title: HOW MUCH DO HIGH-PERFORMANCE WORK PRACTICES MATTER ? A META-ANALYSIS OF THEIR EFFECTS ON ORGANIZATIONAL PERFORMANCE

abstract: Although there is growing evidence that high performance work practices (HPWPs) affect organizational performance, varying sample characteristics, research designs, practices examined, and organizational performance measures used has led extant findings to vary dramatically, making the size of the overall effect difficult to estimate. We use meta-analysis to estimate the effect size and test whether effects are larger for (a) HPWP systems versus individual practices, (b) operational versus financial performance measures, and (c) manufacturing versus service organizations. Statistical aggregation of 92 studies reveals an overall correlation that we estimate at .20. Also, the relationship is stronger when researchers examine systems of HPWPs and among manufacturers, but it appears invariant across performance measures. We use our findings as a basis to offer 4 suggestions intended to shape research practices such that future meta-analyses might answer today's emerging questions.


title: ICDAR2015 competition on recognition of documents with complex layouts - RDCL2015

abstract: This paper presents an objective comparative evaluation of page segmentation and region classification methods for documents with complex layouts. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2015, presenting the results of the evaluation of eight methods - four submitted, two state-of-the-art systems (one commercial and one open-source) and their two immediately previous versions. Three scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions and two evaluating both segmentation and region classification (one with emphasis on text and the other focusing only on text). The results indicate that an innovative approach has a clear advantage but there is still a considerable need to develop robust methods that deal with layout challenges, especially with the non-text content.


title: [Accessory renal arteries in human fetuses].

abstract: Using conventional anatomical methods, renal arteries of 140 human fetuses were studied. It was found (21.1%) that the accessory renal arteries occurred in a three-fold manner: 1. as single arteries (19.2%), 2. as double arteries (2.1%) and 3. as triplex arteries (0.7%). More often they originated from the right part of the circumference of the abdominal aorta, mainly in the female fetuses. These arteries penetrated the following segments of the kidney: the inferior (12.9%), the superior (2.3%), the anterior inferior (2.8%), the posterior (2.1%) and the anterior superior (1.5%). They crossed the renal pelvis more often in front (12.2%) than from behind of it (5%). The frequency of the occurrence of the accessory arteries depends not from the age of the fetus.


title: Detection of gate-oxide defects with timing tests at reduced power supply

abstract: In this paper, we focus on the detection of small gate-oxide defects, which can escape production tests but lead to early-life-failures (ELF) during normal operation. Very-Low-Voltage (VLV) and MinVDD testing have been proposed in the past to screen such “weak” ICs. However, small defects that are not severe enough to trigger logic failures can still escape such tests given the fact that power supply voltage cannot be arbitrarily lowered in a given technology. We suggest a novel approach for increasing the sensitivity of detection of these small gate-oxide defects by applying timing tests in a reduced power supply environment. While not severe enough to cause logic failures, small oxide defects can still introduce observable anomalies in the timing of affected paths, which is amplified at reduced power supply voltages. Experimental simulation results using NanGate 45nm technology are provided to substantiate our conclusions.


The regulation of collagen production in normal lung and during interstitial lung disease.


title: A 14b 40MS/s Redundant SAR ADC with 480MHz Clock in 0.13pm CMOS

abstract: A 2-channel time-interleaved 40MS/s SAR ADC with redundancy is presented. The 0.13mum 1.5V CMOS design runs at 480MHz iteration clock and features 89dB THD and 81dB SNDR. Including the PLL, the second-order anti-alias filter, and reference buffer, the chip consumes 66mW and occupies 0.55mm2.


title: FAKE FACE DATABASE AND PRE- PROCESSING

abstract: Face plays an ethical role in human interaction compared to other biometrics. It is most popular non-intrusive and non-invasive biometrics whose image can easily be snapped without user co-operation. That is why; criminals and imposters always try to tamper their facial identity. Therefore, face tampering detection is one of the most important bottlenecks for security, commercial and industrial orbit. Face tampering detection is one of the most important bottlenecks for security, commercial and industrial orbit. In particular, few researchers have addressed the challenges for the disguise detection but inadequacy is benchmark database in public domain. This paper addresses these problems by preparing of three category of tampered face database within the framework of FRT (Facial Recognition Technology) and evaluates the performance of this database on face recognition algorithms. These categories of database are dummy, colour imposed and masked face.


title: Learning driving styles for autonomous vehicles from demonstration

abstract: It is expected that autonomous vehicles capable of driving without human supervision will be released to market within the next decade. For user acceptance, such vehicles should not only be safe and reliable, they should also provide a comfortable user experience. However, individual perception of comfort may vary considerably among users. Whereas some users might prefer sporty driving with high accelerations, others might prefer a more relaxed style. Typically, a large number of parameters such as acceleration profiles, distances to other cars, speed during lane changes, etc., characterize a human driver's style. Manual tuning of these parameters may be a tedious and error-prone task. Therefore, we propose a learning from demonstration approach that allows the user to simply demonstrate the desired style by driving the car manually. We model the individual style in terms of a cost function and use feature-based inverse reinforcement learning to find the model parameters that fit the observed style best. Once the model has been learned, it can be used to efficiently compute trajectories for the vehicle in autonomous mode. We show that our approach is capable of learning cost functions and reproducing different driving styles using data from real drivers.


