title: The age of gossip: spatial mean field regime

abstract: Disseminating a piece of information, or updates for a piece of information, has been shown to benefit greatly from simple randomized procedures, sometimes referred to as gossiping, or epidemic algorithms. Similarly, in a network where mobile nodes occasionally receive updated content from a base station, gossiping using opportunistic contacts allows for recent updates to be efficiently maintained, for a large number of nodes. In this case, however, gossiping depends on node mobility. For this reason, we introduce a new gossip model, with mobile nodes moving between different classes that can represent locations or states, which determine gossiping behavior of the nodes. Here we prove that, when the number of mobile nodes becomes large, the age of the latest updates received by mobile nodes approaches a deterministic mean-field regime. More precisely, we show that the occupancy measure of the process constructed, with the ages defined above, converges to a deterministic limit that can be entirely characterized by differential equations. This major simplification allows us to characterize how mobility, source inputs and gossiping influence the age distribution for low and high ages. It also leads to a scalable numerical evaluation of the performance of mobile update systems, which we validate (using a trace of 500 taxicabs) and use to propose infrastructure deployment.


title: Transferring topical knowledge from auxiliary long texts for short text clustering

abstract: With the rapid growth of social Web applications such as Twitter and online advertisements, the task of understanding short texts is becoming more and more important. Most traditional text mining techniques are designed to handle long text documents. For short text messages, many of the existing techniques are not effective due to the sparseness of text representations. To understand short messages, we observe that it is often possible to find topically related long texts, which can be utilized as the auxiliary data when mining the target short texts data. In this article, we present a novel approach to cluster short text messages via transfer learning from auxiliary long text data. We show that while some previous work exists that enhance short text clustering with related long texts, most of them ignore the semantic and topical inconsistencies between the target and auxiliary data and hurt the clustering performance. To accommodate the possible inconsistency between source and target data, we propose a novel topic model - Dual Latent Dirichlet Allocation (DLDA) model, which jointly learns two sets of topics on short and long texts and couples the topic parameters to cope with the potential inconsistency between data sets. We demonstrate through large-scale clustering experiments on both advertisements and Twitter data that we can obtain superior performance over several state-of-art techniques for clustering short text documents.


title: Mining Temporal Specifications for Error Detection

abstract: Specifications are necessary in order to find software bugs using program verification tools. This paper presents a novel automatic specification mining algorithm that uses information about error handling to learn temporal safety rules. Our algorithm is based on the observation that programs often make mistakes along exceptional control-flow paths, even when they behave correctly on normal execution paths. We show that this focus improves the effectiveness of the miner for discovering specifications beneficial for bug finding. ::: ::: We present quantitative results comparing our technique to four existing miners. We highlight assumptions made by various miners that are not always born out in practice. Additionally, we apply our algorithm to existing Java programs and analyze its ability to learn specifications that find bugs in those programs. In our experiments, we find filtering candidate specifications to be more important than ranking them. We find 430 bugs in 1 million lines of code. Notably, we find 250 more bugs using per-program specifications learned by our algorithm than with generic specifications that apply to all programs.


title: A biomimetic soft fingertip applicable to haptic feedback systems for texture identification

abstract: Humans recognize textures using the tactile data obtained from the human somatosensory system. Recognition of textures allows humans discriminate objects and materials. Moreover, by understanding the object's or material's texture, the human intuitively estimates roughness and the friction properties of the object or the material. This ability is necessary for object manipulative tasks. Likewise artificial haptic systems too, should have the ability to encode textures and feedback those data to haptic applications such as haptic displays. In this paper a biomimetic soft fingertip sensor that can be used in above haptic systems is introduced. The fingertip has the ability to detect force and vibration modalities. We propose three features calculated from the covariance signal of two adjacent accelerometers in the fingertip to use in texture identification. The covariance signal is transformed using Discrete Wavelet Transform (DWT) and the three features mentioned below are calculated. The mean and variance of the approximate signal, and the energies of the detailed signal are chosen as features. Then, the proposed features were validate by using those in an Artificial Neural Network (ANN) to classify seven wood samples. The results showed a 65% success rate in classifying wood samples and that the proposed features are acceptable to encode textures.


title: A review of feature selection techniques in bioinformatics

abstract: Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques. ::: ::: In this article, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications. ::: ::: Contact: yvan.saeys@psb.ugent.be ::: ::: Supplementary information: http://bioinformatics.psb.ugent.be/supplementary_data/yvsae/fsreview


title: Coordinated Power Control of Variable-Speed Diesel Generators and Lithium-Battery on a Hybrid Electric Boat

abstract: This paper presents coordinated power management strategies for a hybrid electric boat (HEB) based on dynamic load's power distribution approach using diesel generators and batteries. Two variable-speed diesel generator sets connecting to dc-bus via fully controlled rectifiers serve as the main power supply source; one lithium-battery pack linked to dc-bus through a bidirectional DC/DC converter serves as an auxiliary power supply source. The power requirement of the thrusters and other onboard equipment is represented by a load power profile. The main contribution of this paper is focused on the load power sharing of the HEB between the two variable-speed diesel generators and the lithium-battery pack, through the fluctuations extracted from load power and their assignation to the batteries. Another important issue is that when the total load demand is low, one of the diesel generator set will be switched off to improve the diesel-fuel efficiency. The output powers of the diesel generators and the batteries are controlled based on the power distribution strategy via the converters controlling. The performances of the proposed hybrid power system control are evaluated through simulations in MATALB/Simulink, and through reduced-scale experimental tests carried out for a specific driving cycle of the HEB.


title: Modeling Sub-Document Attention Using Viewport Time

abstract: Website measures of engagement captured from millions of users, such as in-page scrolling and viewport position, can provide deeper understanding of attention than possible with simpler measures, such as dwell time. Using data from 1.2M news reading sessions, we examine and evaluate three increasingly sophisticated models of sub-document attention computed from viewport time, the time a page component is visible on the user display. Our modeling incorporates prior eye-tracking knowledge about onscreen reading, and we validate it by showing how, when used to estimate user reading rate, it aligns with known empirical measures. We then show how our models reveal an interaction between article topic and attention to page elements. Our approach supports refined large-scale measurement of user engagement at a level previously available only from lab-based eye-tracking studies.


title: Ad-hoc on-demand distance vector routing

abstract: An ad-hoc network is the cooperative engagement of a collection of mobile nodes without the required intervention of any centralized access point or existing infrastructure. We present Ad-hoc On Demand Distance Vector Routing (AODV), a novel algorithm for the operation of such ad-hoc networks. Each mobile host operates as a specialized router, and routes are obtained as needed (i.e., on-demand) with little or no reliance on periodic advertisements. Our new routing algorithm is quite suitable for a dynamic self starting network, as required by users wishing to utilize ad-hoc networks. AODV provides loop-free routes even while repairing broken links. Because the protocol does not require global periodic routing advertisements, the demand on the overall bandwidth available to the mobile nodes is substantially less than in those protocols that do necessitate such advertisements. Nevertheless we can still maintain most of the advantages of basic distance vector routing mechanisms. We show that our algorithm scales to large populations of mobile nodes wishing to form ad-hoc networks. We also include an evaluation methodology and simulation results to verify the operation of our algorithm.


title: 28/38-GHz dual-band millimeter wave SIW array antenna with EBG structures for 5G applications

abstract: The design of linearly polarized dual-band substrate integrated waveguide (SIW) antenna/array operating at Ka-band is proposed. The single antenna element consists of a SIW cavity with two longitudinal slots engraved in one of the conducting planes. The longer and shorter slots are resonating at 28 GHz and 38 GHz, respectively. Only the simulated results are presented. All simulations have been carried out using industry-standard software, CST Microwave Studio. For single antenna element, an impedance bandwidth (S11< −10 dB) of 0.45 GHz (1.60 %) and 2.20 GHz (5.8 %) is achieved with the maximum gain of 5.2 dBi and 5.9 dBi at 28 GHz and 38 GHz, respectively. To achieve high gain, a horizontally polarized linear array of four elements (1 × 4) is designed. For the antenna array, a microstrip lines feed network is designed using 3-dB wilkinson power divider. At 28 GHz and 38 GHz, the impedance bandwidth is 0.32 GHz (1.14 %) and 1.9 GHz (5%) having maximum gain of 11.9 dBi and 11.2 dBi, respectively. A low loss/cost substrate, RT/Duroid 5880 is used in the proposed designs.


title: Adaptive patch-based image denoising by EM-adaptation

abstract: Effective image prior is a key factor for successful image denoising. Existing learning-based priors require a large collection of images for training. Besides being computationally expensive, these training images do not necessarily correspond to the noisy image of interest. In this paper, we propose an adaptive learning procedure for learning image patch priors. The new algorithm, called the Expectation-Maximization (EM) adaptation, maps a generic prior to a targeted image to create a specific prior. EM adaptation requires significantly less amount of training data compared to the standard EM, and can be applied to pre-filtered images in the absence of clean databases. Experimental results show that the adapted prior is consistently better than the originally un-adapted prior, and has superior performance than some state-of-the-art algorithms.


