Query: SGDR: Stochastic Gradient Descent with Restarts

Relevant passages:
Identity Mappings in Deep Residual Networks <=SEP=> Deep residual networks [1] have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the forward and backward signals can be directly propagated from one block to any other block, when using identity mappings as the skip connections and after-addition activation. A series of ablation experiments support the importance of these identity mappings. This motivates us to propose a new residual unit, which makes training easier and improves generalization. We report improved results using a 1001-layer ResNet on CIFAR-10 (4.62% error) and CIFAR-100, and a 200-layer ResNet on ImageNet. Code is available at: https://github.com/KaimingHe/ resnet-1k-layers.

Adam: A Method for Stochastic Optimization <=SEP=> We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition <=SEP=> We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization <=SEP=> We present a new family of subgradient methods that dynamica lly incorporate knowledge of the geometry of the data observed in earlier iterations to perfo rm more informative gradient-based learning. Metaphorically, the adaptation allows us to find n eedles in haystacks in the form of very predictive but rarely seen features. Our paradigm stems fro m recent advances in stochastic optimization and online learning which employ proximal funct ions to control the gradient steps of the algorithm. We describe and analyze an apparatus for adap tively modifying the proximal function, which significantly simplifies setting a learning rate nd results in regret guarantees that are provably as good as the best proximal function that can be cho sen in hindsight. We give several efficient algorithms for empirical risk minimization probl ems with common and important regularization functions and domain constraints. We experimen tally study our theoretical analysis and show that adaptive subgradient methods outperform state-o f-the-art, yet non-adaptive, subgradient algorithms.

Visualizing Data using t-SNE <=SEP=> We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets.

The impact of staff turnover on software projects: the importance of understanding what makes software practitioners tick <=SEP=> In this paper we investigate the impact of staff turnover on software projects. In particular we investigate whether high staff turnover damages project success. We analyse data from an empirical study of 89 software practitioners to show that projects with high staff turnover are less successful. Furthermore our empirical data suggests a relationship between high staff turnover on projects and low staff motivation levels. We discuss factors which have been previously found to improve motivation levels and conclude that improving motivation levels can reduce staff turnover, which in turn increases project success.

Fast Global Registration <=SEP=> We present an algorithm for fast global registration of partially overlapping 3D surfaces. The algorithm operates on candidate matches that cover the surfaces. A single objective is optimized to align the surfaces and disable false matches. The objective is defined densely over the surfaces and the optimization achieves tight alignment with no initialization. No correspondence updates or closest-point queries are performed in the inner loop. An extension of the algorithm can perform joint global registration of many partially overlapping surfaces. Extensive experiments demonstrate that the presented approach matches or exceeds the accuracy of state-of-the-art global registration pipelines, while being at least an order of magnitude faster. Remarkably, the presented approach is also faster than local refinement algorithms such as ICP. It provides the accuracy achieved by well-initialized local refinement algorithms, without requiring an initialization and at lower computational cost.

Hate Source : White Supremacist Hate Groups and Hate Crime <=SEP=> The relationship between hate group activity and hate crime is theoretically ambiguous. Hate groups may incite criminal behavior in support of their beliefs. On the other hand, hate groups may reduce hate crime by serving as a forum for members to verbally vent their frustrations or as protection from future biased violence. I nd that the presence of an active white supremacist hate group chapter is associated with an 18.7 percent higher hate crime rate. White supremacist groups are not associated with the level of anti-white hate crimes committed by non-whites, nor do they form in expectation of future hate crimes by non-whites. JEL Codes: K14, J15, D71

Surfel-Based Next Best View Planning <=SEP=> Next best view (NBV) planning is a central task for automated three-dimensional (3-D) reconstruction in robotics. The most expensive phase of NBV computation is the view simulation step, where the information gain of a large number of candidate sensor poses are estimated. Usually, information gain is related to the visibility of unknown space from the simulated viewpoint. A well-established technique is to adopt a volumetric representation of the environment and to compute the NBV from ray casting by maximizing the number of unknown visible voxels. This letter explores a novel approach for NBV planning based on surfel representation of the environment. Surfels are oriented surface elements, such as circular disks, without explicit connectivity. A new kind of surfel is introduced to represent the frontier between empty and unknown space. Surfels are extracted during 3-D reconstruction, with minimal overhead, from a KinectFusion volumetric representation. Surfel rendering is used to generate images from each simulated sensor pose. Experiments in a real robot setup are reported. The proposed approach achieves better performance than volumetric algorithms based on ray casting implemented on graphics processing unit (GPU), with comparable results in terms of reconstruction quality. Moreover, surfel-based NBV planning can be applied in larger environments as a volumetric representation is limited by GPU memory.

