title: A Process Programmer Looks at the Spiral Model: A Tribute to the Deep Insights of Barry W. Boehm

abstract: This paper elaborates on implications of Barry W. Boehm's Spiral Model of software development. The paper notes that the Spiral Model presents a compelling view of software development, evocatively represented by a visual image that appeals strongly to intuition, and notes that the view and image have motivated and justifled a range of important views of how software development should be done. This paper enhances and elaborates on the intuitions by supplementing them with a deflnition of the Spiral Model that is enunciated in terms of a rigorously deflned language. The rigorous enunciation and accompanying alternative visual depiction are then used to provide clariflcation and formalization of some of the clearly-indicated elaborations of the Spiral Model. Both the Waterfall Model of software development and the Scrum agile method are presented as possible elaborations of the Spiral Model. Still other elaborations are indicated. Similarities in the visualizations of these development approaches suggest some underlying similarities in the approaches themselves, suggesting the potential value of efiective process visualizations. The breadth of these elaborations is also used to suggest how the Spiral Model seems to provide a strong focus on some of the quintessential aspects of what comprises efiective software development.


An Underwater Positioning Scheme for 3 D Acoustic Sensor Networks


title: The Leaky Mode Resonance Condition Ensures 100% Diffraction Efficiency of Mirror-Based Resonant Gratings

abstract: The resonance condition of a leaky mode propagating in a dielectric multilayer, which is supported by a mirror and excited by a free-space wave from the cover medium, is simply derived from the phase of the reflection at the mirror and cover interfaces. The leaky mode resonance is used to obtain 100% first-order diffraction efficiency in a reflection resonant grating by destructive interference in the direction of Fresnel reflection. A number of examples support the validity of this condition for high efficiency and illustrate the usefulness of this intelligible representation of the diffraction phenomena for the synthesis of novel diffractive elements.


Bilevel Programming Approaches to the Computation of Optimistic and Pessimistic Single-Leader-Multi-Follower Equilibria


title: Swish: a Self-Gated Activation Function

abstract: The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various alternatives to ReLU have been proposed, none have managed to replace it due to inconsistent gains. In this work, we propose a new activation function, named Swish, which is simply $f(x) = x \cdot \text{sigmoid}(x)$. Our experiments show that Swish tends to work better than ReLU on deeper models across a number of challenging datasets. For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9% for Mobile NASNet-A and 0.6% for Inception-ResNet-v2. The simplicity of Swish and its similarity to ReLU make it easy for practitioners to replace ReLUs with Swish units in any neural network.


The effect of leg-to-body ratio on male attractiveness depends on the ecological validity of the figures


title: Connecting’ and ‘Disconnecting’ With Civic Life: Patterns of Internet Use and the Production of Social Capital

abstract: This article explores the relationship between Internet use and the individual-level production of social capital. To do so, the authors adopt a motivational perspective to distinguish among types of Internet use when examining the factors predicting civic engagement, interpersonal trust, and life contentment. The predictive power of new media use is then analyzed relative to key demographic, contextual, and traditional media use variables using the 1999 DDB Life Style Study. Although the size of associations is generally small, the data suggest that informational uses of the Internet are positively related to individual differences in the production of social capital, whereas social-recreational uses are negatively related to these civic indicators. Analyses within subsamples defined by generational age breaks further suggest that social capital production is related to Internet use among Generation X, while it is tied to television use among Baby Boomers and newspaper use among members of the Civic Gene...


title: The Case for Automatic Database Administration using Deep Reinforcement Learning

abstract: Like any large software system, a full-fledged DBMS offers an overwhelming amount of configuration knobs. These range from static initialisation parameters like buffer sizes, degree of concurrency, or level of replication to complex runtime decisions like creating a secondary index on a particular column or reorganising the physical layout of the store. To simplify the configuration, industry grade DBMSs are usually shipped with various advisory tools, that provide recommendations for given workloads and machines. However, reality shows that the actual configuration, tuning, and maintenance is usually still done by a human administrator, relying on intuition and experience. Recent work on deep reinforcement learning has shown very promising results in solving problems, that require such a sense of intuition. For instance, it has been applied very successfully in learning how to play complicated games with enormous search spaces. Motivated by these achievements, in this work we explore how deep reinforcement learning can be used to administer a DBMS. First, we will describe how deep reinforcement learning can be used to automatically tune an arbitrary software system like a DBMS by defining a problem environment. Second, we showcase our concept of NoDBA at the concrete example of index selection and evaluate how well it recommends indexes for given workloads.


title: Critical review of static taint analysis of android applications for detecting information leakages

abstract: This paper presents a rigorous review of static taint analysis for inadvertently information leakage in Android applications. Static taint analysis is widely implemented to monitor and control privacy leaks. The surveyed studies are analyzed and evaluated based on techniques adopted, tools implemented, applications involved in experiments, leaks identified through experiments, and applications identified as infected. Most of the surveyed studies applied Call Graph (CG) or Control Flow Graph (CFG) to perform static taint analysis. We also conducted a number of experiments to further analyze the execution and effectiveness of FlowDroid on Android applications having both intra-component communications and inter-component communications. Although FlowDroid was developed to target only intra-component leaks, this does not limit its application on inter-component communications. However, its precision is reduced.


title: DoS Attacks on Controller Area Networks by Fault Injections from the Software Layer

abstract: The Controller Area Network (CAN) is still the most widely employed bus in the automotive sector. Its lack of security mechanisms led to a high number of attacks and consequently several security countermeasures were proposed, i.e., authentication protocols or intrusion detection mechanisms. We discuss vulnerabilities of the CAN data link layer that can be triggered from the application level with the use of an off the shelf CAN transceiver. Namely, due to the wired-AND design of the CAN bus, dominant bits will always overwrite recessive ones, a functionality normally used to assure priority for frames with low value identifiers. We exploit this characteristic and show Denial of Service attacks both on senders and receivers based on bit injections by using bit banging to maliciously control the CAN transceiver. We demonstrate the effects and limitations of such attacks through experimental analysis and discuss possible countermeasures. In particular, these attacks may have high impact on centralized authentication mechanisms that were frequently proposed in the literature since these attacks can place monitoring nodes in a bus-off state for certain periods of time.


title: A Brachiating Robot Controller

abstract: We report on our empirical studies of a new controller for a two-link brachiating robot. Motivated by the pendulum-like motion of an ape's brachiation, we encode this task as the output of a "target dynamical system". Numerical simulations indicate that the resulting controller solves a number of brachiation problems that we term the "ladder", "swing-up", and "rope" problems. Preliminary analysis provides some explanation for this success. The proposed controller is implemented on a physical system in our laboratory. The robot achieves behaviors including "swing locomotion" and "swing up" and is capable of continuous locomotion over several rungs of a ladder. We discuss a number of formal questions whose answers will be required to gain a full understanding of the strengths and weaknesses of this approach.


title: Shared Steering Control Using Safe Envelopes for Obstacle Avoidance and Vehicle Stability

abstract: Steer-by-wire technology enables vehicle safety systems to share control with a driver through augmentation of the driver's steering commands. Advances in sensing technologies empower these systems further with real-time information about the surrounding environment. Leveraging these advancements in vehicle actuation and sensing, the authors present a shared control framework for obstacle avoidance and stability control using two safe driving envelopes. One of these envelopes is defined by the vehicle handling limits, whereas the other is defined by spatial limitations imposed by lane boundaries and obstacles. A model predictive control (MPC) scheme determines at each time step if the current driver command allows for a safe vehicle trajectory within these two envelopes, intervening only when such a trajectory does not exist. In this way, the controller shares control with the driver in a minimally invasive manner while avoiding obstacles and preventing loss of control. The optimal control problem underlying the controller is inherently nonconvex but is solved as a set of convex problems allowing for reliable real-time implementation. This approach is validated on an experimental vehicle working with human drivers to negotiate obstacles in a low friction environment.


