Query: What Action Causes This? Towards Naive Physical Action-Effect Prediction

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Physics 101: Learning Physical Object Properties from Unlabeled Videos <=SEP=> We study the problem of learning physical properties of objects from unlabeled videos. Humans can learn basic physical laws when they are very young, which suggests that such tasks may be important goals for computational vision systems. We consider various scenarios: objects sliding down an inclined surface and colliding; objects attached to a spring; objects falling onto various surfaces, etc. Many physical properties like mass, density, and coefficient of restitution influence the outcome of these scenarios, and our goal is to recover them automatically. We have collected 17,408 video clips containing 101 objects of various materials and appearances (shapes, colors, and sizes). Together, they form a dataset, named Physics 101, for studying object-centered physical properties. We propose an unsupervised representation learning model, which explicitly encodes basic physical laws into the structure and use them, with automatically discovered observations from videos, as supervision. Experiments demonstrate that our model can learn physical properties of objects from video. We also illustrate how its generative nature enables solving other tasks such as outcome prediction.

Natural Language Processing (almost) from Scratch <=SEP=> We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.

VQA: Visual Question Answering <=SEP=> 

Training Deep Neural Networks on Noisy Labels with Bootstrapping <=SEP=> Current state-of-the-art deep learning systems for visual object recognition and detection use purely supervised training with regularization such as dropout to avoid overfitting. The performance depends critically on the amount of labeled examples, and in current practice the labels are assumed to be unambiguous and accurate. However, this assumption often does not hold; e.g. in recognition, class labels may be missing; in detection, objects in the image may not be localized; and in general, the labeling may be subjective. In this work we propose a generic way to handle noisy and incomplete labeling by augmenting the prediction objective with a notion of consistency. We consider a prediction consistent if the same prediction is made given similar percepts, where the notion of similarity is between deep network features computed from the input data. In experiments we demonstrate that our approach yields substantial robustness to label noise on several datasets. On MNIST handwritten digits, we show that our model is robust to label corruption. On the Toronto Face Database, we show that our model handles well the case of subjective labels in emotion recognition, achieving state-of-theart results, and can also benefit from unlabeled face images with no modification to our method. On the ILSVRC2014 detection challenge data, we show that our approach extends to very deep networks, high resolution images and structured outputs, and results in improved scalable detection.

Yago: a core of semantic knowledge <=SEP=> 

The Type of Information Overload Affects Electronic Knowledge Repository continuance <=SEP=> In the present competitive organizational environment more organizations are implementing knowledge management initiatives to gain strategic advantage. One such initiative is that of implementing electronic knowledge repositories (EKR) which often leads to a rapid increase in the quantity of information employees must process daily, raising concerns of employees being overloaded. This is especially true for current EKRs using distributive technology, enabling customizable individual workspaces which can result in loose knowledge structures. This paper identifies a new type of information overload (IO), extending the concept as occurring in both knowledge seekers and contributors and uses cognitive dissonance theory to provide evidence that IO can change employees’ perception of EKR usage. This research paper provides the first empirical evidence that overload has no sufficient affect on EKR continuance intention directly, but has a significant negative affect on the two main success measures: Perceived usefulness and satisfaction of the system.

Learning to aggregate vertical results into web search results <=SEP=> Aggregated search is the task of integrating results from potentially multiple specialized search services, or verticals, into the Web search results. The task requires predicting not only which verticals to present (the focus of most prior research), but also predicting where in the Web results to present them (i.e., above or below the Web results, or somewhere in between). Learning models to aggregate results from multiple verticals is associated with two major challenges. First, because verticals retrieve different types of results and address different search tasks, results from different verticals are associated with different types of predictive evidence (or features). Second, even when a feature is common across verticals, its predictiveness may be vertical-specific. Therefore, approaches to aggregating vertical results require handling an inconsistent feature representation across verticals, and, potentially, a vertical-specific relationship between features and relevance. We present 3 general approaches that address these challenges in different ways and compare their results across a set of 13 verticals and 1070 queries. We show that the best approaches are those that allow the learning algorithm to learn a vertical-specific relationship between features and relevance.

