Query: Effective Inference for Generative Neural Parsing

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Bayesian Symbol-Refined Tree Substitution Grammars for Syntactic Parsing <=SEP=> We propose Symbol-Refined Tree Substitution Grammars (SR-TSGs) for syntactic parsing. An SR-TSG is an extension of the conventional TSG model where each nonterminal symbol can be refined (subcategorized) to fit the training data. We aim to provide a unified model where TSG rules and symbol refinement are learned from training data in a fully automatic and consistent fashion. We present a novel probabilistic SR-TSG model based on the hierarchical Pitman-Yor Process to encode backoff smoothing from a fine-grained SR-TSG to simpler CFG rules, and develop an efficient training method based on Markov Chain Monte Carlo (MCMC) sampling. Our SR-TSG parser achieves an F1 score of 92.4% in the Wall Street Journal (WSJ) English Penn Treebank parsing task, which is a 7.7 point improvement over a conventional Bayesian TSG parser, and better than state-of-the-art discriminative reranking parsers.

Parsing as Language Modeling <=SEP=> We recast syntactic parsing as a language modeling problem and use recent advances in neural network language modeling to achieve a new state of the art for constituency Penn Treebank parsing — 93.8 F1 on section 23, using 2-21 as training, 24 as development, plus tri-training. When trees are converted to Stanford dependencies, UAS and LAS are 95.9% and 94.1%.

Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles <=SEP=> Parsing accuracy using efficient greedy transition systems has improved dramatically in recent years thanks to neural networks. Despite striking results in dependency parsing, however, neural models have not surpassed stateof-the-art approaches in constituency parsing. To remedy this, we introduce a new shiftreduce system whose stack contains merely sentence spans, represented by a bare minimum of LSTM features. We also design the first provably optimal dynamic oracle for constituency parsing, which runs in amortized O(1) time, compared to O(n) oracles for standard dependency parsing. Training with this oracle, we achieve the best F1 scores on both English and French of any parser that does not use reranking or external data.

Character-Aware Neural Language Models <=SEP=> We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long shortterm memory (LSTM) recurrent neural network language model (RNN-LM). On the English Penn Treebank the model is on par with the existing state-ofthe-art despite having 60% fewer parameters. On languages with rich morphology (Czech, German, French, Spanish, Russian), the model consistently outperforms a Kneser-Ney baseline and word-level/morpheme-level LSTM baselines, again with far fewer parameters. Our results suggest that on many languages, character inputs are sufficient for language modeling.

A Hierarchical Bayesian Language Model Based On Pitman-Yor Processes <=SEP=> We propose a new hierarchical Bayesian n-gram model of natural languages. Our model makes use of a generalization of the commonly used Dirichlet distributions called Pitman-Yor processes which produce power-law distributions more closely resembling those in natural languages. We show that an approximation to the hierarchical Pitman-Yor language model recovers the exact formulation of interpolated Kneser-Ney, one of the best smoothing methods forn-gram language models. Experiments verify that our model gives cross entropy results superior to interpolated Kneser-Ney and comparable to modified Kneser-Ney.

Modelling language evolution: Examples and predictions. <=SEP=> We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

Workload Patterns for Quality-Driven Dynamic Cloud Service Configuration and Auto-Scaling <=SEP=> Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support an iterative approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction-based technique that combines a pattern matching approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-perform ant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques based on for example exponential smoothing.