Advanced OR and AI Methods in Transportation BEE COLONY OPTIMIZATION – A COOPERATIVE LEARNING APPROACH TO COMPLEX TRANSPORTATION PROBLEMS


title: Probability Estimates for Multi-class Classification by Pairwise Coupling

abstract: Pairwise coupling is a popular multi-class classification method that combines together all pairwise comparisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. Both methods can be reduced to linear systems and are easy to implement. We show conceptually and experimentally that the proposed approaches are more stable than two existing popular methods: voting and [3].


title: Performance analysis for Sapele thermal power station : case study of Nigeria

abstract: Performance indices as measured by percentage of shortfall of energy generated, load factor, utilization factor and capacity factor of Sapele thermal power station in the period 1997 to 2006 is presented. The thermal station uses both steam and gas turbines. The station consists of six units of steam turbine and four units of gas turbine with total installed capacity of 1020 MW. But it was established that less than 17 % of the installed capacity was available. The percentage shortfall of energy generated for the period under review ranged from 27.4 to 49.1 %. This was due to aging of the plant equipment, improper operation and maintenance. The load factor was between 39.9 and 64 % as against International best practice of 80 %. The inability of Power Holding Company of Nigeria (PHCN) to do turn-around maintenance on the plant was advanced for the dismal value. Average plant availability of the plant for the studied period was less than 21% as against Industry best practice of over 95 %. The capacity factor was even more dismal. For the period under review, it ranged from as low as 5.49 % in 2006 to the highest value of 17.19 % in 1997. Low capacity factor indicates excessive plant failure. Measures to improve the performance indices of the plant have been suggested such as training of operation and maintenance (O & M) personnel regularly, improvement in O & M practices, proper spare parts inventory, organizing regular management meetings and improve general house keeping of the plant.


title: A DC-90 GHz 4-Vpp differential linear driver in a 0.13 μm SiGe:C BiCMOS technology for optical modulators

abstract: In this paper, a linear driver for optical modulators in a 0.13 μm SiGe:C BiCMOS technology with fT/fmax of 300/500 GHz is presented. The driver is implemented following a distributed amplifier topology in a differential manner. In a 50-Ω environment, the circuit delivers a maximum differential output amplitude of 4 Vpp, featuring a small-signal gain of 13 dB and 3-dB bandwidth of 90 GHz. Time-domain measurements using OOK (up to 56 Gb/s) and PAM-4 (at 30Gbaud) are performed, demonstrating the maximum output swing and linearity of the driver. The output power to power dissipation ratio is 3.6%. To the best knowledge of the authors, this is the first time a linear driver for optical modulators demonstrates such bandwidth.


title: On a connection between importance sampling and the likelihood ratio policy gradient

abstract: Likelihood ratio policy gradient methods have been some of the most successful reinforcement learning algorithms, especially for learning on physical systems. We describe how the likelihood ratio policy gradient can be derived from an importance sampling perspective. This derivation highlights how likelihood ratio methods under-use past experience by (i) using the past experience to estimate only the gradient of the expected return U(θ) at the current policy parameterization θ, rather than to obtain a more complete estimate of U(θ), and (ii) using past experience under the current policy only rather than using all past experience to improve the estimates. We present a new policy search method, which leverages both of these observations as well as generalized baselines—a new technique which generalizes commonly used baseline techniques for policy gradient methods. Our algorithm outperforms standard likelihood ratio policy gradient algorithms on several testbeds.


title: Feature deduction and ensemble design of intrusion detection systems

abstract: Current intrusion detection systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this study is to identify important input features in building an IDS that is computationally efficient and effective. We investigated the performance of two feature selection algorithms involving Bayesian networks (BN) and Classification and Regression Trees (CART) and an ensemble of BN and CART. Empirical results indicate that significant input feature selection is important to design an IDS that is lightweight, efficient and effective for real world detection systems. Finally, we propose an hybrid architecture for combining different feature selection algorithms for real world intrusion detection.


title: Toll-like receptors: critical proteins linking innate and acquired immunity

abstract: Recognition of pathogens is mediated by a set of germline-encoded receptors that are referred to as pattern-recognition receptors (PRRs). These receptors recognize conserved molecular patterns (pathogen-associated molecular patterns), which are shared by large groups of microorganisms. Toll-like receptors (TLRs) function as the PRRs in mammals and play an essential role in the recognition of microbial components. The TLRs may also recognize endogenous ligands induced during the inflammatory response. Similar cytoplasmic domains allow TLRs to use the same signaling molecules used by the interleukin 1 receptors (IL-1Rs): these include MyD88, IL-1R--associated protein kinase and tumor necrosis factor receptor--activated factor 6. However, evidence is accumulating that the signaling pathways associated with each TLR are not identical and may, therefore, result in different biological responses.


title: Deep learning similarities from different representations of source code

abstract: Assessing the similarity between code components plays a pivotal role in a number of Software Engineering (SE) tasks, such as clone detection, impact analysis, refactoring, etc. Code similarity is generally measured by relying on manually defined or hand-crafted features, e.g., by analyzing the overlap among identifiers or comparing the Abstract Syntax Trees of two code components. These features represent a best guess at what SE researchers can utilize to exploit and reliably assess code similarity for a given task. Recent work has shown, when using a stream of identifiers to represent the code, that Deep Learning (DL) can effectively replace manual feature engineering for the task of clone detection. However, source code can be represented at different levels of abstraction: identifiers, Abstract Syntax Trees, Control Flow Graphs, and Bytecode. We conjecture that each code representation can provide a different, yet orthogonal view of the same code fragment, thus, enabling a more reliable detection of similarities in code. In this paper, we demonstrate how SE tasks can benefit from a DL-based approach, which can automatically learn code similarities from different representations.


title: Learning about failure: bankruptcy, firm age, and the resourcebased view

abstract: Systematic differences in the determinants of firm failure between firms that fail early in their life and those that fail after having successfully negotiated the early liabilities of newness and adolescence are identified. Analysis of data from 339 Canadian corporate bankruptcies suggests that failure among younger firms may be attributable to deficiencies in managerial knowledge and financial management abilities. Failure among older firms, on the other hand, may be attributable to an inability to adapt to environmental change.


title: Testing for baseline balance in clinical trials

abstract: Once the data from a clinical trial are available for analysis it is common practice to carry out 'tests of baseline homogeneity' on prognostic covariates before proceeding to analyse the effects of treatment on outcome variables. It is argued that this practice is philosophically unsound, of no practical value and potentially misleading. Instead it is recommended that prognostic variables be identified in the trial-plan and fitted in an analysis of covariance regardless of their baseline distribution (statistical significance).


title: SSBC 2018: Sclera Segmentation Benchmarking Competition

abstract: This paper summarises the results of the Sclera Segmentation Benchmarking Competition (SSBC 2018). It was organised in the context of the 11th IAPR International Conference on Biometrics (ICB 2018). The aim of this competition was to record the developments on sclera segmentation in the cross-sensor environment (sclera trait captured using multiple acquiring sensors). Additionally, the competition also aimed to gain the attention of researchers on this subject of research. For the purpose of benchmarking, we have developed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1), which was used in the context of the previous versions of sclera segmentation competitions. The images in the second dataset were captured using .a mobile phone rear camera of 8-megapixel. As baseline manual segmentation mask of the sclera images from both the datasets were developed. Precision and recall-based statistical measures were employed to evaluate the effectiveness of the submitted segmentation technique and to rank them. Six algorithms were submitted towards the segmentation task. This paper analyses the results produced by these algorithms/system and defines a way forward for this subject of research. Both the datasets along with some of the accompanying ground truth/baseline mask will be freely available for research purposes upon request to authors by email.


title: Pulsed time-of-flight 3D-CMOS imaging using photogate-based active pixel sensors

abstract: A novel time-of-flight (ToF) 3D-image sensor based on photogate (PG) active pixel structures fabricated in a standard 0.35µm CMOS process is presented. Distance measurements are performed using a pulsed near-infrared (λ=905nm) laser with pulse widths of 30ns to 60ns for distance measurements up to 9m. The developed ToF pixel consists of a photogate (A PG =30x30µm2) and four floating diffusion (FD) readout nodes, which enable the detection of reflected laser pulse delay and efficient ambient light suppression. Our fabricated sensor contains 4x16 pixels and exhibits a dynamic range of 56dB and a noise equivalent power of 4.46W/m2 using a single laser pulse.


title: Why the Data Train Needs Semantic Rails

abstract: While catchphrases such as big data, smart data, dataintensive science, or smart dust highlight different aspects, they share a common theme: Namely, a shift towards a data-centric perspective in which the synthesis and analysis of data at an ever-increasing spatial, temporal, and thematic resolution promises new insights, while, at the same time, reducing the need for strong domain theories as starting points. In terms of the envisioned methodologies, those catchphrases tend to emphasize the role of predictive analytics, i.e., statistical techniques including data mining and machine learning, as well as supercomputing. Interestingly, however, while this perspective takes the availability of data as a given, it does not answer the question how one would discover the required data in today’s chaotic information universe, how one would understand which datasets can be meaningfully integrated, and how to communicate the results to humans and machines alike. The Semantic Web addresses these questions. In the following, we argue why the data train needs semantic rails. We point out that making sense of data and gaining new insights works best if inductive and deductive techniques go hand-in-hand instead of competing over the prerogative of interpretation.


title: A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure

abstract: Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R2), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.


title: Automatic Depth Extraction from 2D Images Using a Cluster-Based Learning Framework

abstract: There has been a significant increase in the availability of 3D players and displays in the last years. Nonetheless, the amount of 3D content has not experimented an increment of such magnitude. To alleviate this problem, many algorithms for converting images and videos from 2D to 3D have been proposed. Here, we present an automatic learning-based 2D-3D image conversion approach, based on the key hypothesis that color images with similar structure likely present a similar depth structure. The presented algorithm estimates the depth of a color query image using the prior knowledge provided by a repository of color + depth images. The algorithm clusters this database attending to their structural similarity, and then creates a representative of each color-depth image cluster that will be used as prior depth map. The selection of the appropriate prior depth map corresponding to one given color query image is accomplished by comparing the structural similarity in the color domain between the query image and the database. The comparison is based on a K-Nearest Neighbor framework that uses a learning procedure to build an adaptive combination of image feature descriptors. The best correspondences determine the cluster, and in turn the associated prior depth map. Finally, this prior estimation is enhanced through a segmentation-guided filtering that obtains the final depth map estimation. This approach has been tested using two publicly available databases, and compared with several state-of-the-art algorithms in order to prove its efficiency.