Clustering Validity Based on the Improved Hubert \Gamma Statistic and the Separation of Clusters <=SEP=> The validity of clustering is one important research field in clustering analysis, and many clustering validity functions have been proposed, especially those based on the geometrical structure of data set, such as Dunn's index and Xie-Beni index. In this way, the compactness and the separation of clusters are usually taken into account. Xie-Beni index decreases with the number of partitions increasing. It is difficult to choose the optimal number of clusters when there are lots of clusters in data. In this paper, a novel clustering validity function is proposed, which is based on the improved Huber Gamma statistic combined with the separation of clusters. Unlike other clustering validity, the function has the only maximum with the clustering number increasing. The experiments indicate that the function can be used as the optimal index for the choice of the clustering numbers

Advanced algorithms for neural networks: a C++ sourcebook <=SEP=> 

A Dual-Polarized Dual-Band Antenna With High Gain for 2G/3G/LTE Indoor Communications <=SEP=> A compact dual-polarized dual-band omnidirectional antenna with high gain is presented for 2G/3G/LTE communications, which comprise two horizontal polarization (HP) and a vertical polarization (VP) elements. The upper HP element consists of four pairs of modified printed magneto-electric (ME) dipoles that are fed by a four-way power divider feeding network, and eight pieces of arc-shaped parasitic patches that are printed on both sides of the circular printed circuit board alternately. The four-way power divider feeding network together with the four pairs of ME dipoles mainly provide a stable 360° radiation pattern and high gain, while the eight pieces of patches are used to enhance the bandwidth. The lower HP element is similar to the upper one except that it do not have the parasitic patches. The VP element consists of four pairs of cone-shaped patches. Different from the HP element, the upper VP element provides the lower frequency band while the lower VP one yields the upper frequency band. The VP element and the HP element are perpendicularly arranged to obtain the compact and dual-polarized features. Measured results show that a bandwidth of 39.6% (0.77–1.15 GHz) with a gain of about 2.6 dBi and another bandwidth of 55.3% (1.66–2.93 GHz) with a gain of about 4.5 dBi can be achieved for the HP direction, while a bandwidth of 128% (0.7–3.2 GHz) with a gain of around 4.4 dBi can be acquired for the VP direction. Port isolation larger than 20 dB and low-gain variation levels within 2 dBi are also obtained. Hence, the proposed antenna is suitable for 2G/3G/LTE indoor communications.

Mature HIV-1 capsid structure by cryo-electron microscopy and all-atom molecular dynamics <=SEP=> Retroviral capsid proteins are conserved structurally but assemble into different morphologies. The mature human immunodeficiency virus-1 (HIV-1) capsid is best described by a ‘fullerene cone’ model, in which hexamers of the capsid protein are linked to form a hexagonal surface lattice that is closed by incorporating 12 capsid-protein pentamers. HIV-1 capsid protein contains an amino-terminal domain (NTD) comprising seven α-helices and a β-hairpin, a carboxy-terminal domain (CTD) comprising four α-helices, and a flexible linker with a 310-helix connecting the two structural domains. Structures of the capsid-protein assembly units have been determined by X-ray crystallography; however, structural information regarding the assembled capsid and the contacts between the assembly units is incomplete. Here we report the cryo-electron microscopy structure of a tubular HIV-1 capsid-protein assembly at 8 Å resolution and the three-dimensional structure of a native HIV-1 core by cryo-electron tomography. The structure of the tubular assembly shows, at the three-fold interface, a three-helix bundle with critical hydrophobic interactions. Mutagenesis studies confirm that hydrophobic residues in the centre of the three-helix bundle are crucial for capsid assembly and stability, and for viral infectivity. The cryo-electron-microscopy structures enable modelling by large-scale molecular dynamics simulation, resulting in all-atom models for the hexamer-of-hexamer and pentamer-of-hexamer elements as well as for the entire capsid. Incorporation of pentamers results in closer trimer contacts and induces acute surface curvature. The complete atomic HIV-1 capsid model provides a platform for further studies of capsid function and for targeted pharmacological intervention.