title: Women prefer men who use metaphorical language when paying compliments in a romantic context

abstract: Language plays an important role in romantic attachment. However, it is unclear whether the structure and topic of language use might influence potential mate choice. We investigated 124 female students' preference for compliments paid by males incorporating either literal or metaphoric (conventional/novel) language and targeting their appearance or possessions (house) throughout their menstrual cycle. Male faces paired with novel metaphorical compliments were rated as more attractive by women than those paired with literal ones. Compliments targeting appearance increased male attractiveness more than possessions. Interestingly, compliments on appearance using novel metaphors were preferred by women in a relationship during the fertile phase but by single women during the luteal phase. A similar pattern of altered face attraction ratings was subsequently shown by subjects in the absence of the verbal compliments and even though they were unable to recognize the faces. Thus the maintained attraction bias for faces previously associated with figurative language compliments appears to be unconscious. Overall this study provides the first evidence that women find men who typically use novel metaphorical language to compliment appearance more attractive than those using prosaic language or complimenting possessions. The evolutionary significance for such a language use bias in mate selection is discussed.


title: Can mobile cloudlets support mobile applications?

abstract: A mobile cloudlet is a set of resource-rich mobile devices—referred to as cloudlet nodes—that an initiator mobile device can connect to for services. In this paper, we examine the fundamental mobile cloudlet properties that unfoldwhether and when a mobile cloudlet can provide mobile application ser- vice. Specifically, we investigate thecloudlet size,cloudlet node's lifetime and reachable time. Traces and mathematical analysis demonstrate that 1) the more frequently mobile devices meet, the larger the pool of computing resources an initiator can access; 2) intermittent connection between devices has little adverse effect on the optimal computing performance of mobile cloudlet in the long run; 3) the ratio E(TC)/(E(TI) + E(TC)) indicates the connection likelihood of an initiator and a cloudlet node (i.e., reachability of the cloudlet node), where TC and TI are their contact and inter-contact time. We further derive upper and lower bounds on computing capacity and computing speed of a mobile cloudlet. An initiator can use both bounds to decide whether to offload its task to remote clouds or local mobile cloudlets for better mobile application services.


title: Through silicon via technology — processes and reliability for wafer-level 3D system integration

abstract: 3D integration is a rapidly growing topic in the semiconductor industry that encompasses different types of technologies. The paper addresses one of the most promising technologies which uses through silicon vias (TSV) for interconnecting stacked devices on wafer-level to perform high density interconnects with a good electrical performance at the smallest form factor for 3D architectures. Fraunhofer IZM developed a post frontend 3D integration process, the so- called ICV-SLID technology based on metal bonding using solid-liquid-interdiffusion (SLID) soldering. The SLID metal system provides the mechanical and the electrical connection, both in one single step. The ICV-SLID fabrication process is well suited for the cost-effective production of both, high- performance applications (e.g. 3D microprocessor) and highly miniaturized multi-functional systems. The latter preferably in combination with wafer-level die stacking, as e.g. Thin Chip Integration (TCI) or SnAg-microbump technologies. The fabrication of distributed wireless sensor systems (e. g. e-CUBESreg) is a typical example for the need of such mixed approaches.


title: Efficient processing of joins on set-valued attributes

abstract: Object-oriented and object-relational DBMS support set valued attributes, which are a natural and concise way to model complex information. However, there has been limited research to-date on the evaluation of query operators that apply on sets. In this paper we study the join of two relations on their set-valued attributes. Various join types are considered, namely the set containment, set equality, and set overlap joins. We show that the inverted file, a powerful index for selection queries, can also facilitate the efficient evaluation of most join predicates. We propose join algorithms that utilize inverted files and compare them with signature-based methods for several set-comparison predicates.


title: Trident: Distributed Storage, Analysis, and Exploration of Multidimensional Phenomena

abstract: Rising storage and computational capacities have led to the accumulation of voluminous datasets. These datasets contain insights that describe natural phenomena, usage patterns, trends, and other aspects of complex, real-world systems. Statistical and machine learning models are often employed to identify these patterns or attributes of interest. However, a wide array of potentially relevant models and parameterizations exist, and may provide the best performance only after preprocessing steps have been carried out. Our distributed analytics platform, Trident, facilitates the modeling process by providing high-level data exploration functionality as well as guidance for creation of effective models. Trident handles (1) data partitioning and storage, (2) metadata extraction and indexing, and (3) selective retrievals or transformations to prepare and generate training data. In this study, we evaluate Trident in the context of a 1.1 petabyte epidemiology dataset generated by a disease spread simulation; such datasets are often used in planning for national-scale outbreaks in animal populations.