title: IMPROVED BRASSICA NAPUS L., REGENERATION FROM HYPOCOTYLS USING THIDIAZURON AND BENZYLADENINE AS CYTOKININ SOURCES

abstract: A reproducible system to produce regenerated Brassica napus L., plants has been developed using hypocotyl explants. Different concentrations of Benzyladenine (1.5, 3.0 and 4.5 mg l -1 ) and Thidiazuron (0, 0.15 and 0.30 mg l -1 ) were evaluated for shoot regeneration using 7, 14 and 21 day old hypocotyl explants. Treatments were arranged in a factorial experiment as a randomized complete block design with three replications. Significant differences were found in the interactions of Benzyladenine and Thidiazuron concentrations as well as the ages of the explants. Maximum shoot regeneration was obtained using 4.5 mg l -1 Benzyladenine and 0.3 mg l -1 Thidiazuron. Shoot regeneration was highly affected by the age of the explants. It was found that 21 day old explants were more likely to undergo shoot development than the others. Under these culture conditions, the highest percentage of shoot regeneration was 174.0% for hypocotyl explants. Regenerated shoots rooted when cultured on a root induction medium supplemented with 2 mg l -1 of Indolebutyric acid. Rooted plantlets were successfully established in the soil which developed normal fertile flowers and viable seeds. In light of its efficiency, this hypocotyl regeneration method could be a suitable tool for genetic transformation using Agrobacterium.


title: Ruminations and Flow: Why Do People with a More Harmonious Passion Experience Higher Well-Being?

abstract: Research shows that harmonious and obsessive passion are positively and negatively linked to well-being respectively (e.g., Vallerand et al. in J Personal 75:505–534, 2007; Psychol Sport Exerc 9:373–392, 2008). The present study investigated the psychological mechanisms underlying the different impact of the two types of passion on well-being. A theoretical model involving passion, ruminations, flow experiences, and well-being was tested. Results showed that the more people have a harmonious passion, the more they tend to experience flow in their favorite activity, which in turn predicts higher well-being. Obsessive passion did not seem to be systematically linked to flow in the favorite activity. In contrast, the more people have an obsessive passion, the more they tend to ruminate about their passionate activity while engaging in another activity, which did not seem to be systematically the case for people with a harmonious passion. These ruminations are negatively related to flow experiences in the other activity, which are positively associated with well-being. Flow and ruminations thus seem to contribute to the understanding of the link between passion and well-being.


title: Location Privacy in Mobile Systems: A Personalized Anonymization Model

abstract: This paper describes a personalized k-anonymity model for protecting location privacy against various privacy threats through location information sharing. Our model has two unique features. First, we provide a unified privacy personalization framework to support location k-anonymity for a wide range of users with context-sensitive personalized privacy requirements. This framework enables each mobile node to specify the minimum level of anonymity it desires as well as the maximum temporal and spatial resolutions it is willing to tolerate when requesting for k-anonymity preserving location-based services (LBSs). Second, we devise an efficient message perturbation engine which runs by the location protection broker on a trusted server and performs location anonymization on mobile users' LBS request messages, such as identity removal and spatio-temporal cloaking of location information. We develop a suite of scalable and yet efficient spatio-temporal cloaking algorithms, called CliqueCloak algorithms, to provide high quality personalized location k-anonymity, aiming at avoiding or reducing known location privacy threats before forwarding requests to LBS provider(s). The effectiveness of our CliqueCloak algorithms is studied under various conditions using realistic location data synthetically generated using real road maps and traffic volume data


title: Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention

abstract: Image denoising is always a challenging task in the field of computer vision and image processing. In this paper, we have proposed an encoder-decoder model with direct attention, which is capable of denoising and reconstruct highly corrupted images. Our model consists of an encoder and a decoder, where the encoder is a convolutional neural network and decoder is a multilayer Long Short-Term memory network. In the proposed model, the encoder reads an image and catches the abstraction of that image in a vector, where decoder takes that vector as well as the corrupted image to reconstruct a clean image. We have trained our model on MNIST handwritten digit database after making lower half of every image as black as well as adding noise top of that. After a massive destruction of the images where it is hard for a human to understand the content of those images, our model can retrieve that image with minimal error. Our proposed model has been compared with convolutional encoder-decoder, where our model has performed better at generating missing part of the images than convolutional autoencoder.


title: Efficient Exploration for Dialog Policy Learning with Deep BBQ Networks \& Replay Buffer Spiking.

abstract: When rewards are sparse and action spaces large, Q-learning with $\epsilon$-greedy exploration can be inefficient. This poses problems for otherwise promising applications such as task-oriented dialogue systems, where the primary reward signal, indicating successful completion of a task, requires a complex sequence of appropriate actions. Under these circumstances, a randomly exploring agent might never stumble upon a successful outcome in reasonable time. We present two techniques that significantly improve the efficiency of exploration for deep Q-learning agents in dialogue systems. First, we introduce an exploration technique based on Thompson sampling, drawing Monte Carlo samples from a Bayes-by-backprop neural network, demonstrating marked improvement over common approaches such as $\epsilon$-greedy and Boltzmann exploration. Second, we show that spiking the replay buffer with experiences from a small number of successful episodes, as are easy to harvest for dialogue tasks, can make Q-learning feasible when it might otherwise fail.


title: Hot and crispy: CRISPR–Cas systems in the hyperthermophileSulfolobus solfataricus

abstract: The CRISPR (clustered regularly interspaced short palindromic repeats) and Cas (CRISPR-associated) genes are widely spread in bacteria and archaea, representing an intracellular defence system against invading viruses and plasmids. In the system, fragments from foreign DNA are captured and integrated into the host genome at the CRISPR locus. The locus is transcribed and the resulting RNAs are processed by Cas6 into small crRNAs (CRISPR RNAs) that guide a variety of effector complexes to degrade the invading genetic elements. Many bacteria and archaea have one major type of effector complex. However, Sulfolobus solfataricus strain P2 has six CRISPR loci with two families of repeats, four cas6 genes and three different types of effector complex. These features make S. solfataricus an important model for studying CRISPR–Cas systems. In the present article, we review our current understanding of crRNA biogenesis and its effector complexes, subtype I-A and subtype III-B, in S. solfataricus . We also discuss the differences in terms of mechanisms between the subtype III-B systems in S. solfataricus and Pyrococcus furiosus .


title: Socially aware motion planning with deep reinforcement learning

abstract: For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.


title: The CL-SciSumm Shared Task 2018: Results and Key Insights

abstract: This overview describes the official results of the CL-SciSumm Shared Task 2018 -- the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain. This year, the dataset comprised 60 annotated sets of citing and reference papers from the open access research papers in the CL domain. The Shared Task was organized as a part of the 41st Annual Conference of the Special Interest Group in Information Retrieval (SIGIR), held in Ann Arbor, USA in July 2018. We compare the participating systems in terms of two evaluation metrics. The annotated dataset and evaluation scripts can be accessed and used by the community from: \url{this https URL}.


title: The Capital Asset Pricing Model: Theory and Evidence

abstract: The capital asset pricing model (CAPM) of William Sharpe (1964) and John Lintner (1965) marks the birth of asset pricing theory (resulting in a Nobel Prize for Sharpe in 1990). Before their breakthrough, there were no asset pricing models built from first principles about the nature of tastes and investment opportunities and with clear testable predictions about risk and return. Four decades later, the CAPM is still widely used in applications, such as estimating the cost of equity capital for firms and evaluating the performance of managed portfolios. And it is the centerpiece, indeed often the only asset pricing model taught in MBA level investment courses. ::: ::: The attraction of the CAPM is its powerfully simple logic and intuitively pleasing predictions about how to measure risk and about the relation between expected return and risk. Unfortunately, perhaps because of its simplicity, the empirical record of the model is poor - poor enough to invalidate the way it is used in applications. The model's empirical problems may reflect true failings. (It is, after all, just a model.) But they may also be due to shortcomings of the empirical tests, most notably, poor proxies for the market portfolio of invested wealth, which plays a central role in the model's predictions. We argue, however, that if the market proxy problem invalidates tests of the model, it also invalidates most applications, which typically borrow the market proxies used in empirical tests. ::: ::: For perspective on the CAPM's predictions about risk and expected return, we begin with a brief summary of its logic. We then review the history of empirical work on the model and what it says about shortcomings of the CAPM that pose challenges to be explained by more complicated models.