Green Internet of Things for Smart World <=SEP=> Smart world is envisioned as an era in which objects (e.g., watches, mobile phones, computers, cars, buses, and trains) can automatically and intelligently serve people in a collaborative manner. Paving the way for smart world, Internet of Things (IoT) connects everything in the smart world. Motivated by achieving a sustainable smart world, this paper discusses various technologies and issues regarding green IoT, which further reduces the energy consumption of IoT. Particularly, an overview regarding IoT and green IoT is performed first. Then, the hot green information and communications technologies (ICTs) (e.g., green radio-frequency identification, green wireless sensor network, green cloud computing, green machine to machine, and green data center) enabling green IoT are studied, and general green ICT principles are summarized. Furthermore, the latest developments and future vision about sensor cloud, which is a novel paradigm in green IoT, are reviewed and introduced, respectively. Finally, future research directions and open problems about green IoT are presented. Our work targets to be an enlightening and latest guidance for research with respect to green IoT and smart world.

Learning patterns for discovering domain-oriented opinion words <=SEP=> Sentiment analysis is a challenging task that attracted increasing interest during the last years. The availability of online data along with the business interest to keep up with consumer feedback generates a constant demand for online analysis of user-generated content. A key role to this task plays the utilization of domain-specific lexicons of opinion words that enables algorithms to classify short snippets of text into sentiment classes (positive, negative). This process is known as dictionary-based sentiment analysis. The related work tends to solve this lexicon identification problem by either exploiting a corpus and a thesaurus or by manually defining a set of patterns that will extract opinion words. In this work, we propose an unsupervised approach for discovering patterns that will extract domain-specific dictionary. Our approach (DidaxTo) utilizes opinion modifiers, sentiment consistency theories, polarity assignment graphs and pattern similarity metrics. The outcome is compared against lexicons extracted by the state-of-the-art approaches on a sentiment analysis task. Experiments on user reviews coming from a diverse set of products demonstrate the utility of the proposed method. An implementation of the proposed approach in an easy to use application for extracting opinion words from any domain and evaluate their quality is also presented.

Relativity of remembering: why the laws of memory vanished. <=SEP=> For 120 years, cognitive psychologists have sought general laws of learning and memory. In this review I conclude that none has stood the test of time. No empirical law withstands manipulation across the four sets of factors that Jenkins (1979) identified as critical to memory experiments: types of subjects, kinds of events to be remembered, manipulation of encoding conditions, and variations in test conditions. Another factor affecting many phenomena is whether a manipulation of conditions occurs in randomized, within-subjects designs rather than between-subjects (or within-subject, blocked) designs. The fact that simple laws do not hold reveals the complex, interactive nature of memory phenomena. Nonetheless, the science of memory is robust, with most findings easily replicated under the same conditions as originally used, but when other variables are manipulated, effects may disappear or reverse. These same points are probably true of psychological research in most, if not all, domains.

Decision quality and satisfaction: the effects of online information sources and self-efficacy <=SEP=> Purpose – Digital libraries and social media are two sources of online information with different characteristics. This study integrates self-efficacy into the analysis of the relationship between information sources and decision-making, with the aim of exploring the effect of self-efficacy on decision-making, as well as the interacting effect of self-efficacy and information sources on decision-making. Design/methodology/approach –Survey data were collected and the partial least squares (PLS) structural equation modelling (SEM) was employed to verify the research model. Findings – The effect of digital library usage for acquiring information on perceived decision quality is larger than that of social media usage for acquiring information on perceived decision quality. Self-efficacy in acquiring information stands out as the key determinant for perceived decision quality. The effect of social media usage for acquiring information on perceived decision quality is positively moderated by self-efficacy in acquiring information. Practical implications – Decision-making is a fundamental activity for individuals, but human decision-making is often subject to biases. The findings of this study provide useful insights into decision quality improvement, highlighting the importance of self-efficacy in acquiring information in the face of information overload. Originality/value – This study integrates self-efficacy into the analysis of the relationship between information sources and decision-making, presenting a new perspective for decision-making research and practice alike.