Quantitative functional evaluation of a 3D-printed silicone-embedded prosthesis for partial hand amputation: A case report. <=SEP=> STUDY DESIGN
A male patient with partial hand amputation of his nondominant hand, with only stumps of the proximal phalanx of the first and fifth finger, was evaluated. The performance of using two alternative 3D printed silicone-embedded personalized prostheses was evaluated using the quantitative Jebsen Hand Function Test.


INTRODUCTION
Custom design and fabrication of 3D printed prostheses appears to be a good technique for improving the clinical treatment of patients with partial hand amputations. Despite its importance the literature shows an absence of studies reporting on quantitative functional evaluations of 3D printed hand prostheses.


PURPOSE OF THE STUDY
We aim at producing the first quantitative assessment of the impact of using 3D printed silicone-embedded prostheses that can be fabricated and customized within the clinical environment.


METHODS
Alginate molds and computed tomographic scans were taken from the patient's hand. Each candidate prosthesis was modeled in Computer Aided Design software and then fabricated using a combination of 3D printed parts and silicone-embedded components.


DISCUSSION
Incorporating the patient's feedback during the design loop was very important for obtaining a good aid on his work activities. Although the explored patient-centered design process still requires a multidisciplinary team, functional benefits are large.


CONCLUSION(S)
Quantitative data demonstrates better hand performance when using 3D printed silicone-embedded prosthesis vs not using any aid. The patient accomplished complex tasks such as driving a nail and opening plastic bags. This was impossible without the aid of produced prosthesis.

A new hybrid imperialist swarm-based optimization algorithm for university timetabling problems <=SEP=> 

Graphical Models for Probabilistic and Causal Reasoning <=SEP=> This chapter surveys the development of graphical models known as Bayesian networks, summarizes their semantical basis and assesses their properties and applications to reasoning and planning. Bayesian networks are directed acyclic graphs (DAGs) in which the nodes represent variables of interest (e.g., the temperature of a device, the gender of a patient, a feature of an object, the occurrence of an event) and the links represent causal influences among the variables. The strength of an influence is represented by conditional probabilities that are attached to each cluster of parents-child nodes in the network. Figure 1 illustrates a simple yet typical Bayesian network. It describes the causal relationships among the season of the year (X1), whether rain falls (X2) during the season, whether the sprinkler is on (X3) during that season, whether the pavement would get wet (X4), and whether the pavement would be slippery (X5). All variables in this figure are binary, taking a value of either true or false, except the root variable X1 which can take one of four values: Spring, Summer, Fall, or Winter. Here, the absence of a direct link between X1 and X5, for example, captures our understanding that the influence of seasonal variations on the slipperiness of the pavement is mediated by other conditions (e.g., the wetness of the pavement). As this example illustrates, a Bayesian network constitutes a model of the environment rather than, as in many other knowledge representation schemes (e.g., logic, rule-based systems and neural networks), a model of the reasoning process. It simulates, in fact, the causal mechanisms that operate in the environment, and thus allows the investigator to answer a variety of queries, including: associational queries, such as “Having observed A, what can we expect of B?”; abductive queries, such as “What is the most plausible explanation for a given set of observations?”; and control queries; such as “What will happen if we intervene and act on the environment?” Answers to the first type of query depend only on probabilistic