title: Learning to Optimize Via Posterior Sampling

abstract: This paper considers the use of a simple posterior sampling algorithm to balance between exploration and exploitation when learning to optimize actions such as in multi-armed bandit problems. The algorithm, also known as Thompson Sampling, offers significant advantages over the popular upper confidence bound (UCB) approach, and can be applied to problems with finite or infinite action spaces and complicated relationships among action rewards. We make two theoretical contributions. The first establishes a connection between posterior sampling and UCB algorithms. This result lets us convert regret bounds developed for UCB algorithms into Bayesian regret bounds for posterior sampling. Our second theoretical contribution is a Bayesian regret bound for posterior sampling that applies broadly and can be specialized to many model classes. This bound depends on a new notion we refer to as the eluder dimension, which measures the degree of dependence among action rewards. Compared to UCB algorithm Bayesian regret bounds for specific model classes, our general bound matches the best available for linear models and is stronger than the best available for generalized linear models. Further, our analysis provides insight into performance advantages of posterior sampling, which are highlighted through simulation results that demonstrate performance surpassing recently proposed UCB algorithms.


title: A thousand flowers blooming? An examination of community energy in the UK

abstract: Community energy has been proposed as a new policy tool to help achieve the transition to a low-carbon energy system, but the evidence base for this strategy is partial and fragmented. We therefore present new empirical evidence from the first independent UK-wide survey of community energy projects. Our survey investigates the objectives, origins and development of these groups across the UK, their activities and their networking activities as a sector. We also examine the strengths and weaknesses of these groups, along with the opportunities and threats presented by wider socioeconomic and political contexts, in order to improve understanding of the sector's potential and the challenges it faces. We highlight several key issues concerning the further development of the sector. First, this highly diverse sector is not reducible to a single entity; its multiple objectives need joined-up thinking among government departments. Second, its civil society basis is fundamental to its success at engaging local communities, and makes the sector quite distinct from the large energy companies these community groups are aiming to work alongside. There are inherent tensions and vulnerabilities in such a model, and limits to how much these groups can achieve on their own: consistent policy support is essential.


title: An Evaluation of Statistical Approaches to Text Categorization

abstract: This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A controlled study using three classifiers, kNN, LLSF and WORD, was conducted to examine the impact of configuration variations in five versions of Reuters on the observed performance of classifiers. Analysis and empirical evidence suggest that the evaluation results on some versions of Reuters were significantly affected by the inclusion of a large portion of unlabelled documents, mading those results difficult to interpret and leading to considerable confusions in the literature. Using the results evaluated on the other versions of Reuters which exclude the unlabelled documents, the performance of twelve methods are compared directly or indirectly. For indirect compararions, kNN, LLSF and WORD were used as baselines, since they were evaluated on all versions of Reuters that exclude the unlabelled documents. As a global observation, kNN, LLSF and a neural network method had the best performances except for a Naive Bayes approach, the other learning algorithms also performed relatively well.


title: CTSUM: extracting more certain summaries for news articles

abstract: People often read summaries of news articles in order to get reliable information about an event or a topic. However, the information expressed in news articles is not always certain, and some sentences contain uncertain information about the event. Existing summarization systems do not consider whether a sentence in news articles is certain or not. In this paper, we propose a novel system called CTSUM to incorporate the new factor of information certainty into the summarization task. We first analyze the sentences in news articles and automatically predict the certainty levels of sentences by using the support vector regression method with a few useful features. The predicted certainty scores are then incorporated into a summarization system with a graph-based ranking algorithm. Experimental results on a manually labeled dataset verify the effectiveness of the sentence certainty prediction technique, and experimental results on the DUC2007 dataset shows that our new summarization system cannot only produce summaries with better content quality, but also produce summaries with higher certainty.


title: Detection of K-complexes and sleep spindles (DETOKS) using sparse optimization

abstract: Abstract Background This paper addresses the problem of detecting sleep spindles and K-complexes in human sleep EEG. Sleep spindles and K-complexes aid in classifying stage 2 NREM human sleep. New method We propose a non-linear model for the EEG, consisting of a transient, low-frequency, and an oscillatory component. The transient component captures the non-oscillatory transients in the EEG. The oscillatory component admits a sparse time–frequency representation. Using a convex objective function, this paper presents a fast non-linear optimization algorithm to estimate the components in the proposed signal model. The low-frequency and oscillatory components are used to detect K-complexes and sleep spindles respectively. Results and comparison with other methods The performance of the proposed method is evaluated using an online EEG database. The F1 scores for the spindle detection averaged 0.70 ± 0.03 and the F1 scores for the K-complex detection averaged 0.57 ± 0.02. The Matthews Correlation Coefficient and Cohen's Kappa values were in a range similar to the F1 scores for both the sleep spindle and K-complex detection. The F1 scores for the proposed method are higher than existing detection algorithms. Conclusions Comparable run-times and better detection results than traditional detection algorithms suggests that the proposed method is promising for the practical detection of sleep spindles and K-complexes.