BTS guidelines for the insertion of a chest drain. <=SEP=> D Laws, E Neville, J Duffy, on behalf of the British Thoracic Society Pleural Disease Group, a subgroup of the British Thoracic Society Standards of Care Committee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

LOCOMO satcom terminal: A switchable RHCP/LHCP Array Antenna for on-the-move applications in Ka-band <=SEP=> This paper describes the main features of a multilayer antenna panel for a low cost Ka-band Array Antenna for on-the-move applications. The LOCOMO satcom terminal is based on a dual-polarized low-profile antenna with trasmit/receive and RHCP/LHCP switching capability.

Multi-labeled Relation Extraction with Attentive Capsule Network <=SEP=> To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence. To better cluster the features and precisely extract the relations, we further devise attention-based routing algorithm and sliding-margin loss function, and embed them into our capsule network. The experimental results show that the proposed approach can indeed extract the highly overlapped features and achieve significant performance improvement for relation extraction comparing to the state-of-the-art works. Introduction Relation extraction plays a crucial role in many natural language processing (NLP) tasks. It aims to identify relation facts for pairs of entities in a sentence to construct triples like [Arthur Lee, place born, Memphis]. Relation extraction has received renewed interest in the neural network era, when neural models are effective to extract semantic meanings of relations. Compared with traditional approaches which focus on manually designed features, neural methods such as Convolutional Neural Network (CNN) (Liu et al. 2013; Zeng et al. 2014) and Recurrent Neural Network (RNN) (Zhang and Wang 2015; Zhou et al. 2016) have achieved significant improvement in relation classification. However, previous neural models are unlikely to scale in the scenario where a sentence has multiple relation labels and face the challenges in extracting highly overlapped and discrete relation features due to the following two drawbacks. First, one entity pair can express multiple relations in a sentence, which will confuse relation extractor seriously. For example, as in Figure 1, the entity pair [Arthur Lee, Memphis] keeps three possible relations which are place birth, ∗Corresponding authors: Weijia Jia, Hai Zhao, {jia-wj, zhaohai}@cs.sjtu.edu.cn Copyright c © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. place death and place lived. The sentence S1 and S2 can both express two relations, and the sentence S3 represents another two relations. These sentences contain multiple kinds of relation features which are difficult to be identified clearly. The existing neural models tendentiously merge low-level semantic meanings to one high-level relation representation vector with methods such as max-pooling (Zeng et al. 2014; Zhang, Zhao, and Qin 2016) and word-level attention (Zhou et al. 2016). However, one high-level relation vector is still insufficient to express multiple relations precisely. Second, current methods are neglecting of the discretization of relation features. For instance, as shown in Figure 1, all the sentences express their relations with a few significant words (labeled italic in the figure) distributed discretely in the sentences. However, common neural methods handle sentences with fixed structures, which are difficult to gather relation features of different positions. For example, being spatially sensitive, CNNs adopt convolutional feature detectors to extract local patterns from a sliding window of vector sequences and use the max-pooling to select the prominent ones. Besides, the feature distribution of “no relation (NA, others)” in a dataset is different from that of definite relations. A sentence can be classified to “no relation” only when it does not contain any features of other relations. In this paper, to extract overlapped and discrete relation features, we propose a novel approach for multi-labeled relation extraction with an attentive capsule network. As shown in Figure 1, the relation extractor of the proposed method is constructed with three major layers that are feature extracting, feature clustering and relation predicting. The first one extracts low-level semantic meanings. The second layer clusters low-level features to high-level relation representations, and the final one predicts relation types for each relation representation. The low-level features are extracted with traditional neural models such as Bidirectional Long ShortTerm Memory (Bi-LSTM) and CNN. For the feature clustering layer, we utilize an attentive capsule network inspired by Sabour, Frosst, and Hinton (2017). Capsule (vector) is a small group of neurons used to express features. Its overall length indicates the significance of features, and the direction of a capsule suggests the specific property of the feature. The low-level semantic meanings from the first layer are embedded to amounts of low-level capsules, which will ar X iv :1 81 1. 04 35 4v 1 [ cs .C L ] 1 1 N ov 2 01 8 ID Instances Relations S1 [Arthur Lee], the leader of Love, died on Thursday in [Memphis]. person/place_death