Urban Physics : Effect of the micro-climate on comfort , health and energy demand <=SEP=> Y-NC-ND license. Abstract The global trend towards urbanisation explains the growing interest in the study of the modification of the urban climate due to the heat island effect and global warming, and its impact on energy use of buildings. Also urban comfort, health and durability, referring respectively to pedestrian wind/ thermal comfort, pollutant dispersion and wind-driven rain are of interest. Urban Physics is a wellestablished discipline, incorporating relevant branches of physics, environmental chemistry, aerodynamics, meteorology and statistics. Therefore, Urban Physics is well positioned to provide keycontributions to the current urban problems and challenges. The present paper addresses the role of Urban Physics in the study of wind comfort, thermal comfort, energy demand, pollutant dispersion and wind-driven rain. Furthermore, the three major research methods applied in Urban Physics, namely field experiments, wind tunnel experiments and numerical simulations are discussed. Case studies illustrate the current challenges and the relevant contributions of Urban Physics. & 2012. Higher Education Press Limited Company. Production and hosting by Elsevier B.V. Open access under CC BY-NC-ND license.

Semantic portrait color transfer with internet images <=SEP=> We present a novel color transfer method for portraits by exploring their high-level semantic information. First, a database is set up which consists of a collection of portrait images download from the Internet, and each of them is manually segmented using image matting as a preprocessing step. Second, we search the database using Face++ to find the images with similar poses to a given source portrait image, and choose one satisfactory image from the results as the target. Third, we extract portrait foregrounds from both source and target images. Then, the system extracts the semantic information, such as faces, eyes, eyebrows, lips, teeth, etc., from the extracted foreground of the source using image matting algorithms. After that, we perform color transfer between corresponding parts with the same semantic information. We get the final transferred result by seamlessly compositing different parts together using alpha blending. Experimental results show that our semantics-driven approach can generate better color transfer results for portraits than previous methods and provide users a new means to retouch their portraits.

Real-Time Detection of Camera Tampering <=SEP=> This paper presents a novel technique for camera tampering detection. It is implemented in real-time and was developed for use in surveillance and security applications. This method identifies camera tampering by detecting large differences between older frames of video and more recent frames. A buffer of incoming video frames is kept and three different measures of image dissimilarity are used to compare the frames. After normalization, a set of conditions is tested to decide if camera tampering has occurred. The effects of adjusting the internal parameters of the algorithm are examined. The performance of this method is shown to be extremely favorable in real-world settings.

Effect of Methanol Addition on the Resistivity and Morphology of PEDOT:PSS Layers on Top of Carbon Nanotubes for Use as Flexible Electrodes. <=SEP=> UNLABELLED
Overcoating carbon nanotube (CNT) films on flexible poly(ethylene terephthalate) (PET) foils with poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (


PEDOT
PSS) layers reduces the surface roughness, which is interesting for use in organic electronics. Adding methanol to the


PEDOT
PSS aqueous solution used for spin coating of the


PEDOT
PSS layer improves the wetting behavior of the CNT/PET surface. Samples with different volume fractions of methanol (0, 33, 50, 67, and 75 vol %) are compared with respect to the transmission, horizontal, and vertical resistivity. With grazing-incidence small-angle X-ray scattering, the film morphologies are probed, which is challenging because of the substrate flexibility. At 50 vol %, methanol optimum conditions are achieved with the resistivity close to that of the bare CNT/PET substrates because of the best contact between the


PEDOT
PSS film and CNT surface. At lower methanol ratios, the


PEDOT
PSS films cannot adapt the CNT morphology, and at higher methanol ratios, they rupture into domains and no continuous


PEDOT
PSS layers are formed.

Matching resumes and jobs based on relevance models <=SEP=> We investigate the difficult problem of matching semi-structured resumes and jobs in a large scale real-world collection. We compare standard approaches to Structured Relevance Models (SRM), an extensionof relevance-based language model for modeling and retrieving semi-structured documents. Preliminary experiments show that the SRM approach achieved promising performance and performed better than typical unstructured relevance models.

Business processes--attempts to find a definition <=SEP=> Definitions of business process given in much of the literature on Business Process Management are limited in depth and their related models of business processes are correspondingly constrained. After giving a brief history of the progress of business process modeling techniques from production systems to the office environment, this paper proposes that most definitions are based on machine metaphor type explorations of a process. While these techniques are often rich and illuminating it is suggested that they are too limited to express the true nature of business processes that need to develop and adapt to today’s challenging environment.