title: Anxiety, Stress, and Trauma Symptoms in African Americans: Negative Affectivity Does Not Explain the Relationship between Microaggressions and Psychopathology

abstract: Prior research has demonstrated a clear relationship between experiences of racial microaggressions and various indicators of psychological unwellness. One concern with these findings is that the role of negative affectivity, considered a marker of neuroticism, has not been considered. Negative affectivity has previously been correlated to experiences of racial discrimination and psychological unwellness and has been suggested as a cause of the observed relationship between microaggressions and psychopathology. We examined the relationships between self-reported frequency of experiences of microaggressions and several mental health outcomes (i.e., anxiety [Beck Anxiety Inventory], stress [General Ethnic and Discrimination Scale], and trauma symptoms [Trauma Symptoms of Discrimination Scale]) in 177 African American and European American college students, controlling for negative affectivity (the Positive and Negative Affect Schedule) and gender. Results indicated that African Americans experience more racial discrimination than European Americans. Negative affectivity in African Americans appears to be significantly related to some but not all perceptions of the experience of discrimination. A strong relationship between racial mistreatment and symptoms of psychopathology was evident, even after controlling for negative affectivity. In summary, African Americans experience clinically measurable anxiety, stress, and trauma symptoms as a result of racial mistreatment, which cannot be wholly explained by individual differences in negative affectivity. Future work should examine additional factors in these relationships, and targeted interventions should be developed to help those suffering as a result of racial mistreatment and to reduce microaggressions.


title: Soft Robotic Grippers for Biological Sampling on Deep Reefs

abstract: This article presents the development of an underwater gripper that utilizes soft robotics technology to delicately manipulate and sample fragile species on the deep reef. Existing solutions for deep sea robotic manipulation have historically been driven by the oil industry, resulting in destructive interactions with undersea life. Soft material robotics relies on compliant materials that are inherently impedance matched to natural environments and to soft or fragile organisms. We demonstrate design principles for soft robot end effectors, bench-top characterization of their grasping performance, and conclude by describing in situ testing at mesophotic depths. The result is the first use of soft robotics in the deep sea for the nondestructive sampling of benthic fauna.


title: C-Ku band GaN MMIC T/R frontend module using multilayer ceramics technology

abstract: A C-Ku band GaN monolithic microwave integrated circuit (MMIC) transmitter/receiver (T/R) frontend module with a novel RF interface structure has been successfully developed by using multilayer ceramics technology. This interface improves the insertion loss with wideband characteristics operating up to 40 GHz. The module contains a GaN power amplifier (PA) with output power higher than 10 W over 6–18 GHz and a GaN low-noise amplifier (LNA) with a gain of 15.9 dB over 3.2–20.4 GHz and noise figure (NF) of 2.3–3.7 dB over 4–18 GHz. A fabricated T/R module occupying only 12 × 30 mm2 delivers an output power of 10 W up to the Ku-band. To our knowledge, this is the first demonstration of a C-Ku band T/R frontend module using GaN MMICs with wide bandwidth, 10W output power, and small size operating up to the Ku-band.


Incorporating Soft Variables Into System Dynamics Models : A Suggested Method and Basis for Ongoing Research


title: Repetitive and High Voltage Marx Generator Using Solid-state Devices

abstract: Repetitive high voltage pulsed power system proposed in this study originates from conventional Marx generators. This newly developed Marx modulator employs high voltage (HV) insulated gate bipolar transistors (IGBT) as switches and series- connected diodes as isolated components. Self-supplied IGBT drivers and optic signals are used in the system to avoid insulation problem. Experimental results of 20 stages generating pulses with 60 kV, 20-100 mus and 50~500 Hz are presented to validate the performance of the system in the paper.


title: Digital Time-Optimal Phase Shedding in Multiphase Buck Converters

abstract: This letter proposes a time-optimal digital controller for the phase shedding (PS) in multiphase buck converters. PS is an established technique to improve the efficiency of multiphase converters at light load by changing the active number of phases depending on the load-current level. In order to minimize the output-voltage deviation and the transient time during PS, a minimum time algorithm is investigated. The proposed technique is insensitive to the power stage parameters, as its operation relies only on a feedforward action, depending on the steady-state duty cycle and the number of phases to be turned on or turned off. The proposed approach is validated through experimental tests on a synchronous buck converter.


title: A nonlinear observer approach for concurrent estimation of pose, IMU bias and camera-to-IMU rotation

abstract: This paper concerns the problem of pose estimation for an inertial-visual sensor. It is well known that IMU bias, and calibration errors between camera and IMU frames can impair the achievement of high-quality estimates through the fusion of visual and inertial data. The main contribution of this work is the design of new observers to estimate pose, IMU bias and camera-to-IMU rotation. The observers design relies on an extension of the so-called passive complementary filter on SO(3). Stability of the observers is established using Lyapunov functions under adequate observability conditions. Experimental results are presented to assess this approach.


title: A multilayer substrate integrated 3 dB power divider with embedded thick film resistor

abstract: A 3 dB power divider/combiner in substrate integrated waveguide (SIW) technology is presented. The divider consists of an E-plane SIW bifurcation with an embedded thick film resistor. The transition divides a full-height SIW into two SIWs of half the height. The resistor provides isolation between these two. The divider is fabricated in a multilayer process using high frequency substrates. For the resistor carbon paste is printed on the middle layer of the stack-up. Simulation and measurement results are presented. The measured divider exhibits an isolation of better than 22 dB within a bandwidth of more than 3GHz at 20 GHz.


title: Caesar: a content router for high speed forwarding

abstract: Today, high-end routers forward hundreds of millions of packets per second by means of longest prefix match on forwarding tables with less than a million IP prefixes. Information-Centric Networking, a novel form of networking where content is requested by its name, poses a new challenge in the design of high-end routers: process at least the same amount of packets, assuming a forwarding table that contains hundreds of millions of content prefixes. In this work we design and preliminarily evaluate Caesar, the first content router that supports name-based forwarding at high speed. Caesar efficiently uses available processing and memory units in a high-end router to support forwarding tables containing a billion content prefixes with unlimited characters.