A design for an electronically-steerable holographic antenna with polarization control <=SEP=> We present a design for an electronic-steerable holographic antenna with polarization control composed of a radial array of Ku-band, electronically-steerable, surface-wave waveguide (SWG) artificial-impedance-surface antennas (AISA). The antenna operates by launching surface waves into each of the SWGs via a central feed network. The surface-wave impedance is electronically controlled with varactor-tuned impedance patches. The impedance is adjusted to scan the antenna in elevation, azimuth and polarization. The radial symmetry allows for 360° azimuthal steering. If constructed with previously-demonstrated SWG AISAs, it is capable of scanning in elevation from -75° to 75° with gain variation of less than 3 dB. The polarization can be switched among V-Pol, H-Pol, LHCP and RHCP at will.

Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics <=SEP=> As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics paradigm; design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics paradigm by clinical researchers analyzing electronic medical records.

ℓ<ce:inf loc=post>1</ce:inf>-penalized linear mixed-effects models for high dimensional data with application to BCI <=SEP=> Recently, a novel statistical model has been proposed to estimate population effects and individual variability between subgroups simultaneously, by extending Lasso methods. We will for the first time apply this so-called ℓ(1)-penalized linear regression mixed-effects model for a large scale real world problem: we study a large set of brain computer interface data and through the novel estimator are able to obtain a subject-independent classifier that compares favorably with prior zero-training algorithms. This unifying model inherently compensates shifts in the input space attributed to the individuality of a subject. In particular we are now for the first time able to differentiate within-subject and between-subject variability. Thus a deeper understanding both of the underlying statistical and physiological structures of the data is gained.

BotCop: An Online Botnet Traffic Classifier <=SEP=> A botnet is a network of compromised computers infected with malicious code that can be controlled remotely under a common command and control (C&C) channel. As one the most serious security threats to the Internet, a botnet cannot only be implemented with existing network applications (e.g. IRC, HTTP, or Peer-to-Peer) but also can be constructed by unknown or creative applications, thus making the botnet detection a challenging problem. In this paper, we propose a new online botnet traffic classification system, called BotCop, in which the network traffic are fully classified into different application communities by using payload signatures and a novel decision tree model, and then on each obtained application community, the temporal-frequent characteristic of flows is studied and analyzed to differentiate the malicious communication traffic created by bots from normal traffic generated by human beings. We evaluate our approach with about 30 million flows collected over one day on a large-scale WiFi ISP network and results show that the proposed approach successfully detects an IRC botnet from about 30 million flows with a high detection rate and a low false alarm rate.

On the compression of recurrent neural networks with an application to LVCSR acoustic modeling for embedded speech recognition <=SEP=> We study the problem of compressing recurrent neural networks (RNNs). In particular, we focus on the compression of RNN acoustic models, which are motivated by the goal of building compact and accurate speech recognition systems which can be run efficiently on mobile devices. In this work, we present a technique for general recurrent model compression that jointly compresses both recurrent and non-recurrent inter-layer weight matrices. We find that the proposed technique allows us to reduce the size of our Long Short-Term Memory (LSTM) acoustic model to a third of its original size with negligible loss in accuracy.