A multi-view deep learning architecture for classification of breast microcalcifications <=SEP=> In this paper we address the problem of differentiating between malignant and benign tumors based on their appearance in the CC and MLO mammography views. Classification of clustered breast microcalcifications into benign and malignant categories is an extremely challenging task for computerized algorithms and expert radiologists alike. We describe a deep-learning classification method that is based on two view-level decisions, implemented by two neural networks, followed by a single-neuron layer that combines the viewlevel decisions into a global decision that mimics the biopsy results. Our method is evaluated on a large multi-view dataset extracted from the standardized digital database for screening mammography (DDSM). Experimental results show that our network structure significantly improves on previously suggested methods.

Four-Dimensional Discrete-time Lotka-Volterra Models with an Application to Ecology <=SEP=> This paper presents a study of the two-predators-two-preys discrete-time Lotka-Volterra model with selfinhibition terms for preys with direct applications to ecological problems. Parameters in the model are modified so that each of them has its own biological meaning, enabling more intuitive interpretation of biological conditions to the model . Moreover, the modified version is applicable to simulate a part of a large ecosystem, not only a closed predator-prey system with four species. An easy graphical method of analysis of the conditions on parameters ensuring long persistence under coexistence is presented. As an application, it is explained that a predator specie who feed on relatively small number of preys compared to the other predator species must be selective on the available preys in order for long persistence of the ecosystem. This may be regarded as a theoretical explanation of the existence of flush-pursuer birds, those who uses highly specialized hunting strategy and cross-adapts to the ecosystem relative to the ordinary bird species.

Time Series Forecasting Based on Augmented Long Short-Term Memory <=SEP=> In this paper, we use recurrent autoencoder model to predict the time series in single and multiple steps ahead. Previous prediction methods, such as recurrent neural network (RNN) and deep belief network (DBN) models, cannot learn long term dependencies. And conventional long short-term memory (LSTM) model doesn’t remember recent inputs. Combining LSTM and autoencoder (AE), the proposed model can capture long-term dependencies across data points and uses features extracted from recent observations for augmenting LSTM at the same time. Based on comprehensive experiments, we show that the proposed methods significantly improves the state-of-art performance on chaotic time series benchmark and also has better performance on real-world data. Both single-output and multiple-output predictions are investigated.

Neural basis of speech perception. <=SEP=> The functional neuroanatomy of speech processing has been difficult to characterize. One major impediment to progress has been the failure to consider task effects when mapping speech-related processing systems. We summarize a dual-stream model of speech processing that addresses this situation. In this model, a ventral stream processes speech signals for comprehension, and a dorsal stream maps acoustic speech signals to parietal and frontal-lobe articulatory networks. The model assumes that the ventral stream is largely bilaterally organized, although there are important computational differences between the left- and right-hemisphere systems, whereas the dorsal stream is strongly left-hemisphere-dominant.

Automatic medical image annotation in ImageCLEF 2007: Overview, results, and discussion <=SEP=> In this paper, the automatic medical annotation task of the 2007 CLEF cross-language image retrieval campaign (ImageCLEF) is described. The paper focusses on the images used, the task setup, and the results obtained in the evaluation campaign. Since 2005, the medical automatic image annotation task exists in ImageCLEF with increasing complexity to evaluate the performance of state-of-the-art methods for completely automatic annotation of medical images based on visual properties. The paper also describes the evolution of the task from its origin in 2005 to 2007. The 2007 task, comprising 11,000 fully annotated training images and 1,000 test images to be annotated, is a realistic task with a large number of possible classes at different levels of detail. Detailed analysis of the methods across participating groups is presented with respect to the (i) image representation, (ii) classification method, and (iii) use of the class hierarchy. The results show that methods which build on local image descriptors and discriminative models are able to provide good predictions of the image classes, mostly by using techniques that were originally developed in the machine learning and computer vision domain for object recognition in non-medical images.