title: Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals

abstract: Consider mutli-goal tasks that involve static environments and dynamic goals. Examples of such tasks, such as goal-directed navigation and pick-and-place in robotics, abound. Two types of Reinforcement Learning (RL) algorithms are used for such tasks: model-free or model-based. Each of these approaches has limitations. Model-free RL struggles to transfer learned information when the goal location changes, but achieves high asymptotic accuracy in single goal tasks. Model-based RL can transfer learned information to new goal locations by retaining the explicitly learned state-dynamics, but is limited by the fact that small errors in modelling these dynamics accumulate over long-term planning. In this work, we improve upon the limitations of model-free RL in multi-goal domains. We do this by adapting the Floyd-Warshall algorithm for RL and call the adaptation Floyd-Warshall RL (FWRL). The proposed algorithm learns a goal-conditioned action-value function by constraining the value of the optimal path between any two states to be greater than or equal to the value of paths via intermediary states. Experimentally, we show that FWRL is more sample-efficient and learns higher reward strategies in multi-goal tasks as compared to Q-learning, model-based RL and other relevant baselines in a tabular domain.


title: Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model

abstract: A significant roadblock in multilingual neural language modeling is the lack of labeled non-English data. One potential method for overcoming this issue is learning cross-lingual text representations that can be used to transfer the performance from training on English tasks to non-English tasks, despite little to no task-specific non-English data. In this paper, we explore a natural setup for learning cross-lingual sentence representations: the dual-encoder. We provide a comprehensive evaluation of our cross-lingual representations on a number of monolingual, cross-lingual, and zero-shot/few-shot learning tasks, and also give an analysis of different learned cross-lingual embedding spaces.


title: U-Net: Machine Reading Comprehension with Unanswerable Questions

abstract: Machine reading comprehension with unanswerable questions is a new challenging task for natural language processing. A key subtask is to reliably predict whether the question is unanswerable. In this paper, we propose a unified model, called U-Net, with three important components: answer pointer, no-answer pointer, and answer verifier. We introduce a universal node and thus process the question and its context passage as a single contiguous sequence of tokens. The universal node encodes the fused information from both the question and passage, and plays an important role to predict whether the question is answerable and also greatly improves the conciseness of the U-Net. Different from the state-of-art pipeline models, U-Net can be learned in an end-to-end fashion. The experimental results on the SQuAD 2.0 dataset show that U-Net can effectively predict the unanswerability of questions and achieves an F1 score of 71.7 on SQuAD 2.0.


title: Cannabidiol: A Promising Drug for Neurodegenerative Disorders?

abstract: Neurodegenerative diseases represent, nowadays, one of the main causes of death in the industrialized country. They are characterized by a loss of neurons in particular regions of the nervous system. It is believed that this nerve cell loss underlies the subsequent decline in cognitive and motor function that patients experience in these diseases. A range of mutant genes and environmental toxins have been implicated in the cause of neurodegenerative disorders but the mechanism remains largely unknown. At present, inflammation, a common denominator among the diverse list of neurodegenerative diseases, has been implicated as a critical mechanism that is responsible for the progressive nature of neurodegeneration. Since, at present, there are few therapies for the wide range of neurodegenerative diseases, scientists are still in search of new therapeutic approaches to the problem. An early contribution of neuroprotective and antiinflammatory strategies for these disorders seems particularly desirable because isolated treatments cannot be effective. In this contest, marijuana derivatives have attracted special interest, although these compounds have always raised several practical and ethical problems for their potential abuse. Nevertheless, among Cannabis compounds, cannabidiol (CBD), which lacks any unwanted psychotropic effect, may represent a very promising agent with the highest prospect for therapeutic use.


title: Does past success lead analysts to become overconfident

abstract: This paper provides evidence that analysts who have predicted earnings more accurately than the median analyst in the previous four quarters tend to be simultaneously less accurate and further from the consensus forecast in their subsequent earnings prediction. This phenomenon is economically and statistically meaningful. The results are robust to different estimation techniques and different control variables. Our findings are consistent with an attribution bias that leads analysts who have experienced a short-lived success to become overconfident in their ability to forecast future earnings.


title: Adaptive Filtering for Color Image Sharpening and Denoising

abstract: Both tasks of sharpening and denoising color images are eternity problems for improving image quality. This paper proposes a filtering algorithm which brings a dramatic improvement in sharpening effects and reducing flat-area noises for natural color images. First, we generate a luminance slope map, which classifies a color image into the edge areas and the flat areas with uniform color. Second, the Gaussian derivative filter is applied to sharpening each segmented edge area. In order to keep color balance, the edge-sharpening filter is applied only to the luminance component of the color image. In the flat areas, a filter based on the SUSAN algorithm is applied to reducing the background noises with preserving sharpened edges. The performance of the proposed method is verified for natural image with Gaussian noise and impulse noise.


title: Service-Centric End-to-End Abstractions in Next-Generation Networks

abstract: Next-generation network architectures will be governed by the need for flexibility. Heterogeneous end-systems, novel communication abstractions, and security and manageability challenges will require networks to provide a broad range of services that go beyond the simple store-and-forward capabilities of today's Internet. This paper introduces new abstractions for information transfer and data services in the network, which overcome the constraints of the end-to-end argument that has dominated current network designs. The explicit separation of communication and processing allows the composition of a variety of information transfer patterns that expand the capabilities of the network to meet its next-generation challenges. We discuss implementation issues that arise in this network architecture and present several examples of how applications can utilize the proposed abstractions. This present a first step towards a unified view of the convergence of networking, processing, and their distributed applications.


Lip Reading in Profile


title: A Phase-Based Technique for Localization of UHF-RFID Tags Moving on a Conveyor Belt: Performance Analysis and Test-Case Measurements

abstract: A new phase-based technique for localization and tracking of items moving along a conveyor belt and equipped with ultrahigh frequency-radio frequency identification (UHF-RFID) tags is described and validated here. The technique is based on a synthetic-array approach that takes advantage of the fact that the tagged items move along a conveyor belt whose speed and path are known a priori. In this framework, a joint use is done of synthetic-array radar principles, knowledge-based processing, and efficient exploitation of the reader-tag communication signal. The technique can be easily implemented in any conventional reader based on an in-phase and quadrature receiver and it does not require any modification of the reader antenna configurations usually adopted in UHF-RFID portals. Numerical results are used to investigate the performance analysis of such methods, and also to furnish system design guidelines. Finally, the localization capability is also demonstrated through a measurement campaign in a real conveyor belt scenario, showing that a centimeter-order accuracy in the tag position estimation can be achieved even in a rich multipath environment.


title: Dynamics of Hydrology on the Physico-Chemical Water Quality Parameters and Trophic State of Lake Baringo, Kenya

abstract: Lake Baringo is a freshwater lake situated in the Kenyan Great Rift Valley, characterized by a polymictic regime with southern inlets but without any visible outlet. How the inflowing rivers and the rise in lake water levels affect water quality is still unclear. In this study, samples were collected from May 2014 to June 2015 at sites S2, C1, C2, C3 and N2: S denoted southern part, C central region and N northern part; and analysed for a wide range of physical and chemical parameters and the trophic state of the lake also determined. During 2017, samples were collected from nine stations at sites designated inlet (S1, S2 and S3), Lake Centre (C1, C2 and C3) and offshore (N1, N2 and N3). Spatial differences were determined by Analysis of Variance (ANOVA). All the physicochemical parameters (i.e. for 2017) demonstrated significant (P <0.05) spatial variations. Equally, spatial-temporal differences were noted for one year study period of June 2014 to May 2015 (P <0.05). Data collected for one year indicated a somewhat different trend from what was witnessed on data of 2017. Stations that demonstrated high values of the measured parameters during the 2017 sampling (i.e. temperature for S2, 26.8oC) recorded reduced levels in the one year sampling (26.0oC). However, results from the 2017 study period showed variation from one station to another (p<0.05). The ecological status of the lake as measured by the water quality studies carried out in the present study has changed in comparison with the previous studies because the general trend is indicative of an improved system due to rise in water levels. The study recommends regular monitoring in order to discern the erratic changes witnessed with the changes in water levels leading to deviation of the physico-chemical water quality parameters from the past studies.


title: e-NSP: efficient negative sequential pattern mining based on identified positive patterns without database rescanning

abstract: Mining Negative Sequential Patterns (NSP) is much more challenging than mining Positive Sequential Patterns (PSP) due to the high computational complexity and huge search space required in calculating Negative Sequential Candidates (NSC). Very few approaches are available for mining NSP, which mainly rely on re-scanning databases after identifying PSP. As a result, they are very inefficient. In this paper, we propose an efficient algorithm for mining NSP, called e-NSP, which mines for NSP by only involving the identified PSP, without re-scanning databases. First, negative containment is defined to determine whether or not a data sequence contains a negative sequence. Second, an efficient approach is proposed to convert the negative containment problem to a positive containment problem. The supports of NSC are then calculated based only on the corresponding PSP. Finally, a simple but efficient approach is proposed to generate NSC. With e-NSP, mining NSP does not require additional database scans, and the existing PSP mining algorithms can be integrated into e-NSP to mine for NSP efficiently. e-NSP is compared with two currently available NSP mining algorithms on 14 synthetic and real-life datasets. Intensive experiments show that e-NSP takes as little as 3% of the runtime of the baseline approaches and is applicable for efficient mining of NSP in large datasets.