title: 3D reconstruction of neurons in electron microscopy images

abstract: With the prevalence of brain-related diseases like Alzheimer in an increasing ageing population, Connectomics, the study of connections between neurons of the human brain, has emerged as a novel and challenging research topic. Accurate and fully automatic algorithms are needed to deal with the increasing amount of data from the brain images. This paper presents an automatic 3D neuron reconstruction technique where neurons within each slice image are first segmented and then linked across multiple slices within the publicly available Electron Microscopy dataset (SNEMI3D). First, random Forest classifier is adapted on top of superpixels for the neuron segmentation within each slice image. The maximum overlap between two consecutive images is then calculated for neuron linking, where the adjacency matrix of two different labeling of the segments is used to distinguish neuron merging and splitting. Experiments over the SNEMI3D dataset show that the proposed technique is efficient and accurate.


title: Aggresomes formed by alpha-synuclein and synphilin-1 are cytoprotective.

abstract: Lewy bodies (LBs), which are the hallmark pathologic features of Parkinson's disease and of dementia with LBs, have several morphologic and molecular similarities to aggresomes. Whether such cytoplasmic inclusions contribute to neuronal death or protect cells from the toxic effects of misfolded proteins remains controversial. In this report, the role of aggresomes in cell viability was addressed in the context of over-expressing alpha-synuclein and its interacting partner synphilin-1 using engineered 293T cells. Inhibition of proteasome activity elicited the formation of juxtanuclear aggregates with characteristics of aggresomes including immunoreactivity for vimentin, gamma-tubulin, ubiquitin, proteasome subunit, and hsp70. As expected from the properties of aggresomes, the microtubule disrupting agents, vinblastin and nocodazole, markedly prevented the formation of these inclusions. Similar to LBs, the phosphorylated form of alpha-synuclein co-localized in these synphilin-1-containing aggresomes. Although the caspase inhibitor z-VAD-fmk significantly reduced the number of apoptotic cells, it had no impact on the percentage of aggresome-positive cells. Finally, quantitative analysis revealed aggresomes in 60% of nonapoptotic cells but only in 10% of apoptotic cells. Additionally, alpha-synuclein-induced apoptosis was not coupled with increased prevalence of aggresome-bearing cells. Taken together, these observations indicate a disconnection between aggresome formation and apoptosis, and support a protective role for these inclusions from the toxicity associated with the combined over-expression of alpha-synuclein and synphilin-1.


title: Permanent-Magnet Machines for Distributed Power Generation: A Review

abstract: A review of permanent-magnet machines for distributed power generation is presented. Radial-flux, linear and axial-flux machine configurations are discussed with reference to generation that utilize energy resources such as wind, wave energy, and natural gas. Isolated operation and grid-connected operation are considered. Some of the authors' research work on a surface-inset permanent-magnet synchronous generator (PMSG) for autonomous power system application is also included.


title: Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach

abstract: Most Machine Learning (ML) researchers focus on automatic Machine Learning (aML) where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from the availability of “big data”. However, sometimes, for example in health informatics, we are confronted not a small number of data sets or rare events, and with complex problems where aML-approaches fail or deliver unsatisfactory results. Here, interactive Machine Learning (iML) may be of help and the “human-in-the-loop” approach may be beneficial in solving computationally hard problems, where human expertise can help to reduce an exponential search space through heuristics.


Sogou Neural Machine Translation Systems for WMT17


title: Flat-Panel See-Through Three-Dimensional Display Based on Integral Imaging Using a Transparent Polarized Point Light Source Array

abstract: This study proposes a technique to implement see-through three-dimensional (3-D) display based on point light source integral imaging. This display consists of an edge-lit light guide plate, a polarizer array, and a transparent liquid crystal display (LCD) panel without rear polarizer. The combination of the light guide plate and the polarizer array act as a transparent polarized point light source array (PLSA), and reconstructs the 3-D image with the elemental image array loaded on the LCD panel. The background rays passing through the gaps in the polarized PLSA carry the object information and implement see-through property. The experimental system is constructed based on 1-D integral imaging to verify the proposed method. The see-through and superposition capabilities of the proposed method are demonstrated.


title: Permanent magnet generator design and control for large wind turbines

abstract: Direct drive permanent magnet generators(PMGs) are increasingly capturing the global wind market in large onshore and offshore applications. The aim of this paper is to provide a quick overview of permanent magnet generator design and related control issues for large wind turbines. Generator systems commonly used in wind turbines, the permanent magnet generator types, and control methods are reviewed in the paper. The current commercial PMG wind turbine on market is surveyed. The design of a 5 MW axial flux permanent magnet (AFPM) generator for large wind turbines is discussed and presented in detail.