title: Teaching Java with the BlueJ Environment

abstract: BlueJ is a visual programming environment designed to teach objectoriented programming, using Java as the implementation language. BlueJ allows students to concentrate on solving programming problems without becoming distracted by the mechanics of compiling and executing Java programs. This paper reports on the first use of BlueJ to teach Java to an introductory programming class, in a computing degree in 1999. Several mechanisms were put in place to help students with any problems they encountered. Surveys and interviews were used to collect data on student backgrounds, perceptions and attitudes towards BlueJ. In spite of some problems encountered with installing and running the software, students who participated in the study generally found that BlueJ was helpful in


title: Design and analysis of cross-fed rectangular array antenna; an X-band microstrip array antenna, operating at 11 GHz

abstract: In this paper, an X-Band Microstrip Patch antenna termed as the Cross fed rectangular array antenna has been presented with an enhanced radiation efficiency. The proposed antenna is designed and simulated using FEKO 5.5 suite. It is then fabricated on a 40 × 40mm2 Rogers RT-Duroid 5880LZ dielectric material board. This antenna is composed of a rectangular patch array in a cross fashion with four patches, each with a dimension of 12.3 mm × 9.85 mm excited using a wire-port. The antenna operates at a frequency of 11.458 GHz in the X-Band (8–12 GHz). It has achieved stable radiation efficiency of 80.71% with a gain of 7.4 dB at the operating frequency. It is thus inferred that this antenna can be used for X-Band applications such as in maritime navigation and airborne radars.


title: A Novel Method for Decentralised Peer-to-Peer Software License Validation Using Cryptocurrency Blockchain Technology

abstract: Protecting software copyright has been an issue since the late 1970’s, and software license validation has been a primary method employed in an attempt to minimise software piracy and protect software copyright. This paper presents a novel method for decentralised peer-topeer software license validation using cryptocurrency blockchain technology to ameliorate software piracy, and to provide a mechanism for all software developers to protect their copyrighted works.


title: A Practical One-Shot Multispectral Imaging System Using a Single Image Sensor

abstract: Single-sensor imaging using the Bayer color filter array (CFA) and demosaicking is well established for current compact and low-cost color digital cameras. An extension from the CFA to a multispectral filter array (MSFA) enables us to acquire a multispectral image in one shot without increased size or cost. However, multispectral demosaicking for the MSFA has been a challenging problem because of very sparse sampling of each spectral band in the MSFA. In this paper, we propose a high-performance multispectral demosaicking algorithm, and at the same time, a novel MSFA pattern that is suitable for our proposed algorithm. Our key idea is the use of the guided filter to interpolate each spectral band. To generate an effective guide image, in our proposed MSFA pattern, we maintain the sampling density of the G -band as high as the Bayer CFA, and we array each spectral band so that an adaptive kernel can be estimated directly from raw MSFA data. Given these two advantages, we effectively generate the guide image from the most densely sampled G -band using the adaptive kernel. In the experiments, we demonstrate that our proposed algorithm with our proposed MSFA pattern outperforms existing algorithms and provides better color fidelity compared with a conventional color imaging system with the Bayer CFA. We also show some real applications using a multispectral camera prototype we built.


title: Exploring Cultural Differences in Pictogram Interpretations

abstract: Pictogram communication is successful when participants at both ends of the communication channel share a common pictogram interpretation. Not all pictograms carry a universal interpretation, however; the issue of ambiguous pictogram interpretation must be addressed to assist pictogram communication. To unveil the ambiguity possible in pictogram interpretation, we conduct a human subject experiment to identify culture-specific criteria employed by humans by detecting cultural differences in pictogram interpretations. Based on the findings, we propose a categorical semantic relevance measure which calculates how relevant a pictogram is to a given interpretation in terms of a given pictogram category. The proposed measure is applied to categorized pictogram interpretations to enhance pictogram retrieval performance. The WordNet, the ChaSen, and the EDR Electronic Dictionary registered to the Language Grid are utilized to merge synonymous pictogram interpretations and to categorize pictogram interpretations into super-concept categories. We show how the Language Grid can assist the cross-cultural research process.


title: Enabling Self-Powered Autonomous Wireless Sensors with New-Generation I2C-RFID Chips

abstract: A self-powered autonomous RFID device with sensing and computing capabilities is presented in this paper. Powered by an RF energy-harvesting circuit enhanced by a DC-DC voltage booster in silicon-on-insulator (SOI) technology, the device relies on a microcontroller and a new generation I2C-RFID chip to wirelessly deliver sensor data to standard RFID EPC Class-1 Generation-2 (Gen2) readers. When the RF power received from the interrogating reader is -14 dBm or higher, the device, fabricated on an FR4 substrate using low-cost discrete components, is able to produce 2.4-V DC voltage to power its circuitry. The experimental results demonstrate the effectiveness of the device to perform reliable sensor data transmissions up to 5 meters in fully-passive mode. To the best of our knowledge, this represents the longest read range ever reported for passive UHF RFID sensors compliant with the EPC Gen2 standard.


title: A versatile tactile sensor system for covering large and curved surface areas

abstract: Most of today's robots, while being equipped with arrays of ever more impressive sensors, still lack a fundamental ability so readily available to biological systems: a sense of touch. With the exception of a few humanoid platforms tactile sensing is typically limited to small areas such as palms or finger tips. In this paper we present a tactile sensing system designed specifically to meet the needs of covering large, possibly curved surfaces of the robot. The sensors are at the same time highly sensitive and robust, cheap to manufacture and can be made to have almost any size and shape. As such the presented system can ideally complement existing systems and their high spatial resolution required in finger tips with a system providing highly sensitive and impact resistant coverage for the rest of the body.


title: Rice Market Structure, Conduct and Performance in Nigeria: A Survey of Akwa Ibom State Rice Marketers

abstract: The study examined the structure, conduct and performance of rice marketing in Akwa Ibom State, Nigeria. Data sourced from sixty traders in four markets were analyzed using Gini coefficient, Herfindahl Index, Net returns and descriptive statistics. The result indicated that more of the imported brands of rice were sold in the two urban markets while more of the local brands of rice were sold in the rural markets. Average share of imported rice across the markets was more than local rice. Greater quantities of rice sold in the markets were in 5kg measure, followed by 12.5kg, the least being 50kg measures. Average Net margin of ₦1,894.5 and ₦1,852.6 were realized for imported and local rice. Also, the local rice margin as percentage of total marketing margin (32.23%) was greater than that of imported (27.65%). The study further revealed an average marketing efficiency of 392.9% and 405.1% for imported and local rice. The Gini Coefficient (GC) and Herfendahl Index (HHI) values of 0.683 and 0.295 showed that the rice markets were highly concentrated with non competitive practices showing disparity in earnings. The prevailing duration stocks were held in shops by traders and sources of obtaining market information were three weeks to one month and mobile phone (66.7%) respectively. The use of shared trucks to convey stocks prevailed in the study area and the prevalence promotion strategy was friendly attitude to customer. Furthermore, Aba market accounted for the major source of stock supply in the area. The study recommends the provision of storage facilities, improving the quality of our local rice as well as pursuing policies that would discourage importation as the way out.


title: Evaluating Osseointegration Into a Deeply Porous Titanium Scaffold

abstract: STUDY DESIGN ::: This was a biomechanical push-out testing study using a porcine model. ::: ::: ::: OBJECTIVE ::: The purpose was to evaluate the strength of implant-bone interface of a porous titanium scaffold by comparing it to polyetheretherketone (PEEK) and allograft. ::: ::: ::: SUMMARY OF BACKGROUND DATA ::: Osseointegration is important for achieving maximal stability of spinal fusion implants and it is desirable to achieve as quickly as possible. Common PEEK interbody fusion implants appear to have limited osseointegration potential because of the formation of fibrous tissue along the implant-bone interface. Porous, three-dimensional titanium materials may be an option to enhance osseointegration. ::: ::: ::: METHODS ::: Using the skulls of two swine, in the region of the os frontale, 16 identical holes (4 mm diameter) were drilled to 10 mm depth in each skull. Porous titanium, PEEK, and allograft pins were press fit into the holes. After 5 weeks, animals were euthanized and the skull sections with the implants were cut into sections with each pin centered within a section. Push-out testing was performed using an MTS machine with a push rate of 6 mm/min. Load-deformation curves were used to compute the extrinsic material properties of the bone samples. Maximum force (N) and shear strength (MPa) were extracted from the output to record the bonding strength between the implant and surrounding bone. When calculating shear strength, maximum force was normalized by the actual implant surface area in contact with surrounding bone. ::: ::: ::: RESULTS ::: Mean push-out shear strength was significantly greater in the porous titanium scaffold group than in the PEEK or allograft groups (10.2 vs. 1.5 vs. 3.1 MPa, respectively; P < 0.05). ::: ::: ::: CONCLUSION ::: The push-out strength was significantly greater for the implants with porous titanium coating compared with the PEEK or allograft. These results suggest that the material has promise for facilitating osseointegration for implants, including interbody devices for spinal fusion. ::: ::: ::: LEVEL OF EVIDENCE ::: N/A.


title: A Region-based Alignment-free Partial Fingerprint Matching

abstract: Fingerprint is one of the most researched biometrics and applied to many applications like boarder security, access security, security of electronic devices to name a few. However, alignment and matching of partial fingerprints convincingly is still a challenging problem. This paper proposes a region based partial fingerprint matching approach that matches the fingerprints without aligning them. The approach focuses on extracting the matching regions in unaligned fingerprints. As extraction of regions happens without alignment, a new similarity metric is proposed that can compute similarity value between unaligned fingerprints. The results demonstrate that the proposed approach can eliminate the need for aligning the fingerprints without affecting the accuracy. The popular Fingerprint Verification Competition (FVC) 2002 dataset is used for conducting experiments. Moreover, the results of fingerprint recognition are compared with the best alignment based fingerprint matching techniques.