title: Animation wrinkling: augmenting coarse cloth simulations with realistic-looking wrinkles

abstract: Moving garments and other cloth objects exhibit dynamic, complex wrinkles. Generating such wrinkles in a virtual environment currently requires either a time-consuming manual design process, or a computationally expensive simulation, often combined with accurate parameter-tuning requiring specialized animator skills. Our work presents an alternative approach for wrinkle generation which combines coarse cloth animation with a post-processing step for efficient generation of realistic-looking fine dynamic wrinkles. Our method uses the stretch tensor of the coarse animation output as a guide for wrinkle placement. To ensure temporal coherence, the placement mechanism uses a space-time approach allowing not only for smooth wrinkle appearance and disappearance, but also for wrinkle motion, splitting, and merging over time. Our method generates believable wrinkle geometry using specialized curve-based implicit deformers. The method is fully automatic and has a single user control parameter that enables the user to mimic different fabrics.


title: The application of spark gaps on audio jack for ESD protection

abstract: ESD strikes can be suppressed by placing ESD protection devices along ESD current paths. As primary ESD protection on PCBs, spark gaps are cheap and take little space, but the breakdown voltage is not low enough and the lag time can be too long to protect the circuit. The effect of adding carbon and non linear polymers to the spark gap is investigated in this paper.


title: Co-KV: A Collaborative Key-Value Store Using Near-Data Processing to Improve Compaction for the LSM-tree

abstract: Log-structured merge tree (LSM-tree) based key-value stores are widely employed in large-scale storage systems. In the compaction of the key-value store, SSTables are merged with overlapping key ranges and sorted for data queries. This, however, incurs write amplification and thus degrades system performance, especially under update-intensive workloads. Current optimization focuses mostly on the reduction of the overload of compaction in the host, but rarely makes full use of computation in the device. To address these issues, we propose Co-KV, a Collaborative Key-Value store between the host and a near-data processing ( i.e., NDP) model based SSD to improve compaction. Co-KV offers three benefits: (1) reducing write amplification by a compaction offloading scheme between host and device; (2) relieving the overload of compaction in the host and leveraging computation in the SSD based on the NDP model; and (3) improving the performance of LSM-tree based key-value stores under update-intensive workloads. Extensive db_bench experiment show that Co-KV largely achieves a 2.0x overall throughput improvement, and a write amplification reduction by up to 36.0% over the state-of-the-art LevelDB. Under YCSB workloads, Co-KV increases the throughput by 1.7x - 2.4x while decreases the write amplification and average latency by up to 30.0% and 43.0%, respectively.


title: Jointly Embedding Entities and Text with Distant Supervision

abstract: Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications. However, recent entity embedding methods have relied on structured resources that are expensive to create for new domains and corpora. We present a distantly-supervised method for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of mappings between entities and surface forms. We learn embeddings from open-domain and biomedical corpora, and compare against prior methods that rely on human-annotated text or large knowledge graph structure. Our embeddings capture entity similarity and relatedness better than prior work, both in existing biomedical datasets and a new Wikipedia-based dataset that we release to the community. Results on analogy completion and entity sense disambiguation indicate that entities and words capture complementary information that can be effectively combined for downstream use.


title: Deep Sparse Autoencoders for Football Match and Bet Prediction

abstract: In recent years, betting in Association Football has become increasingly popular. A lot of research has been done on the subject using statistical and other approaches to create predictive models. Correctly forecasting a match outcome is a difficult and complex problem to tackle, due to the nature of the sport. There is a plethora of data to work with, such as odds by bookmakers, team stats, players' performance, etc. With the recent resurgence of Artificial Neural Networks, we try to apply their predictive ability to this problem using Sparse Deep Autoencoders, taking advantage of the odds given by the bookmakers. Our main objective is to get the best possible predictions of the probabilities of each one of the three outcomes of a football match. From that, we can either bet on the highest probability or use the probabilities to calculate the expected value of each bet, i.e. the expected monetary return of it, aiming for the highest accuracy possible while simultaneously making a profit at the end of each season, all the while following a consistent betting strategy.


title: Evaluation is a Dynamic Process : Moving Beyond Dual System Models

abstract: Over the past few decades, dual attitude ⁄process ⁄system models have emerged as the dominant framework for understanding a wide range of psychological phenomena. Most of these models characterize the unconscious and conscious mind as being built from discrete processes or systems: one that is reflexive, automatic, fast, affective, associative, and primitive, and a second that is deliberative, controlled, slow, cognitive, propositional, and more uniquely human. Although these models serve as a useful heuristic for characterizing the human mind, recent developments in social and cognitive neuroscience suggest that the human evaluative system, like most of cognition, is widely distributed and highly dynamic. Integrating these advances with current attitude theories, we review how the recently proposed Iterative Reprocessing Model can account for apparent dual systems as well as discrepancies between traditional dual system models and recent research revealing the dynamic nature of evaluation. Furthermore, we describe important implications this dynamical system approach has for various social psychological domains.


title: Local navigation strategies for a team of robots

abstract: Whenever a mobile robot has to deal with an environment that is totally or partially unknown or dynamically changing, local navigation strategies are very important for the robot to successfully achieve its goals. Unfortunately, local navigation algorithms that have been proposed in the literature offer poor performance (or even fail) whenever the geometry of the free space in which the robot is requested to operate increases its complexity. In this paper, we deal with a team composed of many robots, and we show how robots navigating within an unknown environment with local communication capabilities (only line-of-sight communication is allowed) can cooperate by helping each other to achieve their own goals.


title: A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes

abstract: We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the model, each cell has a low-dimensional latent representation. Additional latent variables account for technical effects that may erroneously set some observations of gene expression levels to zero. Conditional distributions are specified by neural networks, giving the proposed model enough flexibility to fit the data well. We use variational inference and stochastic optimization to approximate the posterior distribution. The inference procedure scales to over one million cells, whereas competing algorithms do not. Even for smaller datasets, for several tasks, the proposed procedure outperforms state-of-the-art methods like ZIFA and ZINB-WaVE. We also extend our framework to take into account batch effects and other confounding factors and propose a natural Bayesian hypothesis framework for differential expression that outperforms tradition DESeq2.


title: MR-ELM: a MapReduce-based framework for large-scale ELM training in big data era

abstract: In the big data era, extreme learning machine (ELM) can be a good solution for the learning of large sample data as it has high generalization performance and fast training speed. However, the emerging big and distributed data blocks may still challenge the method as they may cause large-scale training which is hard to be finished by a common commodity machine in a limited time. In this paper, we propose a MapReduce-based distributed framework named MR-ELM to enable large-scale ELM training. Under the framework, ELM submodels are trained parallelly with the distributed data blocks on the cluster and then combined as a complete single-hidden layer feedforward neural network. Both classification and regression capabilities of MR-ELM have been theoretically proven, and its generalization performance is shown to be as high as that of the original ELM and some common ELM ensemble methods through many typical benchmarks. Compared with the original ELM and the other parallel ELM algorithms, MR-ELM is a general and scalable ELM training framework for both classification and regression and is suitable for big data learning under the cloud environment where the data are usually distributed instead of being located in one machine.


title: SHOP2: An HTN Planning System

abstract: The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.


title: The Effect of Consumer Privacy Empowerment on Trust and Privacy Concerns in E-Commerce

abstract: Privacy concerns and a lack of trust have been shown to reduce consumer's willingness to transact with an online vendor. Understandably, firms are searching for methods to reduce consumer privacy concerns and increase trust. In this study, we introduce a new construct-consumer privacy empowerment. We then propose and test a theoretical model that examines the relationship between consumer privacy empowerment, familiarity, privacy concern and trust. Results indicate support for the model and suggest that perceived privacy empowerment has a strong influence on both privacy concern and trust in e-commerce.


title: Reading Scene Text in Deep Convolutional Sequences

abstract: We develop a Deep-Text Recurrent Network (DTRN) that regards scene text reading as a sequence labelling problem. We leverage recent advances of deep convolutional neural networks to generate an ordered high-level sequence from a whole word image, avoiding the difficult character segmentation problem. Then a deep recurrent model, building on long short-term memory (LSTM), is developed to robustly recognize the generated CNN sequences, departing from most existing approaches recognising each character independently. Our model has a number of appealing properties in comparison to existing scene text recognition methods: (i) It can recognise highly ambiguous words by leveraging meaningful context information, allowing it to work reliably without either pre- or post-processing; (ii) the deep CNN feature is robust to various image distortions; (iii) it retains the explicit order information in word image, which is essential to discriminate word strings; (iv) the model does not depend on pre-defined dictionary, and it can process unknown words and arbitrary strings. Codes for the DTRN will be available.


title: Perfect matching quad layouts for manifold meshes

abstract: This paper introduces a new approach to automatically generate pure quadrilateral patch layouts on manifold meshes. The algorithm is based on a careful construction of a singularity graph of a given input frame field or a given periodic global parameterization. A pure quadrilateral patch layout is then derived as a constrained minimum weight perfect matching of that graph. The resulting layout is optimal relative to a balance between coarseness and geometric feature alignment. We formulate the problem of finding pure quadrilateral patch layouts as a global optimization problem related to a well-known concept in graph theory. The main advantage of the new method is its simplicity and its computation speed. Patch layouts generated by the present algorithm are high quality and are very competitive compared to current state of the art.