Towards a Methodology for Building Ontologies <=SEP=> We outline some requirements for a comprehensive methodology for building ontologies, and review some important work that has been done in the area which could contribute to this goal. We describe our own experiences in constructing a signi cant ontology, emphasising the ontology capture phase. We rst consider the very general issue of categorisation in modelling, and relate it to the process of ontology capture. We then describe the procedure that we used to identify the terms and produce de nitions. We describe a successful way to handle ambiguous terms, which can be an enormous obstacle to reaching a shared understanding. Other important ndings include: it may not be necessary to identify competency questions before building the ontology; the meta-ontology can be chosen after detailed text de nitions are produced; de ning terms which are 'cognitively basic' rst can lead to less re-work. AIAI-TR-183 Page 1 of 13

Practical Byzantine Fault Tolerance <=SEP=> Our growing reliance on online services accessible on the Internet demands highly-available systems that provide correct service without interruptions. Byzantine faults such as software bugs, operator mistakes, and malicious attacks are the major cause of service interruptions. This thesis describes a new replication algorithm, BFT, that can be used to build highly-available systems that tolerate Byzantine faults. It shows, for the first time, how to build Byzantine-fault-tolerant systems that can be used in practice to implement real services because they do not rely on unrealistic assumptions and they perform well. BFT works in asynchronous environments like the Internet, it incorporates mechanisms to defend against Byzantine-faulty clients, and it recovers replicas proactively. The recovery mechanism allows the algorithm to tolerate any number of faults over the lifetime of the system provided fewer than 1/3 of the replicas become faulty within a small window of vulnerability. The window may increase under a denial-of-service attack but the algorithm can detect and respond to such attacks and it can also detect when the state of a replica is corrupted by an attacker. BFT has been implemented as a generic program library with a simple interface. The BFT library provides a complete solution to the problem of building real services that tolerate Byzantine faults. We used the library to implement the first Byzantine-fault-tolerant NFS file system, BFS. The BFT library and BFS perform well because the library incorporates several important optimizations. The most important optimization is the use of symmetric cryptography to authenticate messages. Public-key cryptography, which was the major bottleneck in previous systems, is used only to exchange the symmetric keys. The performance results show that BFS performs 2% faster to 24% slower than production implementations of the NFS protocol that are not replicated. Therefore, we believe that the BFT library can be used to build practical systems that tolerate Byzantine faults. Thesis Supervisor: Barbara H. Liskov Title: Ford Professor of Engineering

The huggable: a therapeutic robotic companion for relational, affective touch <=SEP=> Numerous studies have shown the positive benefits of companion animal therapy. Unfortunately, companion animals are not always available. The Huggable is a new type of robotic companion being designed specifically for such cases. It features a full body sensitive skin for relational affective touch, silent, muscle-like, voice coil actuators, an embedded PC with data collection and networking capabilities. In this paper we briefly describe the Huggable and propose a live demonstration of the robot.

Scap: stream-oriented network traffic capture and analysis for high-speed networks <=SEP=> Many network monitoring applications must analyze traffic beyond the network layer to allow for connection-oriented analysis, and achieve resilience to evasion attempts based on TCP segmentation. However, existing network traffic capture frameworks provide applications with just raw packets, and leave complex operations like flow tracking and TCP stream reassembly to application developers. This gap leads to increased application complexity, longer development time, and most importantly, reduced performance due to excessive data copies between the packet capture subsystem and the stream processing module. This paper presents the Stream capture library (Scap), a network monitoring framework built from the ground up for stream-oriented traffic processing. Based on a kernel module that directly handles flow tracking and TCP stream reassembly, Scap delivers to user-level applications flow-level statistics and reassembled streams by minimizing data movement operations and discarding uninteresting traffic at early stages, while it inherently supports parallel processing on multi-core architectures, and uses advanced capabilities of modern network cards. Our experimental evaluation shows that Scap can capture all streams for traffic rates two times higher than other stream reassembly libraries, and can process more than five times higher traffic loads when eight cores are used for parallel stream processing in a pattern matching application.

LTSA-WS: a tool for model-based verification of web service compositions and choreography <=SEP=> In this paper we describe a tool for a model-based approach to verifying compositions of web service implementations. The tool supports verification of properties created from design specifications and implementation models to confirm expected results from the viewpoints of both the designer and implementer. Scenarios are modeled in UML, in the form of Message Sequence Charts (MSCs), and then compiled into the Finite State Process (FSP) process algebra to concisely model the required behavior. BPEL4WS implementations are mechanically translated to FSP to allow an equivalence trace verification process to be performed. By providing early design verification and validation, the implementation, testing and deployment of web service compositions can be eased through the understanding of the behavior exhibited by the composition. The approach is implemented as a plug-in for the Eclipse development environment providing cooperating tools for specification, formal modeling, verification and validation of the composition process.