title: A Local Density Based Spatial Clustering Algorithm with Noise

abstract: Density-based clustering algorithms are attractive for the task of class identification in spatial database. However, in many cases, very different local-density clusters exist in different regions of data space, therefore, DBSCAN [Ester, M. et al., A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In E. Simoudis, J. Han, & U. M. Fayyad (Eds.), Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (pp. 226-231). Portland, OR: AAAI.] using a global density parameter is not suitable. As an improvement, OPTICS [Ankerst, M. et al,(1999). OPTICS: Ordering Points To Identify the Clustering Structure. In A. Delis, C. Faloutsos, & S. Ghandeharizadeh (Eds.), Proc. ACM SIGMOD Int. Conf. on Management of Data (pp. 49-60). Philadelphia, PA: ACM.] creates an augmented ordering of the database representing its density-based clustering structure, but it only generates the clusters whose local-density exceeds some threshold instead of similar local-density clusters and doesn't produce a clustering of a data set explicitly. Furthermore the parameters required by almost all the well-known clustering algorithms are hard to determine but have a significant influence on the clustering result. In this paper, a new clustering algorithm LDBSCAN relying on a local-density-based notion of clusters is proposed to solve those problems and, what is more, it is very easy for us to pick the appropriate parameters and takes the advantage of the LOF [Breunig, M. M., et al.,(2000). LOF: Identifying Density-Based Local Outliers. In W. Chen, J. F. Naughton, & P. A. Bernstein (Eds.), Proc. ACM SIGMOD Int. Conf. on Management of Data (pp. 93-104). Dalles, TX: ACM.] to detect the noises comparing with other density-based clustering algorithms. The proposed algorithm has potential applications in business intelligence and enterprise information systems.


title: DReLUs: Dual Rectified Linear Units

abstract: Rectified Linear Units (ReLUs) are widely used in feed-forward neural networks, and in convolutional neural networks in particular. However, they can be rarely found in recurrent neural networks due to the unboundedness and the positive image of the rectified linear activation function. In this paper, we introduce Dual Rectified Linear Units (DReLUs), a novel type of rectified unit that comes with a positive and negative image that is unbounded. We show that we can successfully replace the tanh activation function in the recurrent step of quasi recurrent neural networks. In addition, DReLUs are less prone to the vanishing gradient problem, they are noise robust, and they induce sparse activations. Therefore, we are able to stack up to eight quasi recurrent layers, making it possible to improve the current state-of-the-art in character-level language modeling over architectures based on shallow Long Short-Term Memory (LSTM).


Modeling of the stimulating field generation in TMS.


title: A comparison of peer-to-peer architectures

abstract: The large number of peer-to-peer file-sharing applications can be subdivided in three basic categories: having a mediated, pure or hybrid architecture. This paper details each of these and indicates their respective strengths and weaknesses. In addition to this theoretical study, a number of practical experiments were conducted, with special attention for three popular applications, representative of each of the three architectures. Although a number of measurement studies have been done in the past ([1], [3], etc.) these all investigate only a fraction of the available applications and architectures, with very little focus on the bigger picture and to the recent evolutions in peer-to-peer architectures.


title: CRISPR/Cas9-based tools for targeted genome editing and replication control of HBV

abstract: Hepatitis B virus (HBV) infection remains a major global health problem because current therapies rarely eliminate HBV infections to achieve a complete cure. A different treatment paradigm to effectively clear HBV infection and eradicate latent viral reservoirs is urgently required. In recent years, the development of a new RNA-guided gene-editing tool, the CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR-associated nuclease 9) system, has greatly facilitated site-specific mutagenesis and represents a very promising potential therapeutic tool for diseases, including for eradication of invasive pathogens such as HBV. Here, we review recent advances in the use of CRISPR/Cas9, which is designed to target HBV specific DNA sequences to inhibit HBV replication and to induce viral genome mutation, in cell lines or animal models. Advantages, limitations and possible solutions, and proposed directions for future research are discussed to highlight the opportunities and challenges of CRISPR/Cas9 as a new, potentially curative therapy for chronic hepatitis B infection. ::: Open image in new window


title: Mucociliary function deteriorates in the clinical range of inspired air temperature and humidity

abstract: Objective ::: To test whether a reduction in air temperature within the clinical range [37 °C to 30, 100% relative humidity (RH)] altered mucus transport velocity (MTV) and ciliary beat frequency (CBF) in an in vitro ovine tracheal model.


title: Pedestrian detection with LeNet-like convolutional networks

abstract: We present a detection method that is able to detect a learned target and is valid for both static and moving cameras. As an application, we detect pedestrians, but could be anything if there is a large set of images of it. The data set is fed into a number of deep convolutional networks, and then, two of these models are set in cascade in order to filter the cutouts of a multi-resolution window that scans the frames in a video sequence. We demonstrate that the excellent performance of deep convolutional networks is very difficult to match when dealing with real problems, and yet we obtain competitive results.


title: The where in the tweet

abstract: Twitter is a widely-used social networking service which enables its users to post text-based messages, so-called tweets. POI tags on tweets can show more human-readable high-level information about a place rather than just a pair of coordinates. In this paper, we attempt to predict the POI tag of a tweet based on its textual content and time of posting. Potential applications include accurate positioning when GPS devices fail and disambiguating places located near each other. We consider this task as a ranking problem, i.e., we try to rank a set of candidate POIs according to a tweet by using language and time models. To tackle the sparsity of tweets tagged with POIs, we use web pages retrieved by search engines as an additional source of evidence. From our experiments, we find that users indeed leak some information about their accurate locations in their tweets.


title: Authorship Attribution Using Word Sequences

abstract: Authorship attribution is the task of identifying the author of a given text. The main concern of this task is to define an appropriate characterization of documents that captures the writing style of authors. This paper proposes a new method for authorship attribution supported on the idea that a proper identification of authors must consider both stylistic and topic features of texts. This method characterizes documents by a set of word sequences that combine functional and content words. The experimental results on poem classification demonstrated that this method outperforms most current state-of-the-art approaches, and that it is appropriate to handle the attribution of short documents.


title: Intradural arachnoid cyst associated with thoracic spinal compression fracture: 7-year follow up after surgery

abstract: STUDY DESIGN ::: A case report with long-term follow after a surgical procedure. ::: ::: ::: OBJECTIVES ::: To describe a case of intradural arachnoid cyst secondary to a compression fracture in the thoracic spine and to report long-term results after surgical treatment with hemilaminectomy. ::: ::: ::: SETTING ::: Osaka, Japan. ::: ::: ::: METHODS ::: A 68-year-old man who had a traumatic intradural arachnoid cyst following an adjacent compression fracture of T5 underwent surgery. Intraoperatively, after recognition of intradural arachnoid cyst with an echogram following hemilaminectomy, the dural sac was incised and the arachnoid cyst was resected under microscopic observation. ::: ::: ::: RESULTS ::: At 7 years after the operation, the low intensity within the vertebral body of the compression fracture had resolved and the spinal cord remained in its normal shape and position. No progression of kyphotic deformity was detected. ::: ::: ::: CONCLUSION ::: A compression fracture of the thoracic spine can be associated with an intradural arachnoid cyst. Microscopic resection via hemilaminectomy for the cyst showed a good result in a 7-year follow up.


title: SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud

abstract: Inference using deep neural networks is often outsourced to the cloud since it is a computationally demanding task. However, this raises a fundamental issue of trust. How can a client be sure that the cloud has performed inference correctly? A lazy cloud provider might use a simpler but less accurate model to reduce its own computational load, or worse, maliciously modify the inference results sent to the client. We propose SafetyNets, a framework that enables an untrusted server (the cloud) to provide a client with a short mathematical proof of the correctness of inference tasks that they perform on behalf of the client. Specifically, SafetyNets develops and implements a specialized interactive proof (IP) protocol for verifiable execution of a class of deep neural networks, i.e., those that can be represented as arithmetic circuits. Our empirical results on three- and four-layer deep neural networks demonstrate the run-time costs of SafetyNets for both the client and server are low. SafetyNets detects any incorrect computations of the neural network by the untrusted server with high probability, while achieving state-of-the-art accuracy on the MNIST digit recognition (99.4%) and TIMIT speech recognition tasks (75.22%).


title: Experiential avoidance and behavioral disorders: A functional dimensional approach to diagnosis and treatment.

abstract: Syndromal classification is a well-developed diagnostic system but has failed to deliver on its promise of the identification of functional pathological processes. Functional analysis is tightly connected to treatment but has failed to develop testable. replicable classification systems. Functional diagnostic dimensions are suggested as a way to develop the functional classification approach, and experiential avoidance is described as 1 such dimension. A wide range of research is reviewed showing that many forms of psychopathology can be conceptualized as unhealthy efforts to escape and avoid emotions, thoughts, memories, and other private experiences. It is argued that experiential avoidance, as a functional diagnostic dimension, has the potential to integrate the efforts and findings of researchers from a wide variety of theoretical paradigms, research interests, and clinical domains and to lead to testable new approaches to the analysis and treatment of behavioral disorders.


title: Deep Transform: Cocktail Party Source Separation via Complex Convolution in a Deep Neural Network

abstract: Convolutional deep neural networks (DNN) are state of the art in many engineering problems but have not yet addressed the issue of how to deal with complex spectrograms. Here, we use circular statistics to provide a convenient probabilistic estimate of spectrogram phase in a complex convolutional DNN. In a typical cocktail party source separation scenario, we trained a convolutional DNN to re-synthesize the complex spectrograms of two source speech signals given a complex spectrogram of the monaural mixture - a discriminative deep transform (DT). We then used this complex convolutional DT to obtain probabilistic estimates of the magnitude and phase components of the source spectrograms. Our separation results are on a par with equivalent binary-mask based non-complex separation approaches.