Printing Food


title: BACON.5: The Discovery of Conservation Laws

abstract: BACON.5 is a program that discovers empirical laws. The program represents information at varying levels of description, with higher levels summarizing the levels below them. The system applies a small set of data-driven heuristics to detect regularities in numeric and nominal data. These heuristics note constancies and trends, leading BACONS to formulate hypotheses, define theoretical terms, and postulate intrinsic properties. Once the program has formulated an hypothesis, it' uses this to reduce the amount of data it must consider at later times. A simple type of reasoning by analogy also simplifies the discovery of laws containing symmetric forms. These techniques have allowed the system to rediscover Snail's law of refraction, conservation of momentum, Black's specific heat law, and Joule's formulation of conservation of energy. Thus, BACON.S'S heuristics appear to be general mechanisms applicable to discovery in diverse domains.


title: The application of soil temperature measurement by LM35 temperature sensors

abstract: Consider revising medium temperature used LM35 temperature sensor, what is an economic and feasible method. This study mainly researches the applicability of LM35 temperature sensor in soil temperature testing field. Selected the sensor, and based on the theoretical equation between the sensor output voltage and Celsius temperature; introduced correction coefficient, carried through the calibration experiment of the sensor; further more, it is applied to the potted rice's soil temperature detection. The calibration results show that, each sensor correction coefficient is different from others, but these numerical are close to 1, the linear relationship was very significant between tested medium temperature and sensor output voltage. In the key trial period of rice potted, used LM35DZ type temperature sensor to measure the soil temperature. The analysis result show that, the changing trends are basically equal both soil temperature and air temperature, and the characteristics of soil temperatures are lag. The variance analysis shows that, the difference was not significant paper film covered and without covered on soil temperature.


title: Medical Diagnosis using Back propagation Algorithm

abstract: Artificial Intelligence has always lent a helping hand to the practitioners of medicine for improving medical diagnosis and treatment then, paradigm of artificial neural networks is shortly introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described. A lot of Applications tried to help human experts, offering a solution. This paper describes a optimal feed forward Back propagation algorithm. Feed- forward back propagation neural network is used as a classifier to distinguish between infected or non-infected person in both cases. However, Traditional Back propagation algorithm has many shortcomings. Learning often takes long time to converge, and it may fall into local minima. One of the possible remedies to escape from local minima is by using a very small learning rate, which slows down the learning process. The back propagation algorithm presented in this paper used for training depends on a multilayer neural network with a very small learning rate, especially when using a large training set size. It can be applied in a generic manner for any network size that uses a back propagation algorithm and achieved the best performance with the minimum epoch (training iterations) and training time.


title: Automatic Optic Disc Abnormality Detection in Fundus Images: A Deep Learning Approach

abstract: Optic disc (OD) is a key structure in retinal images. It serves as an indicator to detect various diseases such as glaucoma and changes related to new vessel formation on the OD in diabetic retinopathy (DR) or retinal vein occlusion. OD is also essential to locate structures such as the macula and the main vascular arcade. Most existing methods for OD localization are rule-based, either exploiting the OD appearance proper- ties or the spatial relationship between the OD and the main vascular arcade. The detection of OD abnormalities has been performed through the detection of lesions such as hemorrhaeges or through measuring cup to disc ratio. Thus these methods result in complex and in exible im- age analysis algorithms limiting their applicability to large image sets obtained either in epidemiological studies or in screening for retinal or optic nerve diseases. In this paper, we propose an end-to-end supervised model for OD abnormality detection. The most informative features of the OD are learned directly from retinal images and are adapted to the dataset at hand. Our experimental results validated the effectiveness of this current approach and showed its potential application.


title: Unsupervised incremental learning for improved object detection in a video

abstract: Most common approaches for object detection collect thousands of training examples and train a detector in an offline setting, using supervised learning methods, with the objective of obtaining a generalized detector that would give good performance on various test datasets. However, when an offline trained detector is applied on challenging test datasets, it may fail in some cases by not being able to detect some objects or by producing false alarms. We propose an unsupervised multiple instance learning (MIL) based incremental solution to deal with this issue. We introduce an MIL loss function for Real Adaboost and present a tracking based effective unsupervised online sample collection mechanism to collect the online samples for incremental learning. Experiments demonstrate the effectiveness of our approach by improving the performance of a state of the art offline trained detector on the challenging datasets for pedestrian category.


title: Bidirectional equalizer arbitrated by Viterbi decoder path metrics

abstract: A bidirectional decision feedback equalizer (DFE) is proposed with the arbitration of the Viterbi decoder. The proposed DFE uses the minimum path metric between Viterbi decoders for the forward and reverse DFEs in the arbitration process. It has over 2 dB SNR gain against the conventional bidirectional DFE and the single DFE.


title: A 20 Watt Micro-strip X-Band AlGaN/GaN HPA MMIC for Advanced Radar Applications

abstract: In this paper a first iteration design, fabrication and test of a two-stage X-Band MMIC HPA in micro-strip AlGaN/GaN technology is reported. With 20 V drain voltage operating bias point, at 3 dB compression point, the HPA delivers a pulsed output power ranging from 21 to 28.5 W, an associated gain from 12.9 to 16.5 dB and an associated PAE from circa 30% to 40%, over the 8-10.5 GHz frequency bandwidth. In the best performance frequency points (8.5 and 9 GHz) the HPA exhibits a saturated output power of 30 W with an associated PAE of 40%.


title: Detection of Application Layer DDoS attack by feature learning using Stacked AutoEncoder

abstract: An Application Layer Distributed Denial of Service Attack (DDoS) is one of the biggest concerns for web security. Many detection methods are designed to mitigate DDoS attack based on IP and TCP layer instead of the Application layer. These methods are not suitable for detection of Application layer DDoS attack since most of the IP and TCP layer DDoS attacks are based on request flooding attack. But Application layer DDoS attacks consist of request flooding, session flooding, and asymmetric attack. The solutions available to detect Application layer DDoS attack, detect only limited number of Application layer DDoS attacks. The solutions that detect all types of Application layer DDoS attacks have huge algorithm complexity. One of the major challenges in the detection of an Application layer DDoS attack is the non-availability of features to detect such attacks. Hence it is difficult to model normal user behavior from attack behavior. In this paper, Deep learning architecture is introduced to learn deep features of Application layer DDoS attack. Deep learning architecture consist of very deep neural network, typically more than three layers. In the proposed work the concept of AutoEncoder is applied, which is one of the deep learning based models that learns deep useful features in the Application layer DDoS attack dataset. The Stacked AutoEncoder deep learning architecture, is aimed to receive high level features. The performance of the proposed method was evaluated in terms of the metrics such as false positive rate and detection rate. Comparison of the proposed method with the existing methods reveals that the proposed method performs better than the existing methods.


title: Contemporary Review of Rhinoplasty

abstract: We conducted a contemporary review covering advances and trends in primary and functional rhinoplasty as published during the past decade. Specifically, we reviewed studies supporting the evidence for functional rhinoplasty, nasal valve surgery, and septal reconstruction. In addition, key articles discussing cephalic malpositioning of the lower lateral cartilages and tip contouring are reviewed. We also report studies involving lateral osteotomy techniques, computer imaging, and the use of homologous, alloplastic, and absorbable implants. When appropriate, we review outcomes data from key studies because these data are becoming increasingly important for evidence-based medicine, physician grading, and procedure reimbursement. Using evidence-based approaches whenever possible will help to ensure predictable patient outcomes.


title: Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations

abstract: Nonnegative matrix factorization (NMF) and its extensions such as Nonnegative Tensor Factorization (NTF) have become prominent techniques for blind sources separation (BSS), analysis of image databases, data mining and other information retrieval and clustering applications. In this paper we propose a family of efficient algorithms for NMF/NTF, as well as sparse nonnegative coding and representation, that has many potential applications in computational neuroscience, multi-sensory processing, compressed sensing and multidimensional data analysis. We have developed a class of optimized local algorithms which are referred to as Hierarchical Alternating Least Squares (HALS) algorithms. For these purposes, we have performed sequential constrained minimization on a set of squared Euclidean distances. We then extend this approach to robust cost functions using the alpha and beta divergences and derive flexible update rules. Our algorithms are locally stable and work well for NMF-based blind source separation (BSS) not only for the over-determined case but also for an under-determined (over-complete) case (i.e., for a system which has less sensors than sources) if data are sufficiently sparse. The NMF learning rules are extended and generalized for N-th order nonnegative tensor factorization (NTF). Moreover, these algorithms can be tuned to different noise statistics by adjusting a single parameter. Extensive experimental results confirm the accuracy and computational performance of the developed algorithms, especially, with usage of multi-layer hierarchical NMF approach [3].