Network navigability in the social Internet of Things <=SEP=> The Internet of Things is expected to be overpopulated by a very large number of objects, with intensive interactions, heterogeneous communications and millions of services. Consequently, scalability issues will arise from the search of the right object that can provide the desired service. A new paradigm known as Social Internet of Things has been introduced and proposes the integration of social networking concepts into the Internet of Things. The underneath idea is that every object can look for the desired service using its friendships, in a distributed manner. However, in the resulting network, every object will still have to manage a large number of friends, slowing down the search of the services. In this work, we intend to address this issue by analyzing possible strategies to drive the objects to select the appropriate links for the benefit of overall network navigability.

Affect recognition from face and body: early fusion vs. late fusion <=SEP=> This paper presents an approach to automatic visual emotion recognition from two modalities: face and body. Firstly, individual classifiers are trained from individual modalities. Secondly, we fuse facial expression and affective body gesture information first at a feature-level, in which the data from both modalities are combined before classification, and later at a decision-level, in which we integrate the outputs of the monomodal systems by the use of suitable criteria. We then evaluate these two fusion approaches, in terms of performance over monomodal emotion recognition based on facial expression modality only. In the experiments performed the emotion classification using the two modalities achieved a better recognition accuracy outperforming the classification using the individual facial modality. Moreover, fusion at the feature-level proved better recognition than fusion at the decision-level.

Low Complexity Damped Gauss-Newton Algorithms for CANDECOMP/PARAFAC <=SEP=> The damped Gauss-Newton (dGN) algorithm for CANDECOMP /PARAFAC (CP) decomposition has been successfully applied for di fficult tensor factorization such as collinearity of factors, different magnitudes of factors. Nevertheless, for factorization of an N-D tensor of sizeI1 × . . . × IN with rank R, the algorithm is computationally demanding due to construction of large app roximate Hessian of size (( R ∑ n In) × (R ∑ n In)) and its inversion. In this paper, we propose a fast implementation o f the dGN algorithm which is based on novel expressions of the inverse approximate Hessian in block form. The new imp le entation has a lower computational complexity, which beside computation of the gradient (this part is commo n to both methods) involves inversion of a matrix of the sizeNR2 × NR2, much smaller than the Hessian, if n In ≫ NR. In addition, the implementation has a lower memory requirements, because neither the Hessian nor its in verse need to be stored entire in one time. A variant of the algorithm working with complex valued data is proposed a s well. Complexity and performance of the proposed algorithm is compared with those of dGN and ALS with line sear ch on examples with di fficult benchmark tensors.

Capsule Networks for Hyperspectral Image Classification <=SEP=> Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNNbased network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral–spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral–spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton’s capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral– spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral–spatial capsules, which are able to learn HSI spectral–spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted Manuscript received June 9, 2018; revised August 22, 2018; accepted September 19, 2018. This work was supported in part by the Ministerio de Educación (Resolución de 26 de diciembre de 2014 y de 19 de noviembre de 2015, de la Secretaría de Estado de Educación, Formación Profesional y Universidades, por la que se convocan ayudas para la formación de profesorado universitario, de los subprogramas de Formación y de Movilidad incluidos en el Programa Estatal de Promoción del Talento y su Empleabilidad, and en el marco del Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016), in part by the Junta de Extremadura (decreto 297/2014, ayudas para la realización de actividades de investigación y desarrollo tecnológico, and de divulgación y de transferencia de conocimiento por los Grupos de Investigación de Extremadura) under Grant GR15005, in part by the Generalitat Valenciana under Contract APOSTD/2017/007, in part by the Spanish Ministry of Economy under Project ESP2016-79503-C2-2P and Project TIN2015-63646-C5-5-R, in part by the National Natural Science Foundation of China under Grant 61771496, in part by the National Key Research and Development Program of China under Grant 2017YFB0502900, and in part by the Guangdong Provincial Natural Science Foundation under Grant 2016A030313254. (Corresponding author: Jun Li.) M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza are with the Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, Escuela Politécnica, University of Extremadura, 10003 Cáceres, Spain (e-mail: mpaoletti@unex.es; juanmariohaut@unex.es; jplaza@unex.es; aplaza@unex.es). R. Fernandez-Beltran and F. Pla are with the Institute of New Imaging Technologies, Universitat Jaume I, 12071 Castellón de la Plana, Spain (e-mail: rufernan@uji.es; pla@uji.es). J. Li is with the Guangdong Provincial Key Laboratory of Urbanization and Geosimulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China (e-mail: lijun48@mail.sysu.edu.cn). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2018.2871782 using five well-known HSI data sets and several state-of-theart classification methods, reveal that our HSI classification approach based on spectral–spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time.