title: Real-time correlative scan matching

abstract: Scan matching, the problem of registering two laser scans in order to determine the relative positions from which the scans were obtained, is one of the most heavily relied-upon tools for mobile robots. Current algorithms, in a trade-off for computational performance, employ heuristics in order to quickly compute an answer. Of course, these heuristics are imperfect: existing methods can produce poor results, particularly when the prior is weak. The computational power available to modern robots warrants a re-examination of these quality vs. complexity trade-offs. In this paper, we advocate a probabilistically-motivated scan-matching algorithm that produces higher quality and more robust results at the cost of additional computation time. We describe several novel implementations of this approach that achieve real-time performance on modern hardware, including a multi-resolution approach for conventional CPUs, and a parallel approach for graphics processing units (GPUs). We also provide an empirical evaluation of our methods and several contemporary methods, illustrating the benefits of our approach. The robustness of the methods make them especially useful for global loop-closing.


title: Immersion, Presence, and Performance in Virtual Environments: An Experiment with Tri-Dimensional Chess

abstract: This paper describes an experiment to assess the influence of immersion on performance in immersive virtual environments. The task involved Tri-Dimensional Chess, and required subjects to reproduce on a real chess board the state of board learned from a sequence of moves witnessed in a virtual environment. Twenty four subjects were allocated to a factorial design consisting of two levels of immersion (exocentric screen based, and egocentric HMD based), and two kinds of environment (plain and realistic). The results suggest that egocentric subjects performed better than exocentric, and those in the more realistic environment performed better than those in the less realistic environment. Previous knowledge of chess, and amount of virtual practice were also significant, and may be considered as control variables to equalise these factors amongst the subjects. Other things being equal, males remembered the moves better than females, although female performance improved with higher spatial ability test score. The paper also attempts to clarify the relationship between immersion, presence and performance, and locates the experiment within such a theoretical framework.


The use of Fuzzy Logic for Artificial Intelligence in Games


title: Semi-supervised nonlinear dimensionality reduction

abstract: The problem of nonlinear dimensionality reduction is considered. We focus on problems where prior information is available, namely, semi-supervised dimensionality reduction. It is shown that basic nonlinear dimensionality reduction algorithms, such as Locally Linear Embedding (LLE), Isometric feature mapping (ISOMAP), and Local Tangent Space Alignment (LTSA), can be modified by taking into account prior information on exact mapping of certain data points. The sensitivity analysis of our algorithms shows that prior information will improve stability of the solution. We also give some insight on what kind of prior information best improves the solution. We demonstrate the usefulness of our algorithm by synthetic and real life examples.


title: Flow statistics based load balancing in OpenFlow

abstract: Traditional network devices are not very efficient in handling large amount of web related traffic. All these devices contain tightly coupled control plane plus data plane. Software Defined Networking (SDN) is an emerging architecture based on principle of decoupling of these planes. The control plane which is called the brain of network devices is also known as OpenFlow or SDN Controller. By creating SDN applications such as IDS, IPS, load balancer, firewall & router on top of control plane, a simple data plane can be converted into robust network device. We have created a load balancer SDN application which converts the data plane into a powerful load balancer. Load balancers are the network devices which distribute the load among different servers based on particular strategy. The strategy could be random, round robin, weighted round robin. Each strategy has got its own pro & cons. In this paper, we have implemented flow statistics based strategy for our load balancer that runs on top of POX controller. It is written in Python Language. For testing our application, we used a real lab setup up of four computer systems. We compared this application with already available round robin based SDN load balancer application.


title: Searching for the structure of coping: A review and critique of category systems for classifying ways of coping.

abstract: From analyzing 100 assessments of coping, the authors critiqued strategies and identified best practices for constructing category systems. From current systems, a list of 400 ways of coping was compiled. For constructing lower order categories, the authors concluded that confirmatory factor analysis should replace the 2 most common strategies (exploratory factor analysis and rational sorting). For higher order categories, they recommend that the 3 most common distinctions (problem- vs. emotion-focused, approach vs. avoidance, and cognitive vs. behavioral) no longer be used. Instead, the authors recommend hierarchical systems of action types (e.g., proximity seeking, accommodation). From analysis of 6 such systems, 13 potential core families of coping were identified. Future steps involve deciding how to organize these families, using their functional homogeneity and distinctiveness, and especially their links to adaptive processes.


title: Advances in dental veneers: materials, applications, and techniques

abstract: Laminate veneers are a conservative treatment of unaesthetic anterior teeth. The continued development of dental ceramics offers clinicians many options for creating highly aesthetic and functional porcelain veneers. This evolution of materials, ceramics, and adhesive systems permits improvement of the aesthetic of the smile and the self-esteem of the patient. Clinicians should understand the latest ceramic materials in order to be able to recommend them and their applications and techniques, and to ensure the success of the clinical case. The current literature was reviewed to search for the most important parameters determining the long-term success, correct application, and clinical limitations of porcelain veneers.


title: Effects of Strength Training on Running Economy in Highly Trained Runners: A Systematic Review With Meta-Analysis of Controlled Trials.