title: A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables

abstract: A modular system for performing Geographic Object-Based Image Analysis (GEOBIA), using entirely open source (General Public License compatible) software, is presented based around representing objects as raster clumps and storing attributes as a raster attribute table (RAT). The system utilizes a number of libraries, developed by the authors: The Remote Sensing and GIS Library (RSGISLib), the Raster I/O Simplification (RIOS) Python Library, the KEA image format and TuiView image viewer. All libraries are accessed through Python, providing a common interface on which to build processing chains. Three examples are presented, to demonstrate the capabilities of the system: (1) classification of mangrove extent and change in French Guiana; (2) a generic scheme for the classification of the UN-FAO land cover classification system (LCCS) and their subsequent translation to habitat categories; and (3) a national-scale segmentation for Australia. The system presented provides similar functionality to existing GEOBIA packages, but is more flexible, due to its modular environment, capable of handling complex classification processes and applying them to larger datasets.


title: Using Human Computation to Acquire Novel Methods for Addressing Visual Analogy Problems on Intelligence Tests

abstract: The Raven's Progressive Matrices (RPM) test is a commonly used test of intelligence. The literature suggests a variety of problem-solving methods for addressing RPM problems. For a graduate-level artificial intelligence class in Fall 2014, we asked students to develop intelligent agents that could address 123 RPM-inspired problems, essentially crowdsourcing RPM problem solving. The students in the class submitted 224 agents that used a wide variety of problem-solving methods. In this paper, we first report on the aggregate results of those 224 agents on the 123 problems, then focus specifically on four of the most creative, novel, and effective agents in the class. We find that the four agents, using four very different problem-solving methods, were all able to achieve significant success. This suggests the RPM test may be amenable to a wider range of problem-solving methods than previously reported. It also suggests that human computation might be an effective strategy for collecting a wide variety of methods for creative tasks.


title: Exploring large scale data for multimedia QA: an initial study

abstract: With the explosive growth of multimedia contents on the internet, multimedia search has become more and more important. However, users are often bewildered by the vast quantity of information content returned by the search engines. In this scenario, Multimedia Question-Answering (MMQA) emerges as a way to return precise answers by leveraging advanced media content and linguistic analysis as well as domain knowledge. This paper performs an initial study on exploring large scale data for MMQA. First, we construct a web video dataset and discuss its query strategy, statistics, feature description and groundtruth. We then conduct experiments based on the dataset to answer definition event questions using three schemes. We finally conclude the study with discussion for future work.


title: Threat Assessment in the Cloud Environment: A Quantitative Approach for Security Pattern Selection

abstract: Cloud computing has emerged as a fast-growing technology in the past few years. It provides a great flexibility for storing, sharing and delivering data over the Internet without investing on new technology or resources. In spite of the development and wide array of cloud usage, security perspective of cloud computing still remains its infancy. Security challenges faced by cloud environment becomes more complicated when we include various stakeholders' perspectives. In a cloud environment, security perspectives and requirements are usually designed by software engineers or security experts. Sometimes clients' requirements are either ignored or given a very high importance. In order to implement cloud security by providing equal importance to client organizations, software engineers and security experts, we propose a new methodology in this paper. We use Microsoft's STRIDE-DREAD model to assess threats existing in the cloud environment and also to measure its consequences. Our aim is to rank the threats based on the nature of its severity, and also giving a significant importance for clients' requirements on security perspective. Our methodology would act as a guiding tool for security experts and software engineers to proceed with securing process especially for a private or a hybrid cloud. Once threats are ranked, we provide a link to a well-known security pattern classification. Although we have some security pattern classification schemes in the literature, we need a methodology to select a particular category of patterns. In this paper, we provide a novel methodology to select a set of security patterns for securing a cloud software. This methodology could aid a security expert or a software professional to assess the current vulnerability condition and prioritize by also including client's security requirements in a cloud environment.


title: Customer Churn Prediction Modelling Based on Behavioural Patterns Analysis using Deep Learning

abstract: Customer churn refers to when a customer ceases their relationship with a company. A churn rate, used to estimate growth, is now considered as important a metric as financial profit. With growing competition in the market, companies are desperate to keep the churn rate as low as possible. Thus, churn prediction has gained critical importance, not just for existing customers, but also for predicting trends of future customers. This paper demonstrates prediction of churn on a Telco dataset using a Deep Learning Approach. A multilayered Neural Network was designed to build a non-linear classification model. The churn prediction model works on customer features, support features, usage features and contextual features. The possibility of churn as well as the determining factors are predicted. The trained model then applies the final weights on these features and predict the possibility of churn for that customer. An accuracy of 80.03% was achieved. Since the model also provides the churn factors, it can be used by companies to analyze the reasons for these factors and take steps to eliminate them.


Irritable Bowel Syndrome


title: Novel 2D porous carbon nanosheet derived from biomass: Ultrahigh porosity and excellent performances toward V2+/V3+ redox reaction for vanadium redox flow battery

abstract: Abstract In this paper, biomass-derived carbon-based nanosheet from a kind of domestic waste (fish scales) has been successfully prepared as catalysts with high electrocatalytic activity for V2+/V3+ couple of vanadium redox flow battery (VRFB) using hydrothermal, lyophilization, and carbonization treatments. KOH is employed as template and activator, which results in nanosheet structure, ultrahigh porosity, and introduction of oxygen-containing groups. The obtained catalyst (FSC-K) exhibits the advantages of large surface area, many active sites and excellent wettability. FSC-K can effectively reduce electrochemical polarization of electrode reaction and accelerate mass transfer of active species. The cell using FSC-K demonstrates a high energy storage efficiency. Compared with pristine cell, the discharge capacity of FSC-K modified cell is 67.2 mA h at 150 mA cm−2, increased by 18 mA h, and the corresponding energy efficiency reaches 63.8%, enhanced by 7.9%. The capacity retention of FSC-K modified cell keeps 83.1% after 50 cycles at 75 mA cm−2, 16.7% larger than that for pristine cell. This work reveals that the electrocatalyst for V2+/V3+ couple obtained from biomass is significant for improving the comprehensive energy storage performance of VRFB.


title: LockDown: an operating system for achieving service continuity by quarantining principals

abstract: This paper introduces quarantine, a new security primitive for an operating system to use in order to protect information and isolate malicious behavior. Quarantine's core feature is the ability to fork a protection domain on-the-fly to isolate a specific principal's execution of untrusted code without risk of a compromise spreading. Forking enables the OS to ensure service continuity by permitting even high-risk operations to proceed, albeit subject to greater scrutiny and constraints. Quarantine even partitions executing threads that share resources into isolated protection domains. We discuss the design and implementation of quarantine within the L ock D own OS, a security-focused evolution of the C omposite component-based microkernel OS. Initial performance results for quarantine show that about 98% of the overhead comes from the cost of copying memory to the new protection domain.


title: Development of a Wearable Sensing Glove for Measuring the Motion of Fingers Using Linear Potentiometers and Flexible Wires

abstract: In this paper, a wearable sensing glove for measuring the motion of the fingers is proposed. The system consists of linear potentiometers, flexible wires, and linear springs, which makes it compact and lightweight so that it does not interfere with the natural motion of the fingers. Inspired by the way wrinkles on finger joints are smoothed out when the finger is flexed, a flexible wire is attached to the back of each finger. As the flexible wire moves due to the motion of the finger, the joint angles are calculated by measuring the change in length of wire. Linear potentiometers with linear springs were used to maintain the tension of the wires in order to measure the wire length change consistently. Because the motion of the proximal interphalangeal (PIP) joint is dependent on that of the distal interphalangeal (DIP) joint, only two linear potentiometers were used for each finger. A compact sensing module including 10 linear potentiometers and springs was attached to a glove. The proposed system can widely be applied for the systems, which require to measure finger motions accurately, e.g., virtual reality or teleoperation systems. Such feasible applications were actually implemented and introduced in this paper.


title: Geometric deep learning: going beyond Euclidean data

abstract: Many scientific fields study data with an underlying structure that is non-Euclidean. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions) and are natural targets for machine-learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural-language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure and in cases where the invariances of these structures are built into networks used to model them.


title: Optimizing the Transition Process from Sparking for Non-contact TIG Welding Inverters

abstract: Gas Tungsten Arc Welding (GTAW or TIG) process in many cases, particularly in automated applications, does not have self-starting feature. High-frequency high-voltage AC pulses are required across the weld terminals to initiate sparking that help establishing stable DC current. The purpose of this work is to guide the transition process from creating momentary spark to desired stable welding current with minimum effort from welding inverter as well as from the spark generator. In the process the spark generator becomes repeatable and the system as a whole becomes more reliable. The performance index of this particular work is to eliminate the recurring requirement of high voltage high frequency pulses that emits Electromagnetic Interference.


title: Nonpharmacological treatments for patients with Parkinson's disease

abstract: Since 2013, a number of studies have enhanced the literature and have guided clinicians on viable treatment interventions outside of pharmacotherapy and surgery. Thirty-three randomized controlled trials and one large observational study on exercise and physiotherapy were published in this period. Four randomized controlled trials focused on dance interventions, eight on treatment of cognition and behavior, two on occupational therapy, and two on speech and language therapy (the latter two specifically addressed dysphagia). Three randomized controlled trials focused on multidisciplinary care models, one study on telemedicine, and four studies on alternative interventions, including music therapy and mindfulness. These studies attest to the marked interest in these therapeutic approaches and the increasing evidence base that places nonpharmacological treatments firmly within the integrated repertoire of treatment options in Parkinson's disease. (c) 2015 International Parkinson and Movement Disorder Society.


title: Old jokes, new media – Online sexism and constructions of gender in Internet memes:

abstract: The Internet is a space where the harassment of women and marginalised groups online has attracted the attention of both academic and popular press. Feminist research has found that instances of online sexism and harassment are often reframed as “acceptable” by constructing them as a form of humour. Following this earlier research, this present paper explores a uniquely technologically-bound type of humour by adopting a feminist, social-constructionist approach to examine the content of popular Internet memes. Using thematic analysis on a sample of 240 image macro Internet memes (those featuring an image with a text caption overlaid), we identified two broad, overarching themes – Technological Privilege and Others. Within the analysis presented here, complex and troubling constructions of gendered identity in online humour are explored, illustrating the potential for the othering and exclusion of women through humour in technological spaces. We argue that this new iteration of heteronormative, hegemonic m...