Nasal base reduction by alar release: a laboratory evaluation. <=SEP=> BACKGROUND
When reducing the broad nasal base, there is a limit to the amount of soft tissue that can be resected, beyond which the anatomy distorts and the nostrils become stenotic (if resection enters the nostril). Alar mobilization by freeing soft-tissue attachments helps. This study purported to examine the nature of those attachments and the extent of medialization.


METHODS
The supporting tissues of the ala were sequentially divided in 16 fresh hemifacial cadavers. Key structures included the following: (1) the soft tissues and pyriform ligament of the anterior maxilla, (2) the periosteum posterior to the pyriform rim (in the bony nasal vault), and (3) the soft tissues along the horizontal pyriform rim. After release of each tethering region, the ala-pyriform distance was measured.


RESULTS
After releasing the anterior maxillary periosteum and pyriform ligament along the vertical pyriform rim, the ala-pyriform distance was reduced by a mean of 1.9 mm. After releasing the periosteum posterior to the pyriform rim (in the nasal vault), it was reduced by a mean of 1.7 mm. Releasing the soft tissues (which were thick medially) of the horizontal pyriform rim reduced the mean distance 1.0 mm for a total of 4.6 mm. Medialization resulting from anterior and posterior releases was significantly greater than that from the horizontal pyriform rim (p < 0.0006 and p < 0.015, respectively), but they were not significantly different from one another.


CONCLUSIONS
This cadaver study confirmed the role of the stabilizing effect of the pyriform ligament and the periosteum lateral and posterior to the pyriform rim. The total release was substantial, suggesting a clinical means of achieving tension-free alar medialization.

Deep Network Guided Proof Search <=SEP=> Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go. Automated first-order theorem provers can aid in the formalization and verification of mathematical theorems and play a crucial role in program analysis, theory reasoning, security, interpolation, and system verification. Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search. We give experimental evidence that with a hybrid, two-phase approach, deep learning based guidance can significantly reduce the average number of proof search steps while increasing the number of theorems proved. Using a few proof guidance strategies that leverage deep neural networks, we have found first-order proofs of 7.36% of the first-order logic translations of the Mizar Mathematical Library theorems that did not previously have ATP generated proofs. This increases the ratio of statements in the corpus with ATP generated proofs from 56% to 59%.

Ontological Constructs to Create Money Laundering Schemes <=SEP=> There is an increasing tendency in the money laundering sector to utilize electronic commerce and web services. Misuse of web services and electronic money transfers occurs at many points in complex trading schemes. We provide ontological components that can be combined to construct some of these money laundering schemes. These constructs can be helpful for investigators, in order to decompose suspected financial schemes and recognize

Procedural facade variations from a single layout <=SEP=> We introduce a framework to generate many variations of a facade design that look similar to a given facade layout. Starting from an input image, the facade is hierarchically segmented and labeled with a collection of manual and automatic tools. The user can then model constraints that should be maintained in any variation of the input facade design. Subsequently, facade variations are generated for different facade sizes, where multiple variations can be produced for a certain size. Computing such new facade variations has many unique challenges, and we propose a new algorithm based on interleaving heuristic search and quadratic programming. In contrast to most previous work, we focus on the generation of new design variations and not on the automatic analysis of the input's structure. Adding a modeling step with the user in the loop ensures that our results routinely are of high quality.

Survey of Insurance Fraud Detection Using Data Mining Techniques <=SEP=> 62 Abstract— With an increase in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection has become an emerging topics of great importance for academics, research and industries. Financial fraud is a deliberate act that is contrary to law, rule or policy with intent to obtain unauthorized financial benefit and intentional misstatements or omission of amounts by deceiving users of financial statements, especially investors and creditors. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. Financial fraud can be classified into four: bank fraud, insurance fraud, securities and commodities fraud. Fraud is nothing but wrongful or criminal trick planned to result in financial or personal gains. This paper describes the more details on insurance sector related frauds and related solutions. In finance, insurance sector is doing important role and also it is unavoidable sector of every human being.