Efficient Object Instance Search Using Fuzzy Objects Matching <=SEP=> Recently, global features aggregated from local convolutional features of the convolutional neural network have shown to be much more effective in comparison with hand-crafted features for image retrieval. However, the global feature might not effectively capture the relevance between the query object and reference images in the object instance search task, especially when the query object is relatively small and there exist multiple types of objects in reference images. Moreover, the object instance search requires to localize the object in the reference image, which may not be achieved through global representations. In this paper, we propose a Fuzzy Objects Matching (FOM) framework to effectively and efficiently capture the relevance between the query object and reference images in the dataset. In the proposed FOM scheme, object proposals are utilized to detect the potential regions of the query object in reference images. To achieve high search efficiency, we factorize the feature matrix of all the object proposals from one reference image into the product of a set of fuzzy objects and sparse codes. In addition, we refine the feature of the generated fuzzy objects according to its neighborhood in the feature space to generate more robust representation. The experimental results demonstrate that the proposed FOM framework significantly outperforms the state-of-theart methods in precision with less memory and computational cost on three public datasets. The task of object instance search, is to retrieve all the images containing a specific object query and localize the query object in the reference images. It has received a sustained attention over the last decade, leading to many object instance search systems (Meng et al. 2010; Jiang, Meng, and Yuan 2012; Jiang et al. 2015; Tolias, Avrithis, and Jégou 2013; Tao et al. 2014; Razavian et al. 2014a; 2014b; Tolias, Sicre, and Jégou 2016; Meng et al. 2016; Bhattacharjee et al. 2016b; 2016a; Mohedano et al. 2016; Cao et al. 2016; Wu et al. 2016). Since the query object only occupies a small part of an image, the global representation may not be effective to capture the relevance between the query object with reference image. Therefore, the relevance between the query object and one reference image is not determined by the overall similarity between the query and the reference image. Copyright c © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. ...

Parsing English into Abstract Meaning Representation Using Syntax-Based Machine Translation <=SEP=> We present a parser for Abstract Meaning Representation (AMR). We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser significantly improves upon state-of-the-art results.

An image encryption scheme based on elliptic curve pseudo random and Advanced Encryption System <=SEP=> Elliptic curve cryptography (ECC) has proven to be an effective cryptography. ECC has its own advantages such as efficient key size compared to other public key infrastructures. This paper exploits the Elliptic curve random generator defined by National Institute of Standards and Technology (NIST) to generate a sequence of arbitrary numbers based on curves. The random generation phase is based on public shared key and a changing point G, which is a generator of a curve to obtain random sequences. Then, Advanced Encryption System is applied to these sequences acquiring arbitrary keys for encrypting image. Using AES alongside well distributed randoms provides a prominent encryption technique. Our experiments show that the proposed method fulfills the basics of cryptography including simpleness and correctness. Moreover, the results of the evaluation prove the effectiveness and security of the proposed method.

Black Box Testing: Techniques for Functional Testing of Software and Systems <=SEP=> Editor: Peter Anderson Rochester Institute of Technology 1 Lomb Memorial Dr. Rochester, NY 14623 pga@cs.rit.edu Each technique receives similar coverage, including a description of its new and prerequisite vocabulary, its model, the bug assumptlons that underlie its effectiveness, typical applications, examples (almost all based on standard IRS tax forms), caveats and limitations, automation and tools for the technique, a self-evaluation quiz, and exercises. Common threads in the presentation show the role