A randomized trial of two different doses of a SHR-5 Rhodiola rosea extract versus placebo and control of capacity for mental work. <=SEP=> A randomized, double-blind, placebo-controlled, parallel-group clinical study with an extra non-treatment group was performed to measure the effect of a single dose of standardized SHR-5 Rhodiola rosea extract on capacity for mental work against a background of fatigue and stress. An additional objective was to investigate a possible difference between two doses, one dose being chosen as the standard mean dose in accordance with well-established medicinal use as a psychostimulant/adaptogen, the other dose being 50% higher. Some physiological parameters, e.g. pulse rate, systolic and diastolic blood pressure, were also measured. The study was carried out on a highly uniform population comprising 161 cadets aged from 19 to 21 years. All groups were found to have very similar initial data, with no significant difference with regard to any parameter. The study showed a pronounced antifatigue effect reflected in an antifatigue index defined as a ratio called AFI. The verum groups had AFI mean values of 1.0385 and 1.0195, 2 and 3 capsules respectively, whilst the figure for the placebo group was 0.9046. This was statistically highly significant (p < 0.001) for both doses (verum groups), whilst no significant difference between the two dosage groups was observed. There was a possible trend in favour of the lower dose in the psychometric tests. No such trend was found in the physiological tests.

Euthanasia and physician-assisted suicide: focus on the data. <=SEP=> 1 MBritain and the United States began in the late 19th century. Legislation was periodically proposed only to be defeated until, in 1942, Switzerland decriminalised assistance in suicide for cases when there were no “selfish motives”. In 2002, euthanasia was legalised in the Netherlands and Belgium, then in Luxembourg in 2009, and most recently, in 2015 in Colombia and in 2016 in Canada. PAS, but not euthanasia, has been legalised in five US states. In Oregon, PAS was legalised by popular referendum in 1997. In addition, in 2009, Washington State legalised PAS by referendum and Montana by court ruling; Vermont in 2013 and California in 2015 also legalised PAS by legislation.

Encoder Semantic Decoder Input Image MultiTask Loss Instance Decoder Depth Decoder Semantic Task Uncertainty Instance Task Uncertainty Depth Task Uncertainty Σ <=SEP=> Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives. In this paper we make the observation that the performance of such systems is strongly dependent on the relative weighting between each task’s loss. Tuning these weights by hand is a difficult and expensive process, making multi-task learning prohibitive in practice. We propose a principled approach to multi-task deep learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. This allows us to simultaneously learn various quantities with different units or scales in both classification and regression settings. We demonstrate our model learning per-pixel depth regression, semantic and instance segmentation from a monocular input image. Perhaps surprisingly, we show our model can learn multi-task weightings and outperform separate models trained individually on each task.