The Semantic and Stylistic Differentiation of Synonyms and Near-Synonyms


title: Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter

abstract: This paper presents two types of nonlinear controllers for an autonomous quadrotor helicopter. One type, a feedback linearization controller involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. The second type involves a new approach to an adaptive sliding mode controller using input augmentation in order to account for the underactuated property of the helicopter, sensor noise, and uncertainty without using control inputs of large magnitude. The sliding mode controller performs very well under noisy conditions, and adaptation can effectively estimate uncertainty such as ground effects.


title: Factors that influence recurrent lumbar disc herniation

abstract: INTRODUCTION ::: The most common cause of poor outcome following lumbar disc surgery is recurrent herniation. Recurrence has been noted in 5% to 15% of patients with surgically treated primary lumbar disc herniation. There have been many studies designed to determine the risk factors for recurrent lumbar disc herniation. In this study, we retrospectively analysed the influence of disc degeneration, endplate changes, surgical technique, and patient's clinical characteristics on recurrent lumbar disc herniation. ::: ::: ::: METHODS ::: Patients who underwent primary single-level L4-L5 lumbar discectomy and who were reoperated on for recurrent L4-L5 disc herniation were retrospectively reviewed. All these operations were performed between August 2004 and September 2009 at the Neurosurgery Department of Ataturk Education and Research Hospital in Ankara, Turkey. ::: ::: ::: RESULTS ::: During the study period, 126 patients were reviewed, with 101 patients underwent primary single-level L4-L5 lumbar discectomy and 25 patients were reoperated on for recurrent L4-L5 disc herniation. Preoperative higher intervertebral disc height (P<0.001) and higher body mass index (P=0.042) might be risk factors for recurrence. Modic endplate changes were statistically significantly greater in the recurrent group than in the non-recurrent group (P=0.032). ::: ::: ::: CONCLUSION ::: Our study suggests that patients who had recurrent lumbar disc herniation had preoperative higher disc height and higher body mass index. Modic endplate changes had a higher tendency for recurrence of lumbar disc herniation. Well-planned and well-conducted large-scale prospective cohort studies are needed to confirm this and enable convenient treatment modalities to prevent recurrent disc pathology.


title: Partitioning Compute Units in CNN Acceleration for Statistical Memory Traffic Shaping

abstract: Convolutional Neural Networks (CNNs) have become the default choice for processing visual information, and the design complexity of CNNs has been steadily increasing to improve accuracy. To cope with the massive amount of computation needed for such complex CNNs, the latest solutions utilize blocking of an image over the available dimensions (e.g., horizontal, vertical, channel, and kernel) and batching of multiple input images to improve data reuse in the memory hierarchy. While there has been a large collection of works on maximizing data reuse, only a few studies have focused on the memory bottleneck problem caused by limited bandwidth. Bandwidth bottleneck can easily occur in CNN acceleration as CNN layers have different sizes with varying computation needs and as batching is typically performed over each layer of CNN for an ideal data reuse. In this case, the data transfer demand for a layer can be relatively low or high compared to the computation requirement of the layer, and therefore temporal fluctuations in memory access can be induced eventually causing bandwidth problems. In this paper, we first show that there exists a high degree of fluctuation in memory access to computation ratio depending on CNN layers and functions in the layer being processed by the compute units (cores), where the compute units are tightly synchronized to maximize data reuse. Then we propose a strategy of partitioning the compute units where the cores within each partition process a batch of input data in a synchronous manner to maximize data reuse but different partitions run asynchronously. Because the partitions stay asynchronous and typically process different CNN layers at any given moment, the memory access traffic sizes of the partitions become statistically shuffled. Thus, the partitioning of compute units and asynchronous use of them make the total memory access traffic size be smoothened over time, and the degree of partitioning determines a tradeoff between data reuse efficiency and memory bandwidth utilization efficiency. We call this smoothing statistical memory traffic shaping, and we show that it can lead to 8.0 percent of performance gain on a commercial 64-core processor when running ResNet-50.


title: Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study

abstract: Summary Background Although more than 80% of the global burden of cardiovascular disease occurs in low-income and middle-income countries, knowledge of the importance of risk factors is largely derived from developed countries. Therefore, the effect of such factors on risk of coronary heart disease in most regions of the world is unknown. Methods We established a standardised case-control study of acute myocardial infarction in 52 countries, representing every inhabited continent. 15 152 cases and 14 820 controls were enrolled. The relation of smoking, history of hypertension or diabetes, waist/hip ratio, dietary patterns, physical activity, consumption of alcohol, blood apolipoproteins (Apo), and psychosocial factors to myocardial infarction are reported here. Odds ratios and their 99% CIs for the association of risk factors to myocardial infarction and their population attributable risks (PAR) were calculated. Findings Smoking (odds ratio 2·87 for current vs never, PAR 35·7% for current and former vs never), raised ApoB/ApoA1 ratio (3·25 for top vs lowest quintile, PAR 49·2% for top four quintiles vs lowest quintile), history of hypertension (1·91, PAR 17·9%), diabetes (2·37, PAR 9·9%), abdominal obesity (1·12 for top vs lowest tertile and 1·62 for middle vs lowest tertile, PAR 20·1% for top two tertiles vs lowest tertile), psychosocial factors (2·67, PAR 32·5%), daily consumption of fruits and vegetables (0·70, PAR 13·7% for lack of daily consumption), regular alcohol consumption (0·91, PAR 6·7%), and regular physical activity (0·86, PAR 12·2%), were all significantly related to acute myocardial infarction (p<0·0001 for all risk factors and p=0·03 for alcohol). These associations were noted in men and women, old and young, and in all regions of the world. Collectively, these nine risk factors accounted for 90% of the PAR in men and 94% in women. Interpretation Abnormal lipids, smoking, hypertension, diabetes, abdominal obesity, psychosocial factors, consumption of fruits, vegetables, and alcohol, and regular physical activity account for most of the risk of myocardial infarction worldwide in both sexes and at all ages in all regions. This finding suggests that approaches to prevention can be based on similar principles worldwide and have the potential to prevent most premature cases of myocardial infarction.


title: A mathematical model of mechanotransduction reveals how mechanical memory regulates mesenchymal stem cell fate decisions

abstract: Mechanical and biophysical properties of the cellular microenvironment regulate cell fate decisions. Mesenchymal stem cell (MSC) fate is influenced by past mechanical dosing (memory), but the mechanisms underlying this process have not yet been well defined. We have yet to understand how memory affects specific cell fate decisions, such as the differentiation of MSCs into neurons, adipocytes, myocytes, and osteoblasts. We study a minimal gene regulatory network permissive of multi-lineage MSC differentiation into four cell fates. We present a continuous model that is able to describe the cell fate transitions that occur during differentiation, and analyze its dynamics with tools from multistability, bifurcation, and cell fate landscape analysis, and via stochastic simulation. Whereas experimentally, memory has only been observed during osteogenic differentiation, this model predicts that memory regions can exist for each of the four MSC-derived cell lineages. We can predict the substrate stiffness ranges over which memory drives differentiation; these are directly testable in an experimental setting. Furthermore, we quantitatively predict how substrate stiffness and culture duration co-regulate the fate of a stem cell, and we find that the feedbacks from the differentiating MSC onto its substrate are critical to preserve mechanical memory. Strikingly, we show that re-seeding MSCs onto a sufficiently soft substrate increases the number of cell fates accessible. Control of MSC differentiation is crucial for the success of much-lauded regenerative therapies based on MSCs. We have predicted new memory regions that will directly impact this control, and have quantified the size of the memory region for osteoblasts, as well as the co-regulatory effects on cell fates of substrate stiffness and culture duration. Taken together, these results can be used to develop novel strategies to better control the fates of MSCs in vitro and following transplantation.


title: EmoTxt: A toolkit for emotion recognition from text

abstract: We present EmoTxt, a toolkit for emotion recognition from text, trained and tested on a gold standard of about 9K question, answers, and comments from online interactions. We provide empirical evidence of the performance of EmoTxt. To the best of our knowledge, EmoTxt is the first open-source toolkit supporting both emotion recognition from text and training of custom emotion classification models.