title: Tractable Inference for Complex Stochastic Processes

abstract: The monitoring and control of any dynamic system depends crucially on the ability to reason about its current status and its future trajectory. In the case of a stochastic system, these tasks typically involve the use of a belief state--a probability distribution over the state of the process at a given point in time. Unfortunately, the state spaces of complex processes are very large, making an explicit representation of a belief state intractable. Even in dynamic Bayesian networks (DBNs), where the process itself can be represented compactly, the representation of the belief state is intractable. We investigate the idea of maintaining a compact approximation to the true belief state, and analyze the conditions under which the errors due to the approximations taken over the lifetime of the process do not accumulate to make our answers completely irrelevant. We show that the error in a belief state contracts exponentially as the process evolves. Thus, even with multiple approximations, the error in our process remains bounded indefinitely. We show how the additional structure of a DBN can be used to design our approximation scheme, improving its performance significantly. We demonstrate the applicability of our ideas in the context of a monitoring task, showing that orders of magnitude faster inference can be achieved with only a small degradation in accuracy.


title: Robust low-rank data matrix approximations

abstract: We review some recent approaches to robust approximations of low-rank data matrices. We consider the problem of estimating a low-rank mean matrix when the data matrix is subject to measurement errors as well as gross outliers in some of its entries. The purpose of the paper is to make various algorithms accessible with an understanding of their abilities and limitations to perform robust low-rank matrix approximations in both low and high dimensional problems.


title: Considerations on Gastroschisis Repair

abstract: Background: Although primary closure of the gastroschisis is possible in many cases, there have been various strategies published and materials used to cover the eviscerated bowel when the abdominal wall defect cannot be closed in one step, providing bowel protection and reduction of heat and fluid loss. There have been suggestions of coverage materials such as skin graft, lyophilized dura mater graft, free flap corium and meshed skin graft (1,2). Purpose: We highlight an alternative repair method of gastroschisis in those cases where there is a disproportion between the amount of eviscerated organs and the hypoplastic abdominal cavity. If in this case primary closure of the abdominal wall is chosen, the difference in volume can cause a significant increase in intraabdominal pressure. Method: In some cases, when complete primary closure was not possible, we used an alternative method to repair the parietal defect using umbilical cord patch. Results: This technique creates a mesothelial surface in contact with the bowel. Remote tracking of these patients showed excellent results. Considerations on Gastroschisis Repair S. Ionescu, B. Andrei, S. Tirlea, B.M. Bunea, E. Licsandru, C. Cirstoveanu, M. Bizubac, M. Ivanov, M. Shelleh, A. Gurita, R. Tabacaru Department of Pediatric Surgery, “Marie S. Curie” Emergency Children Hospital, Bucharest, Romania Newborn Intensive Care Unit, “Marie S. Curie” Emergency Children Hospital, Bucharest, Romania Anesthesiology Unit, “Marie S. Curie” Emergency Children Hospital, Bucharest, Romania Chirurgia (2013) 108: 509-515 No. 4, July August Copyright© Celsius Corresponding author: Sebastian Ionescu, MD Department of Pediatric Surgery “Marie S. Curie” Emergency Children Hospital 62 Constantin Brancoveanu Blvd., sector 4 Bucharest – 041451, Romania Tel/fax: 021/4601040 E-mail: nsionescu@yahoo.com This paper represents an original article, all the authors had the same contribution.


Realistic Modeling and Dynamic Simulation of KUKA KR5 Robot using RecurDyn


title: DETECTION OF Rhizomucor pusillus ON SUNFLOWER SEED

abstract: The accelerated ageing test method (AA), agar plate method (A) and blotter ::: method (B) have been used to detect the Rhizomucor pusillus and other ::: mycobita on 24 samples of sunflower seed. Sterilised and unsterilised ::: sunflower seeds have been incubated at 25oC and 42oC in the dark for 72 and ::: 144 hours. The fungus was not detected in any sample at 25oC, not even after ::: 144 h incubation of seeds. The fungal frequency ranged from 58.3 (B method) ::: to 75.0% (A method) and from 4.2% (B method) to 16.7% (AA method) after 72 h ::: incubation of unsterilised and sterilised samples at 42oC, respectively. The ::: fungal incidence was 25.5% (AA method), 21.9% (A method) and 20.3% (B method) ::: after 72 h incubation of unsterilised seed, and 2% on sterilised seed in all ::: three applied methods. By extended incubation of unsterilised and sterilised ::: seeds up to 144 h at 42°C the frequency and incidence of the fungus did not ::: significantly change. The results of the present research show that the AA ::: test method, widely applied in seed longevity testing, can be used as a ::: simple and efficient method for the detection of R. pusillus as well as other ::: mycobiota on sunflower seed. Furthermore, these are the first data on ::: determining the presence of R. pusillus in Serbia. [Projekat Ministarstva ::: nauke Republike Srbije, br. TR-31023]


title: Utilization of urban sewage sludge: Chinese perspectives

abstract: PURPOSE ::: Urbanization and industrialization in China has resulted in a dramatic increase in the volume of wastewater and sewage sludge produced from wastewater treatment plants. Problems associated with sewage sludge have attracted increasing attention from the public and urban planners. How to manage sludge in an economically and environmentally acceptable manner is one of the critical issues that modern societies are facing. ::: ::: ::: METHODS ::: Sludge treatment systems consist of thickening, dewatering, and several different alternative main treatments (anaerobic digestion, aerobic digestion, drying, composting, and incineration). Agricultural application, landfill, and incineration are the principal disposal methods for sewage sludge in China. However, sewage sludge disposal in the future should focus on resource recovery, reducing environmental impacts and saving economic costs. ::: ::: ::: RESULTS ::: The reuse of biosolids in all scenarios can be environmentally beneficial and cost-effective. Anaerobic digestion followed by land application is the preferable options due to low economic and energy costs and material reuse. ::: ::: ::: CONCLUSION ::: It is necessary to formulate a standard suitable for the utilization of sewage sludge in China.


Speaker/Listener-Orientation and Formality Marking in Japanese


title: A field study of two online dry-out methods for power transformers

abstract: Dry-out methods for transformers, using cellulose cartridges and molecular sieves, were compared. The latter are preferable for keeping a dry transformer dry, whereas the former are more efficient in drying a wet transformer. In this article, we compare the effectiveness of the cellulose cartridge filter and molecular sieve methods. Two identical transformers with similar insulation wetness were used. We estimated the water content of the paper (WCP) insulation from (1) the water activity of the oil, (2) the dielectric response of the transformer, and (3) the water content of individual paper samples determined by Karl Fischer titration. We also measured the acid number of the oil, its interfacial tension, dielectric strength, furan content, dissolved gas content, and particle count.


title: Lattice attacks on digital signature schemes

abstract: We describe a lattice attack on the Digital Signature Algorithm (DSA) when used to sign many messages, m_i, under the assumption that a proportion of the bits of each of the associated ephemeral keys, y_i, can be recovered by alternative techniques.


title: The Interplay of Cross-Situational Word Learning and Sentence-Level Constraints

abstract: A variety of mechanisms contribute to word learning. Learners can track co-occurring words and referents across situations in a bottom-up manner (cross-situational word learning, CSWL). Equally, they can exploit sentential contexts, relying on top-down information such as verb-argument relations and world knowledge, offering immediate constraints on meaning (word learning based on sentence-level constraints, SLCL). When combined, CSWL and SLCL potentially modulate each other's influence, revealing how word learners deal with multiple mechanisms simultaneously: Do they use all mechanisms? Prefer one? Is their strategy context dependent? Three experiments conducted with adult learners reveal that learners prioritize SLCL over CSWL. CSWL is applied in addition to SLCL only if SLCL is not perfectly disambiguating, thereby complementing or competing with it. These studies demonstrate the importance of investigating word-learning mechanisms simultaneously, revealing important characteristics of their interaction in more naturalistic learning environments.


title: Towards a Taxonomy of Microservices Architectures

abstract: The microservices architectural style is gaining more and more momentum for the development of applications as suites of small, autonomous, and conversational services, which are then easy to understand, deploy and scale. However, the proliferation of approaches leveraging microservices calls for a systematic way of analyzing and assessing them as a completely new ecosystem: the first cloud-native architectural style. This paper defines a preliminary analysis framework in the form of a taxonomy of concepts, encompassing the whole microservices lifecycle, as well as organizational aspects. This framework is necessary to enable effective exploration, understanding, assessing, comparing, and selecting microservice-based models, languages, techniques, platforms, and tools. Then, we analyze state of the art approaches related to microservices using this taxonomy to provide a holistic perspective of available solutions.


title: Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model: Conventional Observation

abstract: This paper presents an approach for employing artificial neural networks (NN) to emulate an ensemble Kalman filter (EnKF) as a method of data assimilation. The assimilation methods are tested in the Simplified Parameterizations PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation model (AGCM), using synthetic observational data simulating localization of balloon soundings. For the data assimilation scheme, the supervised NN, the multilayer perceptrons (MLP-NN), is applied. The MLP-NN are able to emulate the analysis from the local ensemble transform Kalman filter (LETKF). After the training process, the method using the MLP-NN is seen as a function of data assimilation. The NN were trained with data from first three months of 1982, 1983, and 1984. A hind-casting experiment for the 1985 data assimilation cycle using MLP-NN were performed with synthetic observations for January 1985. The numerical results demonstrate the effectiveness of the NN technique for atmospheric data assimilation. The results of the NN analyses are very close to the results from the LETKF analyses, the differences of the monthly average of absolute temperature analyses is of order 0.02. The simulations show that the major advantage of using the MLP-NN is better computational performance, since the analyses have similar quality. The CPU-time cycle assimilation with MLP-NN is 90 times faster than cycle assimilation with LETKF for the numerical experiment.