Comparing software and hardware schemes for reducing the cost of branches <=SEP=> Pipelining has become a common technique to increase throughput of the instruction fetch, instruction decode, and instruction execution portions of modern computers. Branch instructions disrupt the flow of instructions through the pipeline, increasing the overall execution cost of branch instructions. Three schemes to reduce the cost of branches are presented in the context of a general pipeline model. Ten realistic Unix domain programs are used to directly compare the cost and performance of the three schemes and the results are in favor of the software-based scheme. For example, the software-based scheme has a cost of 1.65 cycles/branch vs. a cost of 1.68 cycles/branch of the best hardware scheme for a highly pipelined processor (11-stage pipeline). The results are 1.19 (software scheme) vs. 1.23 cycles/branch (best hardware scheme) for a moderately pipelined processor (5-stage pipeline).

Style Finder: Fine-Grained Clothing Style Detection and Retrieval <=SEP=> With the rapid proliferation of smartphones and tablet computers, search has moved beyond text to other modalities like images and voice. For many applications like Fashion, visual search offers a compelling interface that can capture stylistic visual elements beyond color and pattern that cannot be as easily described using text. However, extracting and matching such attributes remains an extremely challenging task due to high variability and deformability of clothing items. In this paper, we propose a fine-grained learning model and multimedia retrieval framework to address this problem. First, an attribute vocabulary is constructed using human annotations obtained on a novel fine-grained clothing dataset. This vocabulary is then used to train a fine-grained visual recognition system for clothing styles. We report benchmark recognition and retrieval results on Women's Fashion Coat Dataset and illustrate potential mobile applications for attribute-based multimedia retrieval of clothing items and image annotation.

Comparative Evaluation of Direct Torque Control Strategies for Permanent Magnet Synchronous Machines <=SEP=> This paper presents a comprehensive evaluation of several direct torque control (DTC) strategies for permanent magnet synchronous machines (PMSMs), namely DTC, model predictive DTC, and duty ratio modulated DTC. Moreover, field-oriented control is also included in this study. The aforementioned control strategies are reviewed and their control performances are analyzed and compared. The comparison is carried out through simulation, finite-element analysis, and experimental results of a PMSM fed by a two-level voltage source inverter. With the intent to fully reveal the advantages and disadvantages of each control strategy, critical evaluation has been conducted on the basis of several criteria: Torque ripple, stator flux ripple, switching frequency of inverter, steady-state control performance, dynamic response, machine losses, parameter sensitivity, algorithm complexity, and stator current total harmonic distortion.