abstract: Balsalobre-Fernández, C, Santos-Concejero, J, and Grivas, GV. Effects of strength training on running economy in highly trained runners: a systematic review with meta-analysis of controlled trials. J Strength Cond Res 30(8): 2361-2368, 2016-The purpose of this study was to perform a systematic review and meta-analysis of controlled trials to determine the effect of strength training programs on the running economy (RE) of high-level middle- and long-distance runners. Four electronic databases were searched in September 2015 (PubMed, SPORTDiscus, MEDLINE, and CINAHL) for original research articles. After analyzing 699 resultant original articles, studies were included if the following criteria were met: (a) participants were competitive middle- or long-distance runners; (b) participants had a V[Combining Dot Above]O2max >60 ml·kg·min; (c) studies were controlled trials published in peer-reviewed journals; (d) studies analyzed the effects of strength training programs with a duration greater than 4 weeks; and (e) RE was measured before and after the strength training intervention. Five studies met the inclusion criteria, resulting in a total sample size of 93 competitive, high-level middle- and long-distance runners. Four of the 5 included studies used low to moderate training intensities (40-70% one repetition maximum), and all of them used low to moderate training volume (2-4 resistance lower-body exercises plus up to 200 jumps and 5-10 short sprints) 2-3 times per week for 8-12 weeks. The meta-analyzed effect of strength training programs on RE in high-level middle- and long-distance runners showed a large, beneficial effect (standardized mean difference [95% confidence interval] = -1.42 [-2.23 to -0.60]). In conclusion, a strength training program including low to high intensity resistance exercises and plyometric exercises performed 2-3 times per week for 8-12 weeks is an appropriate strategy to improve RE in highly trained middle- and long-distance runners.


title: Soft Robot Arm Inspired by the Octopus

abstract: The octopus is a marine animal whose body has no rigid structures. It has eight arms composed of a peculiar muscular structure, named a muscular hydrostat. The octopus arms provide it with both locomotion and grasping capabilities, thanks to the fact that their stiffness can change over a wide range and can be controlled through combined contractions of the muscles. The muscular hydrostat can better be seen as a modifiable skeleton. Furthermore, the morphology the arms and the mechanical characteristics of their tissues are such that the interaction with the environment (i.e., water) is exploited to simplify control. Thanks to this effective mechanism of embodied intelligence, the octopus can control a very high number of degrees of freedom, with relatively limited computing resources. From these considerations, the octopus emerges as a good model for embodied intelligence and for soft robotics. The prototype of a robot arm has been built based on an artificial muscular hydrostat inspired to the muscular ...


title: On Space-Time Filtering Framework for Matching Human Actions Across Different Viewpoints

abstract: Space-time template matching is considered as a promising approach for human action recognition. However, a major drawback of template-based methods is computational overhead due to matching in spatial domain. Recently, space-time correlation-based action filters have been proposed for recognizing human actions in frequency domain. These action filters present reduction in time complexity as Fourier transform-based matching is faster than spatial template matching. However, the utility of such action filters is challenged due to a number of factors: 1) inability to deal with view variations due to implicit lack of support for view-invariance; 2) these filters can be trained only for one action class at a time, and separate filters are required for each action class with increased computational overhead; 3) these filters simply take average of similar action instances and behave no better than average filters; and 4) slightly misaligned action data sets create problems as these filters are not shift-invariant. In this paper, we try to address these shortcomings by proposing an advanced space-time filtering framework for recognizing human actions despite large viewpoint variations. Rather than using crude intensity values, we use 3D tensor structure at each pixel, which characterizes the most common local motion in action sequences. Discrete tensor Fourier transform is then applied to achieve frequency domain representations. Then, we form view clusters from multiple view action data and use space-time correlation filtering to achieve discriminative view representations. These representations are used in an innovative way to achieve action recognition despite viewpoint variations. Extensive experimentation is performed on well-known multiple view action data sets, including IXMAS, WVU, and N-UCLA action data set. A detailed performance comparison with the existing view-invariant action recognition techniques indicates that our approach works equally well for RGB and RGB-D video data with increased accuracy and efficiency.


title: A system for automatic text detection in video

abstract: Video indexing is an important problem that has occupied recent research efforts. The text appearing in video can provide semantic information about the scene content. Detecting and recognizing text events can provide indices into the video for content based querying. We describe a system for detecting, tracking, and extracting artificial and scene text in MPEG-1 video. Preliminary results are presented.


Mining asynchronous periodic patterns in time series data


title: WAVELET TRANSFORM ANALYSIS OF OPEN CHANNEL WAKE FLOWS

abstract: Wavelet transform analysis offers a new approach to signal processing through its ability to decompose signals in both time and frequency. As such, it is more suited to nonstationary and intermittent signals than traditional Fourier analysis. The first part of this paper provides an introduction to the theory and signal processing properties of both continuous and discrete wavelet transform analysis. An account is then given of the application of wavelet transform analysis to a variety of experimental open channel wake flows. Feature location is undertaken using a continuous wavelet transform, and both turbulent statistical analysis and thresholding of the turbulent signal components are undertaken using a discrete wavelet transform.