title: Privacy attitudes and privacy behaviour : A review of current research on the privacy paradox phenomenon

abstract: Do people really care about their privacy? Surveys show that privacy is a primary concern for citizens in the digital age. On the other hand, individuals reveal personal information for relatively small rewards, often just for drawing the attention of peers in an online social network. This inconsistency of privacy attitudes and privacy behaviour is often referred to as the "privacy paradox". In this paper, we present the results of a review of research literature on the privacy paradox. We analyse studies that provide evidence of a paradoxical dichotomy between attitudes and behaviour and studies that challenge the existence of such a phenomenon. The diverse research results are explained by the diversity in research methods, the different contexts and the different conceptualisations of the privacy paradox. We also present several interpretations of the privacy paradox, stemming from social theory, psychology, behavioural economics and, in one case, from quantum theory. We conclude that current research has improved our understanding of the privacy paradox phenomenon. It is, however, a complex phenomenon that requires extensive further research. Thus, we call for synthetic studies to be based on comprehensive theoretical models that take into account the diversity of personal information and the diversity of privacy concerns. We suggest that future studies should use evidence of actual behaviour rather than self-reported behaviour.


title: Pragmatism vs interpretivism in qualitative information systems research

abstract: Qualitative research is often associated with interpretivism, but alternatives do exist. Besides critical research and sometimes positivism, qualitative research in information systems can be performed following a paradigm of pragmatism. This paradigm is associated with action, intervention and constructive knowledge. This paper has picked out interpretivism and pragmatism as two possible and important research paradigms for qualitative research in information systems. It clarifies each paradigm in an ideal-typical fashion and then conducts a comparison revealing commonalities and differences. It is stated that a qualitative researcher must either adopt an interpretive stance aiming towards an understanding that is appreciated for being interesting; or a pragmatist stance aiming for constructive knowledge that is appreciated for being useful in action. The possibilities of combining pragmatism and interpretivism in qualitative research in information systems are analysed. A research case (conducted through action research (AR) and design research (DR)) that combines interpretivism and pragmatism is used as an illustration. It is stated in the paper that pragmatism has influenced IS research to a fairly large extent, albeit in a rather implicit way. The paradigmatic foundations are seldom known and explicated. This paper contributes to a further clarification of pragmatism as an explicit research paradigm for qualitative research in information systems. Pragmatism is considered an appropriate paradigm for AR and DR.


title: You Can ' t Not Believe Everything You Read

abstract: Can people comprehend assertions without believing them? Descartes (1644/1984) suggested that people can and should, whereas Spinoza (1677/1982) suggested that people should but cannot. Three experiments support the hypothesis that comprehension includes an initial belief in the information comprehended. Ss were exposed to false information about a criminal defendant (Experiments 1 and 2) or a college student (Experiment 3). Some Ss were exposed to this information while under load (Experiments 1 and 2) or time pressure (Experiment 3). Ss made judgments about the target (sentencing decisions or liking judgments). Both load and time pressure caused Ss to believe the false information and to use it in making consequential decisions about the target. In Spinozan terms, both manipulations prevented Ss from "unbelieving" the false information they automatically believed during comprehension.


title: Single Miller capacitor frequency compensation with nulling resistor for three-stage amplifiers

abstract: A frequency compensation technique for three- stage amplifiers is introduced. The proposed solution exploits only one Miller capacitor and a resistor in the compensation network. The straightness of the technique is used to design, using a standard CMOS 0.35-mum process, a 1.5-V OTA driving a 150-pF load capacitor. The dc current consumption is about 14 muA at DC and a 1.6-MHz gain-bandwidth product is obtained, providing significant improvement in both MHz-pF/mA and (V/mus)-pF/mA performance parameters.


A Meaning-based English Math Word Problem Solver with Understanding, Reasoning and Explanation


title: Pyruvate kinase type M2 promotes tumour cell exosome release via phosphorylating synaptosome-associated protein 23

abstract: Tumour cells secrete exosomes that are involved in the remodelling of the tumour-stromal environment and promoting malignancy. The mechanisms governing tumour exosome release, however, remain incompletely understood. Here we show that tumour cell exosomes secretion is controlled by pyruvate kinase type M2 (PKM2), which is upregulated and phosphorylated in tumours. During exosome secretion, phosphorylated PKM2 serves as a protein kinase to phosphorylate synaptosome-associated protein 23 (SNAP-23), which in turn enables the formation of the SNARE complex to allow exosomes release. Direct phosphorylation assay and mass spectrometry confirm that PKM2 phosphorylates SNAP-23 at Ser95. Ectopic expression of non-phosphorylated SNAP-23 mutant (Ser95→Ala95) significantly reduces PKM2-mediated exosomes release whereas expression of selective phosphomimetic SNAP-23 mutants (Ser95→Glu95 but not Ser20→Glu20) rescues the impaired exosomes release induced by PKM2 knockdown. Our findings reveal a non-metabolic function of PKM2, an enzyme associated with tumour cell reliance on aerobic glycolysis, in promoting tumour cell exosome release.


title: Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences

abstract: Depression is ranked as the largest contributor to global disability and is also a major reason for suicide. Still, many individuals suffering from forms of depression are not treated for various reasons. Previous studies have shown that depression also has an effect on language usage and that many depressed individuals use social media platforms or the internet in general to get information or discuss their problems. This paper addresses the early detection of depression using machine learning models based on messages on a social platform. In particular, a convolutional neural network based on different word embeddings is evaluated and compared to a classification based on user-level linguistic metadata. An ensemble of both approaches is shown to achieve state-of-the-art results in a current early detection task. Furthermore, the currently popular $ERDE$ E R D E score as metric for early detection systems is examined in detail and its drawbacks in the context of shared tasks are illustrated. A slightly modified metric is proposed and compared to the original score. Finally, a new word embedding was trained on a large corpus of the same domain as the described task and is evaluated as well.


AUDIT-FIRM GROUP APPOINTMENT: AN ARTIFICIAL INTELLIGENCE APPROACH


title: Inventory constrained maritime routing and scheduling for multi-commodity liquid bulk, Part I: Applications and model

abstract: Abstract This paper formulates a model for finding a minimum cost routing in a network for a heterogeneous fleet of ships engaged in pickup and delivery of several liquid bulk products. The problem is frequently encountered by maritime chemical transport companies, including oil companies serving an archipelago of islands. The products are assumed to require dedicated compartments in the ship. The problem is to decide how much of each product should be carried by each ship from supply ports to demand ports, subject to the inventory level of each product in each port being maintained between certain levels that are set by the production rates, the consumption rates, and the storage capacities of the various products in each port. This important and challenging inventory constrained multi-ship pickup–delivery problem is formulated as a mixed-integer nonlinear program. We show that the model can be reformulated as an equivalent mixed-integer linear program with special structure. Over 100 test problems are randomly generated and solved using CPLEX 7.5. The results of our numerical experiments illuminate where problem structure can be exploited in order to solve larger instances of the model. Part II of the sequel will deal with new algorithms that take advantage of model properties.


title: ReDMark: Framework for residual diffusion watermarking based on deep networks

abstract: Abstract Due to the rapid growth of machine learning tools and specifically deep networks in various computer vision and image processing areas, applications of Convolutional Neural Networks for watermarking have recently emerged. In this paper, we propose a deep end-to-end diffusion watermarking framework (ReDMark) which can learn a new watermarking algorithm in any desired transform space. The framework is composed of two Fully Convolutional Neural Networks with residual structure which handle embedding and extraction operations in real-time. The whole deep network is trained end-to-end to conduct a blind secure watermarking. The proposed framework simulates various attacks as a differentiable network layer to facilitate end-to-end training. The watermark data is diffused in a relatively wide area of the image to enhance security and robustness of the algorithm. Comparative results versus recent state-of-the-art researches highlight the superiority of the proposed framework in terms of imperceptibility, robustness and speed.


title: Clinical features, diagnostic criteria, and management of Coffin-Siris syndrome

abstract: Coffin–Siris syndrome (OMIM#135900) is a multiple congenital anomaly syndrome classically characterized by hypo‐ or aplasia of the fifth digit nails or phalanges, as well as coarse facial features, sparse scalp hair, and moderate to severe cognitive and/or developmental delay. The recent identification of molecular etiologies has served to effectively characterize a large set of patients who have been described with Coffin–Siris between the time of its initial description and the present. However, despite recent advances, a number of patients who traditionally fit the diagnosis have yet to have identified causes. This could be due to patients who lie outside the defined phenotype, or alternatively, to additional as yet unidentified genes which may play roles. Here we outline the range of clinical features described in the broader diagnostic category, review the continuing phenotypic challenges and note those subsets of patients for whom molecular causes have yet to be clarified. Finally, we discuss recommendations for clinical management of these individuals. © 2014 Wiley Periodicals, Inc.


title: Design of a quad element patch antenna at 5.8 GHz

abstract: This paper presents simulation and experimental verification of a quad microstrip patch antenna that operates at 5.8 GHz. Sonnet antenna design software was used to simulate the performance of the antenna. To reduce the design's complexity and the computational load, the antenna and the feeding lines were simulated separately. An optimization was done for each subpart to get the optimum desired results. Finally, all the subparts were merged and the final structure was simulated to check the performance. A prototype of the antenna was fabricated on a double sided PCB substrate (relative permittivity=10.2, thickness=1.28 mm) using a PCB milling machine. The s 11 of −14 dB and −18.8 dB and maximum gain of 6.2 dB and 4.2 dB were obtained, from the simulation and experimental measurements, respectively.