Introduction to Neurolinguistics <=SEP=> attitude is lacking in patients with anomia (inability to name), since they cannot categorize, and in patients with agrammatism (difficulties using grammatical morphemes and function words), since they cannot use elements that “have no meaning in isolation.” In his description of abstract attitude, Goldstein mixes psychological factors, such as the ability to separate figure and ground, with physiological factors, like spreading and activation (i.e., the fact that stimuli can activate an abnormally large area in the cortex if thresholds of activation for neurons are changed by brain damage, which makes the patient unable to detect, for example, exactly where somebody is touching his skin). An abstract attitude is required for the fully efficient use of language and therefore the linguistic disorder arises secondarily. Words lose their function to organize the external world. Goldstein assumed different positions with respect to localization, but he was mainly an anti-localist. He claimed that the cortex could be separated into a “peripheral part” with structural localization and a “central part” which is equipotential and constructs “dynamic structures” against a “dynamic background.” Lesions in the central part cause a loss of abstract attitude. It was Karl Lashley (1933, 1950), a physiologist, who presented the theory of cerebral equipotentiality and mass effect. Equipotentiality means that all parts of the cortex have the same “ability” and mass effect means that functions are dependent on how much of the cortex is used. Lashley claimed that studies of humans were too unreliable, due to humans’ complex behavior, and therefore concentrated on experiments with rats in mazes. He accepted specified functions only for motor and sensory projection areas and objected to attempts to localize mental functions. He emphasized that psychological unity does not guarantee neurological unity; for example, many structures participate in intentional movements. Lashley tried to identify different integrative mechanisms, such as intensity, spatial orientation and serial ordering. Inspired by Marie, Bay (1962) suggested that speech is dependent on a large number of “extralinguistic” factors, which are disturbed by brain damage. In aphasia, the transmission of information is disturbed. This disorder always involves a reduced intellect. The aphasic patient’s concepts become pathologically distorted, which can be seen in tasks like drawing or sculpting. The same defect is noticed in speech: concepts become fuzzy, deformed, and poorly differentiated. Jason Brown’s theory of aphasia is inspired by Jackson, von Monakow, and Freud. It contains, among other things, evolutionary aspects and a regression hypothesis combined with a functional view of aphasia. He raises the connections between emotions, degree of consciousness and intentionality, creativity, and language. Brown’s theory is presented in several books, including Mind, brain and consciousness from 1977. (See also Chapter 10.) Chapter 2. The development of theories about brain and language 27 Localism and associationism Localism continued to survive but it did not attract much attention during the first half of the 20th century. Some examples are mentioned below. Swedish neurologist Salomon Henschen (1926) formulated a detailed model on the basis of all the clinical pathological cases of aphasia that had been described to that point. The model contained a large number of separate centers for different aspects of language function. Korbinian Brodmann (1909), another neurologist, divided the cortex into numerous histologically defined areas, each of which had a determined physiological function. His numbering system is still used to give exact references to areas of the brain (see Figure 14.6 in Chapter 14). Karl Kleist (1916) investigated war injuries from the First World War and tried to find a psychological explanation for each cytoand myeloarchitectonic (related to cellular patterns and myelinated nerve cell patterns, respectively) area. Speech and language were localized according to tradition. Johannes Nielsen (1947) devoted himself to the anatomical-physiological classification of cortical functions. He distinguished between receptive and afferent signals, and between primary identification through one sense and secondary identification via associations of different senses at a higher level. According to Nielsen, recognition means receiving impressions and comparing them to engrams (physical alterations of nerve tissue in response to stimuli). Remembering, on the other hand, is starting from the concept and identifying it with an engram, which takes place at a higher level. Two types of aphasia can then occur: (a) a type that changes the connection between language and recognition, and (b) a type that changes the “signification” (what Head called “semantic aphasia”). The latter is not localized in specific areas; rather, any lesion can affect the whole system. The destruction of a projection area causes a total loss of function, while the destruction of an adjacent zone causes agnosia (inability to recognize) and the destruction of association zones that are not localized results in memory disorders. Thus, this theory is strongly inspired by associationism. Associationism is rediscovered by Geschwind Associationism continues to be very influential today. It was Norman Geschwind who rediscovered associationism and made it known as “connectionism,” or the neoclassical school of neurolinguistics, in his 1965 book Disconnection syndromes in animals and man. (For more information, see Chapter 3.) Introduction to Neurolinguistics 28 Dynamic localization of function: Pavlov, Vygotsky, and Luria Dynamic localization of function is a tradition originating in Russia which developed during the 20th century in parallel with the other traditions. Today, it also has considerable influence in Western Europe and the U.S. The well-known physiologist Ivan Pavlov (1949) claimed that complex behavior could not be handled by isolated and fixed brain structures, but must emerge during the ontogenetic development of dynamic systems. These systems were said to consist of connected structures in different places within the nervous system. Psychologist Lev Vygotsky emphasized that it is necessary to first investigate what is to be localized, before asking the question where. Functions must be analyzed with respect to ontogenetic development. Vygotsky (1970) saw a function as a complex activity whereby the whole organism adapts to a task. The activity can be performed in different ways, because of the cooperation of several organs. This dynamic cooperation is controlled by neural structures that monitor different organs and are localized in different places. Vygotsky also formulated a theory about the development of language and thinking that became very influential. The noted psychologist and aphasiologist Alexander Luria studied aphasia within the framework of the theory of dynamic localization of function. Luria’s work has been extremely influential in aphasia research, and especially in clinical work. (It will be further discussed in Chapter 3.) The test psychological tradition Theodore Weisenburg and Katherine McBride investigated cognitive factors in persons with aphasia, persons with right-hemisphere lesions, and control subjects, using a large test battery of verbal and nonverbal tasks. Their methodology became the model for a large number of studies. The results showed that persons with aphasia varied a great deal cognitively. In general, aphasia was associated with a cognitive disorder, but there was no necessary correlation. Certain persons with severe aphasia had excellent results on nonverbal tests. Thus, there was no support for Head’s theories. Weisenburg and McBride claimed that the importance of language for thinking must differ in different individuals and that the intellect before the lesion must be determinant. Many psychologists were inspired by Weisenburg and McBride and by Goldstein and developed experiments for testing “symbolic thinking” in aphasics. Subjects with aphasia performed classification tasks, Raven’s matrices (analogy tests), and similar tests. The results most often showed considerable variability, and the general conclusion was that the cognitive disorder, where such a disorder existed, could not be attributed to the linguistic disorder. Nevertheless, certain types of cognitive disorders were often found in persons with aphasia, such as difficulties in discriminating faces and Chapter 2. The development of theories about brain and language 29 interpreting mimed actions, as well as construction apraxia (i.e., problems performing spatial construction tasks). Linguists and the linguistic influence on aphasiology Aphasia research was traditionally an area for neurologists, but philosophers and psychologists have also had considerable influence on the area. In the middle of the 20th century, linguists joined this group. They saw studies of aphasia as a means of testing general theories about language. By functionally analyzing different disorders using the descriptive methods of linguistics, they aimed to gain better understanding of the role of the brain in normal speech. The term neurolinguistics was eventually adopted for these studies. In 1941, Roman Jakobson published the book Kindersprache, Aphasie und allgemeine Lautgesetze (“Child language, aphasia and phonological universals”), in which he pointed out parallels between linguistic universals, children’s language development, and symptoms of aphasia. At this point, he was the only linguist to systematically describe aphasia within the structuralist framework. Jakobson applied Ferdinand de Saussure’s principle of paradigmatic and syntagmatic relations in language. If language consisted of two types of operations — choice of units and combination of units — then two types of aphasia should be possible: (a) a