A Social-Neuroscience Perspective on Empathy <=SEP=> In recent years, abundant evidence from behavioral and cognitive studies and functional-imaging experiments has indicated that individuals come to understand the emotional and affective states expressed by others with the help of the neural architecture that produces such states in themselves. Such a mechanism gives rise to shared representations, which constitutes one important aspect of empathy, although not the sole one. We suggest that other components, including people’s ability to monitor and regulate cognitive and emotional processes to prevent confusion between self and other, are equally necessary parts of a functional model of empathy. We discuss data from recent functional-imaging studies in support of such amodel and highlight the role of specific brain regions, notably the insula, the anterior cingulate cortex, and the right temporo-parietal region. Because this model assumes that empathy relies on dissociable informationprocessing mechanisms, it predicts a variety of structural or functional dysfunctions, depending on which mechanism is disrupted. KEYWORDS—empathy; intersubjectivity; affective sharing; perspective taking; emotion regulation Empathy refers to the capacity to understand and respond to the unique affective experiences of another person. At an experiential level of description, this psychological construct denotes a sense of similarity between one’s own feelings and those expressed by another person. At a basic level of description, empathy can be conceived of as an interaction between any two individuals, with one experiencing and sharing the feeling of the other. This sharing of feelings does not necessarily imply that one will act or even feel impelled to act in a supportive or sympathetic way. The social and emotional situations eliciting empathy can be quite complex, depending on the feelings experienced by the observed person (target), the relationship of the target to the observer, and the context in which they socially interact. In recent years, there has been a growing interest in the cognitive-affective neuroscience of empathy. In this article, we first discuss what the components of this psychological construct are and then present empirical data that can cast some light on the neurocognitive mechanisms subserving empathy, with a special emphasis on the perception of pain in others. THE MAJOR COMPONENTS OF EMPATHY Despite the various definitions of empathy, there is broad agreement on three primary components: (a) an affective response to another person, which often, but not always, entails sharing that person’s emotional state; (b) a cognitive capacity to take the perspective of the other person; and (c) emotion regulation. Some scholars favor a particular aspect over the others in their definitions. For instance, Hoffman (1981) views empathy as a largely involuntary vicarious response to affective cues from another person, while Batson et al. (1997) emphasize people’s intentional role-taking ability, which taps mainly into cognitive resources. These two aspects represent the opposite sides of the same coin: Depending on how empathy is triggered, the automatic tendency to mimic the expressions of others (bottom-up processing) and the capacity for the imaginative transposing of oneself into the feeling and thinking of another (top-down processing) are differentially involved. Moreover, both aspects tap, to some extent, similar neural mechanisms that underpin emotion processing. It is unlikely, however, that the overlap between selfand other representations is absolute. Such a complete overlapping between self and other could lead to personal distress (i.e., a self-focused, aversive response to another’s emotional state). This would consequently hamper the ability to toggle between selfand other perspectives and would not constitute an adaptive behavior. Therefore, self-regulatory processes are at play to prevent confusion between selfand other feelings. Address correspondence to Jean Decety, Department of Psychology, University of Chicago, 5848 S. University Avenue, Chicago, IL 60637; e-mail: decety@uchicago.edu. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 54 Volume 15—Number 2 Copyright r 2006 Association for Psychological Science Affective Sharing Between Self and Others A number of theorists have pointed out that empathy involves resonating with another person’s unconscious affect and experiencing that person’s experience along with him or her while keeping one’s own self-integrity intact. Notably, Basch (1983) speculated that, because their respective autonomic nervous systems are genetically programmed to respond in a like fashion, a given affective expression by a member of a particular species sometimes triggers a similar response in other members of that species. The view that unconscious automatic mimicry of a target generates in the observer the autonomic response associated with that bodily state and facial expression subsequently received empirical support from a variety of studies as well as observations from ethologists (Preston & de Waal, 2002). For instance, viewing facial expressions triggers similar expressions on one’s own face, even in the absence of conscious recognition of the stimulus. It was proposed that people may ‘‘catch’’ the emotions of others as a result of feedback generated by elementary motor mimicry of others’ expressive behavior, producing a simultaneous matching emotional experience. Interestingly, Levenson and Ruef (1992) found that a perceiver’s accuracy in inferring a target’s negative emotional states is related to the degree of physiological synchrony between the perceiver and the target. In other words, when the physiological state (e.g., heart rate, muscle activity) of two individuals is more closely matched, they are more accurate at perceiving each other’s feelings. Recently a functional magnetic resonance imaging (fMRI) experiment confirmed these results by showing that when participants are required to observe or to imitate facial expressions of various emotions, increased neurodynamic activity is detected in the brain regions that are implicated in the facial expression of these emotions, including the superior temporal sulcus, the anterior insula, and the amygdala, as well as specific areas of the premotor cortex (Carr, Iacoboni, Dubeau, Mazziotta, & Lenzi, 2003). The similarity between the expression of an emotion and the perception of that emotion has also been demonstrated for disgust. Damage to the insula, a region crucial in monitoring body state, can impair both the experience of disgust and the recognition of social signals (e.g., facial and emotional expression) that convey disgust. Functional neuroimaging studies (see Decety & Jackson, 2004, for review) have later shown that observing facial expressions of disgust and feelings of disgust activated very similar sites in the anterior insula and anterior cingulate cortex (ACC). Altogether these results point to one basic mechanism for social interaction: the direct link between perception and action. Such a system automatically prompts the observer to resonate with the emotional state of another individual, with the observer emulating the motor representations and associated autonomic and somatic responses of the observed target (Preston& deWaal, 2002). This covert mimicry process is responsible for shared affects and feelings between self and other. Developmental research has demonstrated that motor and affective mimicry are active already in the earliest interactions between infants and caregivers, raising the possibility that these processes are hardwired. The expression of pain provides a crucial signal that can motivate helping behaviors in others. Finding out how individuals perceive others in pain is thus an interesting way to decipher the underlying neural mechanisms of empathy. Recently, a handful of fMRI studies have indicated that the observation of pain in others is mediated by several brain areas that are implicated in processing the affective and motivational aspects of one’s own pain. In one study, participants received painful stimuli and observed signals indicating that their partner, who was present in the same room, had received the same stimuli (Singer et al., 2004). The rostral (or anterior) ACC, the insula, and the cerebellum were active during both conditions. In another study, participants were shown photographs depicting body parts in painful or neutral everyday-life situations, and were asked to imagine the level of pain that these situations would produce (Jackson, Meltzoff, & Decety, 2005). In comparison to neutral situations, painful conditions elicited significant activation in regions involved in the affective aspects of pain processing, notably the ACC and the anterior insula. Moreover, analyses taking into account the behavioral responses of participants revealed that the level of activity within the ACC correlated with ratings of pain that subjects ascribed to the different situations. This finding strongly suggests that this region plays a crucial role in affective modulation, which is triggered by the assessment of painful situations. Altogether, these results lend support to the idea that common neural circuits are involved in representing one’s own and others’ affective painrelated states (Fig. 1). Adopting the Perspective of the Other Humans have the capacity to intentionally adopt the subjective perspective of others by putting themselves into other people’s shoes and imagining what they feel. Such a capacity requires that one mentally simulate the other’s perspective using one’s own neural machinery. In one neuroimaging study, the participants were presented with short written sentences that depicted real-life situations likely to induce social emotions (e.g., shame) or other situations that were emotionally neutral (Ruby & Decety, 2004). Participants were each asked to imagine how they would feel if they were in those situatio