Events Detection, Coreference and Sequencing: What's next? Overview of the TAC KBP 2017 Event Track


title: Preventing Covert Webcam Hacking in the Civilian and Governmental Sectors

abstract: This paper looks at both the civilian and governmental sectors to examine covert webcam hacking. By using remote administration tools commonly used by hackers, a specific executable is created and installed on a computer. Another computer then attempts to access the infected computer and utilize its remote access to gain information through the webcam. By going through this process, preventative measures can be examined within the civilian sector. This paper also looks at ways that governmental webcams could be hacked and examines similarities and difference between the governmental and civilian sectors. By looking at difference the governmental sector has with webcams, preventative measures can be altered or created in order to prevent covert webcam hacking.


title: Towards Linear Time Neural Machine Translation with Capsule Networks

abstract: In this study, we first investigate a novel capsule network with dynamic routing for linear time Neural Machine Translation (NMT), referred as \textsc{CapsNMT}. \textsc{CapsNMT} uses an aggregation mechanism to map the source sentence into a matrix with pre-determined size, and then applys a deep LSTM network to decode the target sequence from the source representation. Unlike the previous work \cite{sutskever2014sequence} to store the source sentence with a passive and bottom-up way, the dynamic routing policy encodes the source sentence with an iterative process to decide the credit attribution between nodes from lower and higher layers. \textsc{CapsNMT} has two core properties: it runs in time that is linear in the length of the sequences and provides a more flexible way to select, represent and aggregates the part-whole information of the source sentence. On WMT14 English-German task and a larger WMT14 English-French task, \textsc{CapsNMT} achieves comparable results with the state-of-the-art NMT systems. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for sequence to sequence problems.


title: The mechanisms of muscle hypertrophy and their application to resistance training.

abstract: The quest to increase lean body mass is widely pursued by those who lift weights. Research is lacking, however, as to the best approach for maximizing exercise-induced muscle growth. Bodybuilders generally train with moderate loads and fairly short rest intervals that induce high amounts of metabolic stress. Powerlifters, on the other hand, routinely train with high-intensity loads and lengthy rest periods between sets. Although both groups are known to display impressive muscularity, it is not clear which method is superior for hypertrophic gains. It has been shown that many factors mediate the hypertrophic process and that mechanical tension, muscle damage, and metabolic stress all can play a role in exercise-induced muscle growth. Therefore, the purpose of this paper is twofold: (a) to extensively review the literature as to the mechanisms of muscle hypertrophy and their application to exercise training and (b) to draw conclusions from the research as to the optimal protocol for maximizing muscle growth.


title: Kernel Trick Embedded Gaussian Mixture Model

abstract: In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter estimation algorithm for GMM in feature space. Kernel GMM could be viewed as a Bayesian Kernel Method. Compared with most classical kernel methods, the proposed method can solve problems in probabilistic framework. Moreover, it can tackle nonlinear problems better than the traditional GMM. To avoid great computational cost problem existing in most kernel methods upon large scale data set, we also employ a Monte Carlo sampling technique to speed up kernel GMM so that it is more practical and efficient. Experimental results on synthetic and real-world data set demonstrate that the proposed approach has satisfing performance.


title: A survey of commercial frameworks for the Internet of Things

abstract: In 2011 Ericsson and Cisco estimated 50 billion Internet connected devices by 2020, encouraged by this industry is developing application frameworks to scale the Internet of Things. This paper presents a survey of commercial frameworks and platforms designed for developing and running Internet of Things applications. The survey covers frameworks supported by big players in the software and electronics industries. The frameworks are evaluated against criteria such as architectural approach, industry support, standards based protocols and interoperability, security, hardware requirements, governance and support for rapid application development. There is a multitude of frameworks available and here a total 17 frameworks and platforms are considered. The intention of this paper is to present recent developments in commercial IoT frameworks and furthermore, identify trends in the current design of frameworks for the Internet of Things; enabling massively connected cyber physical systems.


title: ELECTROMYOGRAPHIC MUSCLE ACTIVITY IN CURL-UP EXERCISES WITH DIFFERENT POSITIONS OF UPPER AND LOWER EXTREMITIES

abstract: The purpose of the study was to evaluate the electromyographic (EMG) activity of muscles in curl-up exercises depending on the position of the upper and lower extremities. From the perspective of biomechanics, different positions of the extremities result in shifting the center of gravity and changing muscular loads in abdominal strength exercises. The subjects of the research were 3 healthy students (body mass 53-56 kg and height 163-165 cm) with no history of low back pain or abdominal surgery. Subjects completed 18 trials for each of the 9 exercises (static curl-up with 3 positions of the upper and 3 position of the lower extremities). The same experiment with the same subjects was conducted on the next day. The EMG activity of rectus abdominis (RA), erector spinae (ES), and quadriceps femoris-long head (rectus femoris [RF]) was examined during the exercises. The surface electrical activity was recorded for the right and left sides of each muscle. The raw data for each muscle were rectified and integrated. The statistical analysis showed that changing the position of upper extremities in the examined exercises affects the EMG activity of RA and ES but does not significantly affect the EMG activity of RF. Additionally, it was found that curl-up exercises with the upper extremities extended behind the head and the lower extremities flexed at 90° in the hip and knee joints involve RA with the greatest intensity, whereas curl-up exercises with the upper extremities extended along the trunk and the lower extremities flexed at 90° in the hip and knee joints involve RA with the lowest intensity.


title: On Optimal Proactive Caching for Mobile Networks With Demand Uncertainties

abstract: Mobile data users are known to possess predictable characteristics both in their interests and activity patterns. Yet, their service is predominantly performed, especially at the wireless edges, “reactively” at the time of request, typically when the network is under heavy traffic load. This strategy incurs excessive costs to the service providers to sustain on-time (or delay-intolerant) delivery of data content, while their resources are left underutilized during the light-loaded hours. This motivates us in this work to study the problem of optimal “proactive” caching whereby, future delay-intolerant data demands can be served within a given prediction window ahead of their actual time-of-arrival to minimize service costs. To that end, we first establish fundamental bounds on the minimum possible cost achievable by any proactive policy, as a function of the prediction uncertainties. These bounds provide interesting insights on the impact of uncertainty on the maximum achievable proactive gains. We then propose specific proactive caching strategies, both for uniform and fluctuating demand patterns, that are asymptotically-optimal in the limit as the prediction window size grows while the prediction uncertainties remain fixed. We further establish the exponential convergence rate characteristics of our proposed solutions to the optimal, revealing close-to-optimal performance characteristics of our designs even with small prediction windows. Also, proactive design is contrasted with its reactive and delay-tolerant counter-parts to obtain interesting results on the unavoidable costs of uncertainty and the potentially remarkable gains of proactive operation.


title: Developing apps for mobile phones

abstract: The current popularity of smartphones is mostly due to the possibility of selecting mobile apps that suit one's needs. It is tempting for a developer to conclude that the development of mobile apps is simple. Unfortunately, this is not yet the case. Indeed, there are so many different mobile app paradigms and the choice for the appropriate one could be a challenging task. In fact, understanding which mobile app paradigm fits best to a specific development circumstance is crucial and could have direct influence on quality, development cost and therefore sale revenue of the apps. The goal of this paper is to contribute to the selection of suitable mobile app paradigms in their development. Five mobile paradigms namely native apps, platform-based apps, mobile widgets, Web apps and HTML mobile apps are identified and described. They are then evaluated from developer viewpoint based on a set of selected criteria. The outcome of the evaluation suggests which paradigm fits better to a certain type of apps and also developers. In order to ensure the objectiveness and accuracy of the evaluation, we build an object recognition app, which is very challenging and exciting to develop, using the two most promising paradigms.


title: Simple Optimal String Matching Algorithm

abstract: We present a new string matching algorithm linear in the worst case (in O(m + n) where n is the size of the text and m the size of the searched word, both taken on an alphabet Σ) and optimal on average (with equiprobability and independence of letters, in O(m + n log|Σ| m/m)). Of all the algorithms that verify these two complexities, our is the simplest since it uses only a single structure, a suffix automaton. Moreover, its preprocessing phase is linearly dynamical, i.e. it is possible to search the words p1, then p1p2, p1p2p3, . . . , p1p2p3 . . . pi with O(Σ|pi|) total preprocessing time. Among the algorithms that verify this property (for instance the Knuth-Morris-Pratt) our algorithm is the only one to be optimal on average.


title: A Scalable FPGA-Based Accelerator for High-Throughput MCMC Algorithms

abstract: Markov Chain Monte Carlo (MCMC) algorithms are used to obtain samples from any target probability distribution and are widely used in stochastic processing techniques. Stochastic processing techniques such as machine learning and image processing need to compute large amounts of data in real-time, thus high throughput MCMC samplers are of utmost importance. Parallel Tempering (PT) MCMC has proven better mixing and convergence for high-dimensional and multi-modal distributions compared to other popular MCMC algorithms. In this paper, we employ a special case of Dth order Markov chains to modify the PT-MCMC algorithm, named "Multiple Parallel Tempering" (MPT). The modification converts one MCMC sampler into multiple independent samplers that generate and interleave their samples on one output line each clock cycle. A fully scalable and pipelined hardware accelerator for the PT and proposed MPT sampler is designed and implemented on Artix-7 Xilinx FPGA for chain numbers of 1, 2, and 8. The post-place and route FPGA implementation results indicate that the throughput of the proposed MPT sampler for chain numbers 1, 2, and 8 achieves 31x, 31x, and 28x respectively higher as compared to PT sampler with the same chain number configuration.