Análisis de la Simplificación de Expresiones Numéricas en Español mediante un Estudio Empírico <=SEP=> In this paper we present the results of an empirical study carried out on a parallel corpus of original and manually simplified texts in Spanish and a subsequent survey, with the aim of targeting simplification operations concerning numerical expressions. For the purpose of the study, a “numerical expression” is understood as any phrase expressing quantity possibly modified with a numerical hedge, such as almost a quarter. Data is analyzed both in context and in isolation, and attention is paid to the difference the target reader makes to simplification. Our future work aims at computational implementation of the transformation rules extracted so far.

Sistema de Conversión Texto a Voz de Código Abierto Para Lenguas Ibéricas <=SEP=> This paper presents a text-to-speech system based on statistical synthesis which, for the first time, allows generating speech in any of the four official languages of Spain as well as English. Using the AhoTTS system already developed for Spanish and Basque as a starting point, we have added support for Catalan, Galician and English using the code of available open-source modules. The resulting system, named multilingual AhoTTS, has also been released as open-source and it is already being used in real applications.

The RB and p 53 pathways in cancer <=SEP=> The life history of cancer cells encompasses a series of genetic missteps in which normal cells are progressively transformed into tumor cells that invade surrounding tissues and become malignant. Most prominent among the regulators disrupted in cancer cells are two tumor suppressors, the retinoblastoma protein (RB) and the p53 transcription factor. Here, we discuss interconnecting signaling pathways controlled by RB and p53, attempting to explain their potentially universal involvement in the etiology of cancer. Pinpointing the various ways by which the functions of RB and p53 are subverted in individual tumors should provide a rational basis for developing more refined tumor-specific therapies.

Creativity, the Turing Test, and the (Better) Lovelace Test <=SEP=> The Turing Test (TT) is claimed by many to be a way to test for the presence, in computers, of such ``deep'' phenomena as thought and consciousness. Unfortunately, attempts to build computational systems able to pass TT (or at least restricted versions of this test) have devolved into shallow symbol manipulation designed to, by hook or by crook, trick. The human creators of such systems know all too well that they have merely tried to fool those people who interact with their systems into believing that these systems really have minds. And the problem is fundamental: the structure of the TT is such as to cultivate tricksters. A better test is one that insists on a certain restrictive epistemic relation between an artificial agent (or system) A, its output o, and the human architect H of A – a relation which, roughly speaking, obtains when H cannot account for how A produced o. We call this test the ``Lovelace Test'' in honor of Lady Lovelace, who believed that only when computers originate things should they be believed to have minds.