Author Attribution with CNN’s <=SEP=> In this report, the results from my CS224D final project are given and explained. The project was based on the application of relatively new neural network architectures, namely convolutional neural networks over word embeddings, to the task of authorship identification. The problem was posed as a classification task and models were evaluated over two datasets, a baseline of my own collection and a competition dataset with pre-existing, published results. Despite robust results on the baseline dataset, the CNN architecture failed to outmatch the best competition submission, although it did outscore most of the competitors’ submissions.

iKnow how you walk — A smartphone based personalized gait diagnosing system <=SEP=> Humans, due to aging and hormonal changes are prone to pains in their limbs. As a result of which, the fundamental activity of humans i.e., movement pattern of limbs also known as gait is affected. By exerting unequal weight on both limbs in-order to avoid pain in one leg, humans slowly develop an abnormal gait pattern consisting of limping and sideways bend in the posture. This often goes unnoticed for a long time. We propose a system using smartphones that can sense and detect abnormal walking patterns. The sensing of limb movement is performed by an embedded accelerometer in a smartphone and detection of abnormal walk pattern is performed by classifying different features such as stride length, walk velocity etc. By incorporating Naive Bayes and Decision trees classifiers, we obtained close to 89% accuracies in classifying different levels of abnormalaties. Further validation was done by implementing a decision tree based gait variation detector smartphone application across five users which resulted in 90% accuracy.

Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm <=SEP=> The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation-no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. Here, the authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. The authors show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, the authors show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.

The Influence of IS Affordances on Work Practices in Health Care: A Relational Coordination Approach <=SEP=> High-reliability healthcare organizations, such as Intensive Care Units, operate in challenging high-velocity environments regarding decision-making, uncertainty, and time constraints. Further, these organizations are multidisciplinary, professionally driven, and characterized by a strong hierarchical structure. Given such conditions, effective cross-functional communication is critical for ensuring successful work practices. We propose a theoretical model of IS affordances and Relational Coordination in the presence of functional status differences. We establish a set of propositions regarding how use patterns and types of IS affordances enable or constrain potential effects on Relational Coordination. We are currently conducting a case study of an ICU in a major University hospital, with the goal to iteratively refine our theory and propositions. We hope that this research will offer an in-depth understanding of the types of opportunities related to information systems, and the role of individual-level interpretations and actions across functional groups for these opportunities.

Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition <=SEP=> Domain adaptation aims to correct the mismatch in statistical properties between the source domain on which a classifier is trained and the target domain to which the classifier is to be applied. In this paper, we address the challenging scenario of unsupervised domain adaptation, where the target domain does not provide any annotated data to assist in adapting the classifier. Our strategy is to learn robust features which are resilient to the mismatch across domains and then use them to construct classifiers that will perform well on the target domain. To this end, we propose novel kernel learning approaches to infer such features for adaptation. Concretely, we explore two closely related directions. In the first direction, we propose unsupervised learning of a geodesic flow kernel (GFK). The GFK summarizes the inner products in an infinite sequence of feature subspaces that smoothly interpolates between the source and target domains. In the second direction, we propose supervised learning of a kernel that discriminatively combines multiple base GFKs. Those base kernels model the source and the target domains at fine-grained granularities. In particular, each base kernel pivots on a different set of landmarks—the most useful data instances that reveal the similarity between the source and the target domains, thus bridging them to achieve adaptation. Our approaches are computationally convenient, automatically infer important hyper-parameters, and are capable of learning features and classifiers discriminatively without demanding labeled data from the target domain. In extensive empirical studies on standard benchmark recognition datasets, our appraches yield state-of-the-art results compared to a variety of competing methods.

Dissecting Contributions of Prefrontal Cortex and Fusiform Face Area to Face Working Memory <=SEP=> Interactions between prefrontal cortex (PFC) and stimulusspecific visual cortical association areas are hypothesized to mediate visual working memory in behaving monkeys. To clarify the roles for homologous regions in humans, event-related fMRI was used to assess neural activity in PFC and fusiform face area (FFA) of subjects performing a delay-recognition task for faces. In both PFC and FFA, activity increased parametrically with memory load during encoding and maintenance of face stimuli, despite quantitative differences in the magnitude of activation. Moreover, timing differences in PFC and FFA activation during memory encoding and retrieval implied a context dependence in the flow of neural information. These results support existing neurophysiological models of visual working memory developed in the nonhuman primate.

Perception-Guided Multimodal Feature Fusion for Photo Aesthetics Assessment <=SEP=> Photo aesthetic quality evaluation is a challenging task in multimedia and computer vision fields. Conventional approaches suffer from the following three drawbacks: 1) the deemphasized role of semantic content that is many times more important than low-level visual features in photo aesthetics; 2) the difficulty to optimally fuse low-level and high-level visual cues in photo aesthetics evaluation; and 3) the absence of a sequential viewing path in the existing models, as humans perceive visually salient regions sequentially when viewing a photo.
 To solve these problems, we propose a new aesthetic descriptor that mimics humans sequentially perceiving visually/semantically salient regions in a photo. In particular, a weakly supervised learning paradigm is developed to project the local aesthetic descriptors (graphlets in this work) into a low-dimensional semantic space. Thereafter, each graphlet can be described by multiple types of visual features, both at low-level and in high-level. Since humans usually perceive only a few salient regions in a photo, a sparsity-constrained graphlet ranking algorithm is proposed that seamlessly integrates both the low-level and the high-level visual cues. Top-ranked graphlets are those visually/semantically prominent graphlets in a photo. They are sequentially linked into a path that simulates the process of humans actively viewing. Finally, we learn a probabilistic aesthetic measure based on such actively viewing paths (AVPs) from the training photos that are marked as aesthetically pleasing by multiple users. Experimental results show that: 1) the AVPs are 87.65% consistent with real human gaze shifting paths, as verified by the eye-tracking data; and 2) our photo aesthetic measure outperforms many of its competitors.