title: An SIW-Based Large-Scale Corporate-Feed Array Antenna

abstract: A $16 \times 16$ substrate-integrated waveguide (SIW) cavity-backed array antenna is proposed in this paper. The array consists of two layers. The top layer employs novel SIW-based subarrays with a compact size. The bottom layer is an $8 \times 8$ SIW corporate-feed network to feed the subarrays. The Chebyshev amplitude weighting is employed in the feed network, which substantially reduces the side lobe level (SLL). The array antenna is fabricated using low-cost printed circuit board technology. The experimental results show that the proposed array antenna has a large bandwidth of 15% from 18.5 to 21.5 GHz with a peak gain of 29.1 dBi at 20.5 GHz. Across the entire band, high radiation efficiency above 62% and a low SLL below $- 17\,\mathbf{dB}$ are realized. The design principle can be used as a guidance for large-scale planar array antenna design and the proposed antenna can be used as a receiving antenna located on the ground in satellite communication systems.


title: A novel concept-level approach for ultra-concise opinion summarization

abstract: The task of ultra-concise opinion summarization is addressed.Syntactic simplification, sentence regeneration and concept representation are used.Our approach outperforms a number of state-of-the-art systems.The best readability results using simplification are around 2.83 out of 3. The Web 2.0 has resulted in a shift as to how users consume and interact with the information, and has introduced a wide range of new textual genres, such as reviews or microblogs, through which users communicate, exchange, and share opinions. The exploitation of all this user-generated content is of great value both for users and companies, in order to assist them in their decision-making processes. Given this context, the analysis and development of automatic methods that can help manage online information in a quicker manner are needed. Therefore, this article proposes and evaluates a novel concept-level approach for ultra-concise opinion abstractive summarization. Our approach is characterized by the integration of syntactic sentence simplification, sentence regeneration and internal concept representation into the summarization process, thus being able to generate abstractive summaries, which is one the most challenging issues for this task. In order to be able to analyze different settings for our approach, the use of the sentence regeneration module was made optional, leading to two different versions of the system (one with sentence regeneration and one without). For testing them, a corpus of 400 English texts, gathered from reviews and tweets belonging to two different domains, was used. Although both versions were shown to be reliable methods for generating this type of summaries, the results obtained indicate that the version without sentence regeneration yielded to better results, improving the results of a number of state-of-the-art systems by 9%, whereas the version with sentence regeneration proved to be more robust to noisy data.


title: Mental health nurses' views on therapeutic optimism.

abstract: Registered nurses (RN) coordinate acute mental health units on a 24-hour basis and it behoves researchers to actually ask these nurses what they think contributes to their ability to work with patients in optimistic ways. In this study, 40 RN working in acute mental health settings were asked a series of questions to explore positive aspects of nursing work, which includes therapeutic optimism. Three themes were identified: (i) different ways nurses foster therapeutic optimism; (ii) perceptions of how an optimistic environment is fostered, and (iii) improvement of ward culture. Findings show the pivotal role mental health nurses have in improving teamwork, good communication, sharing, and collaboration, in addition to preceptoring and supervision. Furthermore, effective clinical management is essential to therapeutic optimism and, in this research, is considered to be the aspect of acute mental health nursing most relevant to improving the ward culture.


title: Significant Pattern Mining on Continuous Variables

abstract: Significant pattern mining, the search for sets of binary features that are statistically significantly enriched in a class of objects, is of fundamental importance in a wide range of applications from economics to statistical genetics. Still, all existing approaches make the restrictive assumption that the features are binary and require a binarization of continuous data during preprocessing, which often leads to a loss of information. Here, we solve the open problem of significant pattern mining on continuous variables. Our approach detects all patterns that are statistically significantly associated with a class of interest, while rigorously correcting for multiple testing. Key to this approach is the use of Spearman's rank correlation coefficient to represent the frequency of a pattern. Our experiments demonstrate that our novel approach detects true patterns with higher precision and recall than competing methods that require a prior binarization of the data.


title: Logic for problem solving

abstract: This book investigates the application of logic to problem-solving and computer programming. It assumes no previous knowledge of these fields, and may be Karl duncker in addition to make difficult fill one of productive. The unifying epistemological virtues of program variables tuples in different terminologies he wants. Functional fixedness which appropriate solutions are most common barrier. Social psychologists over a goal is represented can take. There is often largely unintuitive and, all be overcome standardized procedures like copies? Functional fixedness it can be made possible for certain fields looks. In the solution paths or pencil. After toiling over the ultimate mentions that people cling rigidly to strain on. Luckily the book for knowledge of atomic sentences or fundamental skills. Functional fixedness is a problem solving techniques such.


title: Polynomial blending in a mesh hole-filling application

abstract: In this paper, a feature-preserving mesh hole-filling algorithm is realized by the polynomial blending technique. We first search for feature points in the neighborhood of the hole. These feature points allow us to define the feature curves with missing parts in the hole. A polynomial blending curve is constructed to complete the missing parts of the feature curves. These feature curves divide the original complex hole into small simple sub-holes. We use the Bezier-Lagrange hybrid patch to fill each sub-hole. The experimental results show that our mesh hole-filling algorithm can effectively restore the original shape of the hole.


title: Academic mobility as a key factor of quality assurance in tertiary education

abstract: Academic mobility of both faculty and students forms a critical element of the international dimension of higher education and is one of the most important factors of quality assurance of higher education institutions worldwide. Internationalization and globalization of Russian higher education system is having one of the major impacts on the quality management and assurance. Faculty and student mobility figures are now one of the core factors for obtaining state accreditation for Russian universities as well as an important indicator showing the university success and prestige. The aim of the paper is a closer look at the perspectives and results of the academic mobility, the factors stimulating and discouraging mobility, and the outcomes of the mobility influencing the education in Russian universities. Siberian State Aerospace University has had more than 10 years experience in organizing student mobility. Most of the problems in carrying out international activities are common for the whole Russia but still there are some peculiarities featuring technical universities situated in the centre of Russia.


title: An image topic model for image denoising

abstract: Abstract Topic model is a powerful tool for the basic document or image processing tasks. In this study we introduce a novel image topic model, called Latent Patch Model (LPM), which is a generative Bayesian model and assumes that the image and pixels are connected by a latent patch layer. Based on the LPM, we further propose an image denoising algorithm namely multiple estimate LPM (MELPM). Unlike other works, the proposed denoising framework is totally implemented on the latent patch layer, and it is effective for both Gaussian white noises and impulse noises. Experimental results demonstrate that LPM performs well in representing images. And its application in image denoising achieves competitive PSNR and visual quality with conventional algorithms.


title: Moving object detection in video satellite image based on deep learning

abstract: Moving object detection in video satellite image is studied. A detection algorithm based on deep learning is proposed. The small scale characteristics of remote sensing video objects are analyzed. Firstly, background subtraction algorithm of adaptive Gauss mixture model is used to generate region proposals. Then the objects in region proposals are classified via the deep convolutional neural network. Thus moving objects of interest are detected combined with prior information of sub-satellite point. The deep convolution neural network employs a 21-layer residual convolutional neural network, and trains the network parameters by transfer learning. Experimental results about video from Tiantuo-2 satellite demonstrate the effectiveness of the algorithm.


title: Address Space Layout Permutation (ASLP): Towards Fine-Grained Randomization of Commodity Software

abstract: Address space randomization is an emerging and promising method for stopping a broad range of memory corruption attacks. By randomly shifting critical memory regions at process initialization time, address space randomization converts an otherwise successful malicious attack into a benign process crash. However, existing approaches either introduce insufficient randomness, or require source code modification. While insufficient randomness allows successful brute-force attacks, as shown in recent studies, the required source code modification prevents this effective method from being used for commodity software, which is the major source of exploited vulnerabilities on the Internet. We propose Address Space Layout Permutation (ASLP) that introduces high degree of randomness (or high entropy) with minimal performance overhead. Essential to ASLP is a novel binary rewriting tool that can place the static code and data segments of a compiled executable to a randomly specified location and performs fine-grained permutation of procedure bodies in the code segment as well as static data objects in the data segment. We have also modified the Linux operating system kernel to permute stack, heap, and memory mapped regions. Together, ASLP completely permutes memory regions in an application. Our security and performance evaluation shows minimal performance overhead with orders of magnitude improvement in randomness (e.g., up to 29 bits of randomness on a 32-bit architecture).


title: CATS: Characterizing automation of Twitter spammers

abstract: Twitter, with its rising popularity as a micro-blogging website, has inevitably attracted the attention of spammers. Spammers use myriad of techniques to evade security mechanisms and post spam messages, which are either unwelcome advertisements for the victim or lure victims in to clicking malicious URLs embedded in spam tweets. In this paper, we propose several novel features capable of distinguishing spam accounts from legitimate accounts. The features analyze the behavioral and content entropy, bait-techniques, and profile vectors characterizing spammers, which are then fed into supervised learning algorithms to generate models for our tool, CATS. Using our system on two real-world Twitter data sets, we observe a 96% detection rate with about 0.8% false positive rate beating state of the art detection approach. Our analysis reveals detection of more than 90% of spammers with less than five tweets and about half of the spammers detected with only a single tweet. Our feature computation has low latency and resource requirement making fast detection feasible. Additionally, we cluster the unknown spammers to identify and understand the prevalent spam campaigns on Twitter.


