-to-base station) channel capacity of such a wireless sensor network and observe the effect of variations in the number of clusterheads on end-to-end capacity.

Building Enterprise Systems Infrastructure Flexibility as Enabler of Organisational Agility: Empirical Evidence <=SEP=> Enterprise systems (ES) that capture the most advanced developments of information technology are becoming common fixtures in most organisations. However, how ES affect organizational agility (OA) has been less researched and the existing research remains equivocal. From the perspective that ES can positively contribute to OA, this research via theory-based model development and rigorous empirical investigation of the proposed model, has bridged significant research gaps and provided empirical evidence for, and insights into, the effect of ES on OA. The empirical results based on data collected from 179 large organizations in Australia and New Zealand which have implemented and used ES for at least one year show that organisations can achieve agility out of their ES in two ways: by developing ES technical competences to build ES-enabled capabilities that digitise their key sensing and responding processes; and when ES-enabled sensing and responding capabilities are aligned in relatively turbulent environment.

Central Lip Lift as Aesthetic and Physiognomic Plastic Surgery: The Effect on Lower Facial Profile. <=SEP=> BACKGROUND
A central lip lift was introduced to Westerners in 1980s. However, no studies have been conducted on the facial aesthetic and physiognomic perspectives of a central lip lift in the Asian population.


OBJECTIVES
The authors presented the central lip lift as aesthetic and physiognomic treatment in Asians and explained its effect on lower facial profile.


METHODS
A retrospective chart review was performed in 202 cases of asians. The authors analyzed patient age, cause of long philtrum, purpose of the treatment, and postoperative satisfaction. The authors then performed an anthropometric assessment and a photographic analysis.


RESULTS
The vertical disproportion of the lower face was improved after the treatment, and there was significant shortening of the philtrum length (P < .001) and an increase in a visible upper vermilion (P < .001). In Westerners, a long philtrum was mainly caused by the aging process. Aging patients (range, 40-59 years) underwent the central lip lift for upper lip rejuvenation. In contrast, in Asia, a long philtrum was primarily caused by bone retraction after an orthognathic surgery or orthodontic procedure. Young patients (range, 20-39 years old) underwent the central lip lift to correct a relatively lengthened philtrum after 2-jaw surgery. Furthermore, about half of the patients (52.0%) underwent the central lip lift for facial physiognomic improvement.


CONCLUSIONS
In today's multiracial society, plastic surgeons planning a central lip lift in Asian patients should consider both aesthetic and physiognomic perspectives. Regardless of the aesthetic outcome, the surgeon should strive to maximize patient satisfaction.


LEVEL OF EVIDENCE
4 Therapeutic.

A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration <=SEP=> We generalize the alternating minimization algorithm recently proposed in [32] to efficiently solve a general, edge-preserving, variational model for recovering multichannel images degraded by withinand cross-channel blurs, as well as additive Gaussian noise. This general model allows the use of localized weights and higher-order derivatives in regularization, and includes a multichannel extension of total variation (MTV) regularization as a special case. In the MTV case, we show that the model can be derived from an extended half-quadratic transform of Geman and Yang [14]. For color images with three channels and when applied to the MTV model (either locally weighted or not), the per-iteration computational complexity of this algorithm is dominated by nine fast Fourier transforms. We establish strong convergence results for the algorithm including finite convergence for some variables and fast q-linear convergence for the others. Numerical results on various types of blurs are presented to demonstrate the performance of our algorithm compared to that of the MATLAB deblurring functions. We also present experimental results on regularization models using weighted MTV and higher-order derivatives to demonstrate improvements in image quality provided by these models over the plain MTV model.

Hyperbolic centroidal Voronoi tessellation <=SEP=> The centroidal Voronoi tessellation (CVT) has found versatile applications in geometric modeling, computer graphics, and visualization. In this paper, we extend the concept of the CVT from Euclidean space to hyperbolic space. A novel hyperbolic CVT energy is defined, and the relationship between minimizing this energy and the hyperbolic CVT is proved. We also show by our experimental results that the hyperbolic CVT has the similar property as its Euclidean counterpart where the sites are uniformly distributed according to given density values. Two algorithms -- Lloyd's algorithm and the L-BFGS algorithm -- are adopted to compute the hyperbolic CVT, and the convergence of Lloyd's algorithm is proved. As an example of the application, we utilize the hyperbolic CVT to compute uniform partitions and high-quality remeshing results for high-genus (genus>1) surfaces.

Bayes Optimality in Linear Discriminant Analysis <=SEP=> We present an algorithm that provides the one-dimensional subspace, where the Bayes error is minimized for the C class problem with homoscedastic Gaussian distributions. Our main result shows that the set of possible one-dimensional spaces v, for which the order of the projected class means is identical, defines a convex region with associated convex Bayes error function g(v). This allows for the minimization of the error function using standard convex optimization algorithms. Our algorithm is then extended to the minimization of the Bayes error in the more general case of heteroscedastic distributions. This is done by means of an appropriate kernel mapping function. This result is further extended to obtain the d dimensional solution for any given d by iteratively applying our algorithm to the null space of the (d - l)-dimensional solution. We also show how this result can be used to improve upon the outcomes provided by existing algorithms and derive a low-computational cost, linear approximation. Extensive experimental validations are provided to demonstrate the use of these algorithms in classification, data analysis and visualization.

Emotional modulation of cognitive control: approach-withdrawal states double-dissociate spatial from verbal two-back task performance. <=SEP=> Emotional states might selectively modulate components of cognitive control. To test this hypothesis, the author randomly assigned 152 undergraduates (equal numbers of men and women) to watch short videos intended to induce emotional states (approach, neutral, or withdrawal). Each video was followed by a computerized 2-back working memory task (spatial or verbal, equated for difficulty and appearance). Spatial 2-back performance was enhanced by a withdrawal state and impaired by an approach state; the opposite pattern held for verbal performance. The double dissociation held more strongly for participants who made more errors than average across conditions. The results suggest that approach-withdrawal states can have selective influences on components of cognitive control, possibly on a hemispheric basis. They support and extend several frameworks for conceptualizing emotion-cognition interactions.