title: Mining Social Media Content for Crime Prediction

abstract: Social media provides increasing opportunities for users to voluntarily share their thoughts and concerns in a large volume of data. While user-generated data from each individual may not provide considerable information, when combined, they include hidden variables, which may convey significant events. In this paper, we pursue the question of whether social media context can provide socio-behavior "signals" for crime prediction. The hypothesis is that crowd publicly available data in social media, in particular Twitter, may include predictive variables, which can indicate the changes in crime rates. We developed a model for crime trend prediction where the objective is to employ Twitter content to identify whether crime rates have dropped or increased for the prospective time frame. We also present a Twitter sampling model to collect historical data to avoid missing data over time. The prediction model was evaluated for different cities in the United States. The experiments revealed the correlation between features extracted from the content and crime rate directions. Overall, the study provides insight into the correlation of social content and crime trends as well as the impact of social data in providing predictive indicators.


title: A 14b 20mW 640MHz CMOS CT /spl Delta//spl Sigma/ ADC with 20MHz Signal Bandwidth and 12b ENOB

abstract: A 3rd-order single-loop CT DeltaSigma modulator with a 4b internal quantizer operating at 640MHz achieves 76dB SNR, -78dB THD, and 74dB SINAD in a 20MHz signal bandwidth with an OSR of 16. The modulator operates between 20 to 40MS/S output data rate and dissipates 20mW from a 1.2V supply at 40MS/S. The degradation of stability due to excess loop delay is solved with a quantizer feedback architecture


title: From subjects to partners: experience of a project in Rio de Janeiro Brazil.

abstract: Typically sex workers are used to provide information some of which is very invasive and unnecessary with no meaning and mostly asked only to satisfy the curiosity of researchers. Another common use of sex workers in research is to facilitate the access of researchers to respondents as it is not easy to enter most settings. For a very long time we have been considered hard to reach and have been used to provide access to sex workers and sex workers neighbourhoods exposing ourselves to several dangers mainly acquiring us the mistrust of colleagues and attracting the attention of the police and other authorities. Sex workers are also used to provide samples (blood urine or even genital secretions) for testing. Donors have become very demanding particularly since the HIV pandemic to know the seroprevalence of sex workers in different regions of the world. In some extreme cases – but not rarely – the participants in this kind of research do not have access to the results of their tests and do not benefit from participating in this kind of research. Furthermore this type of research has contributed to an increase in the stigma against us as some figures have been used to justify the idea that sex workers are ‘vectors’ of HIV from ‘higher to lower incidence populations’. Most international ethical guidelines1 regarding research in human beings do not apply to sex workers. Basically people who participate in any kind of research must understand the objectives risks and benefits and several research projects with sex workers simply ignore this very basic principle. Sex workers who participated in research often just say that “some people came here to ask some questions”. It is not common to see sex workers taking part in ethical committees or participating in the whole process of research design. (excerpt)


title: Graph Signal Processing – A Probabilistic Framework

abstract: This theoretical paper aims to provide a probabilistic framework for graph signal processing. By modeling signals on graphs as Gaussian Markov Random Fields, we present numerous important aspects of graph signal processing, including graph construction, graph transform, graph downsampling, graph prediction, and graph-based regularization, from a probabilistic point of view. As examples, we discuss a number of methods for constructing graphs based on statistics from input data sets; we show that the graph transform is the optimal linear transform to decorrelate the signal; we describe the optimality of the Kron reduction for graph downsampling in a probabilistic sense; and we derive the optimal predictive transform coding scheme applicable to both motion prediction and intra predictive coding.


title: Power-Synchronization Control of Grid-Connected Voltage-Source Converters

abstract: In this paper, a novel control method of grid-connected voltage-source converters (VSCs) is proposed. The method can be generally applied for all grid-connected VSCs but may be of most importance in high-voltage dc (HVDC) applications. Different from the previous control methods, the proposed method utilizes the internal synchronization mechanism in ac systems, in principle, similar to the operation of a synchronous machine. By using this type of power-synchronization control, the VSC avoids the instability caused by a standard phase-locked loop in a weak AC-system connection. Moreover, a VSC terminal can give the weak ac system strong voltage support, just like a normal synchronous machine does. The control method is verified by both analytical models and time simulations.


title: Comparing the performance of object databases and ORM tools

abstract: The currently popular distributed, n-tiered, object-oriented application architecture provokes many design debates. Designs of such applications are often divided into logical 'tiers' -- usually user interface, business logic and domain object, or data, tiers, each with their own design issues. In particular, the latter contains data that needs to be stored and retrieved from permanent storage. Decisions need to be made as to the most appropriate way of doing this -- the choices are usually whether to use an object database, to communicate directly with a relational database, or to use object-relational mapping (ORM) tools to allow objects to be translated to and from relational form.Most often, depending on the perceived profile of the application, architects make these decisions using rules of thumb derived from particular experience or the design patterns literature. Examples include: object-oriented databases ease programming, relational databases ease report generation and data mining; object-oriented databases are good for navigation around an object model, relational databases are good for sequential processing and complex queries; if you are writing an application from scratch, use an object database, if you need to integrate to various sources of legacy data, use an ORM tool. Although helpful, these rules are often highly context-dependent and are often misapplied.Research into the nature and magnitude of 'design forces' in this area has resulted in a series of benchmarks, intended to allow architects to more clearly understand the implications of design decisions concerning object persistence. In this paper, the performance of selected open source object persistence tools is investigated, to attempt to clarify the myths surrounding the performance of the different options. In particular, we compare Hibernate, representative of the ORM stable, and db4o, representative of object-oriented databases. The OO7 benchmark is used to compare the speed of execution of a suite of typical persistence-related operations in both candidates. We then propose some preliminary explanations of the sometimes surprising results.


title: Contingency Planning Over Probabilistic Obstacle Predictions for Autonomous Road Vehicles

abstract: This paper presents a novel optimization-based path planner that is capable of planning multiple contingency paths to directly account for uncertainties in the future trajectories of dynamic obstacles. This planner addresses the particular problem of probabilistic collision avoidance for autonomous road vehicles that are required to safely interact, in close proximity, with other vehicles with unknown intentions. The presented path planner utilizes an efficient spline-based trajectory representation and fast but accurate collision probability bounds to simultaneously optimize multiple continuous contingency paths in real time. These collision probability bounds are efficient enough for real-time evaluation, yet accurate enough to allow for practical close-proximity driving behaviors such as passing an obstacle vehicle in an adjacent lane. An obstacle trajectory clustering algorithm is also presented to enable the path planner to scale to multiple-obstacle scenarios. Simulation results show that the contingency planner allows for a more aggressive driving style than planning a single path without compromising the overall safety of the robot.


title: Rolling Guidance Filter

abstract: Images contain many levels of important structures and edges. Compared to masses of research to make filters edge preserving, finding scale-aware local operations was seldom addressed in a practical way, albeit similarly vital in image processing and computer vision. We propose a new framework to filter images with the complete control of detail smoothing under a scale measure. It is based on a rolling guidance implemented in an iterative manner that converges quickly. Our method is simple in implementation, easy to understand, fully extensible to accommodate various data operations, and fast to produce results. Our implementation achieves realtime performance and produces artifact-free results in separating different scale structures. This filter also introduces several inspiring properties different from previous edge-preserving ones.


title: Beetle Swarm Optimization Algorithm:Theory and Application

abstract: In this paper, a new meta-heuristic algorithm, called beetle swarm optimization algorithm, is proposed by enhancing the performance of swarm optimization through beetle foraging principles. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization algorithm, genetic algorithm (GA) and grasshopper optimization algorithm . Numerical experiments show that the beetle swarm optimization algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblaus optimization problem, are also considered and the proposed beetle swarm optimization algorithm is shown to be competitive in those applications.


title: Data augmentation instead of explicit regularization

abstract: Modern deep artificial neural networks have achieved impressive results through models with a very large number of parameters---compared to the number of training examples---that control overfitting with the help of regularization. Regularization can be implicit, as is the case of stochastic gradient descent and parameter sharing in convolutional layers, or explicit. Explicit regularization techniques, most common forms are weight decay and dropout, reduce the effective capacity of the model and typically require the use of deeper and wider architectures to compensate for the reduced capacity. Although these techniques have proven successful in terms of improved generalization, they seem to waste some capacity. In contrast, data augmentation techniques rely on increasing the number of training examples to improve generalization without reducing the effective capacity. Unlike weight decay and dropout, data augmentation is independent of the specific network architecture, since it is applied on the training data. In this paper we systematically compare data augmentation and explicit regularization on some popular architectures and data sets. Our results demonstrate that data augmentation alone can achieve the same performance or higher as regularized models and exhibits much higher adaptability to changes in the architecture and the amount of training data.


title: Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models

abstract: Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. Conventional techniques usually focus on visualizing the internal logic of a specific model type (i.e., deep neural networks), lacking the ability to extend to a more complex scenario where different model types are integrated. To this end, Manifold is designed as a generic framework that does not rely on or access the internal logic of the model and solely observes the input (i.e., instances or features) and the output (i.e., the predicted result and probability distribution). We describe the workflow of Manifold as an iterative process consisting of three major phases that are commonly involved in the model development and diagnosis process: inspection (hypothesis), explanation (reasoning), and refinement (verification). The visual components supporting these tasks include a scatterplot-based visual summary that overviews the models' outcome and a customizable tabular view that reveals feature discrimination. We demonstrate current applications of the framework on the classification and regression tasks and discuss other potential machine learning use scenarios where Manifold can be applied.