Applying probabilistic term weighting to OCR text in the case of a large alphabetic library catalogue


title: A Comparative Study of Pub / Sub Methods in Structured P 2 P Networks

abstract: Methods for publish/subscribe applications over P2P networks have been a research issue for a long time. Many approaches have been developed and evaluated, but typically each based on different assumptions, which makes their mutual comparison very difficult if not impossible. We identify two design patterns that can be used to implement publish/subscribe applications over structured P2P networks and provide an analytical analysis of their complexity. Based on a characterization of different real-world usage scenarios we present evidence as to which approach is preferable for certain application classes. Finally, we present simulation results that support our analysis.


title: Probabilistic memory-based collaborative filtering

abstract: Memory-based collaborative filtering (CF) has been studied extensively in the literature and has proven to be successful in various types of personalized recommender systems. In this paper, we develop a probabilistic framework for memory-based CF (PMCF). While this framework has clear links with classical memory-based CF, it allows us to find principled solutions to known problems of CF-based recommender systems. In particular, we show that a probabilistic active learning method can be used to actively query the user, thereby solving the "new user problem." Furthermore, the probabilistic framework allows us to reduce the computational cost of memory-based CF by working on a carefully selected subset of user profiles, while retaining high accuracy. We report experimental results based on two real-world data sets, which demonstrate that our proposed PMCF framework allows an accurate and efficient prediction of user preferences.


title: Improving Question Answering with External Knowledge

abstract: We focus on multiple-choice question answering (QA) tasks in subject areas such as science, where we require both broad background knowledge and the facts from the given subject-area reference corpus. In this work, we explore simple yet effective methods for exploiting two sources of external knowledge for subject-area QA. The first enriches the original subject-area reference corpus with relevant text snippets extracted from an open-domain resource (i.e., Wikipedia) that cover potentially ambiguous concepts in the question and answer options. As in other QA research, the second method simply increases the amount of training data by appending additional in-domain subject-area instances. ::: Experiments on three challenging multiple-choice science QA tasks (i.e., ARC-Easy, ARC-Challenge, and OpenBookQA) demonstrate the effectiveness of our methods: in comparison to the previous state-of-the-art, we obtain absolute gains in accuracy of up to 8.1%, 13.0%, and 12.8%, respectively. While we observe consistent gains when we introduce knowledge from Wikipedia, we find that employing additional QA training instances is not uniformly helpful: performance degrades when the added instances exhibit a higher level of difficulty than the original training data. As one of the first studies on exploiting unstructured external knowledge for subject-area QA, we hope our methods, observations, and discussion of the exposed limitations may shed light on further developments in the area.


title: Polyglot Semantic Role Labeling

abstract: Previous approaches to multilingual semantic dependency parsing treat languages independently, without exploiting the similarities between semantic structures across languages. We experiment with a new approach where we combine resources from a pair of languages in the CoNLL 2009 shared task to build a polyglot semantic role labeler. Notwithstanding the absence of parallel data, and the dissimilarity in annotations between languages, our approach results in an improvement in SRL performance on multiple languages over a monolingual baseline. Analysis of the polyglot model shows it to be advantageous in lower-resource settings.


title: Fairness GAN

abstract: In this paper, we introduce the Fairness GAN, an approach for generating a dataset that is plausibly similar to a given multimedia dataset, but is more fair with respect to protected attributes in allocative decision making. We propose a novel auxiliary classifier GAN that strives for demographic parity or equality of opportunity and show empirical results on several datasets, including the CelebFaces Attributes (CelebA) dataset, the Quick, Draw!\ dataset, and a dataset of soccer player images and the offenses they were called for. The proposed formulation is well-suited to absorbing unlabeled data; we leverage this to augment the soccer dataset with the much larger CelebA dataset. The methodology tends to improve demographic parity and equality of opportunity while generating plausible images.


title: The potential distribution measurement on the stress grading system of high-voltage rotating machines by using pockels effect

abstract: A surface potential measuring system was developed by utilizing Pockels sensor for measuring the potential distribution on the stress grading (SG) system of form wound motor coils. A Bi 4 Ge 3 O 12 (BGO) Pockels crystal is put close to a measuring object and the capacitively-induced voltage on the end surface of the BGO crystal is measured by using Pockels effect of the crystal itself. The spatial resolution of the measuring system is 5 mm, and the frequency response reaches up to GHz-range. With this system, the surface potential distributions on a model bar coil were measured. According to the measuring results, it is shown that the newly developed potential measuring system here is very useful for evaluating the reliability of the SG system of motors.


title: Cut, Glue, & Cut: A Fast, Approximate Solver for Multicut Partitioning

abstract: Recently, unsupervised image segmentation has become increasingly popular. Starting from a superpixel segmentation, an edge-weighted region adjacency graph is constructed. Amongst all segmentations of the graph, the one which best conforms to the given image evidence, as measured by the sum of cut edge weights, is chosen. Since this problem is NP-hard, we propose a new approximate solver based on the move-making paradigm: first, the graph is recursively partitioned into small regions (cut phase). Then, for any two adjacent regions, we consider alternative cuts of these two regions defining possible moves (glue & cut phase). For planar problems, the optimal move can be found, whereas for non-planar problems, efficient approximations exist. We evaluate our algorithm on published and new benchmark datasets, which we make available here. The proposed algorithm finds segmentations that, as measured by a loss function, are as close to the ground-truth as the global optimum found by exact solvers. It does so significantly faster then existing approximate methods, which is important for large-scale problems.


title: Nursing professional attire: probing patient preferences to inform implementation.

abstract: OBJECTIVE ::: The aim of this study was to increase understanding of patient perceptions of nursing professional image, appearance, and identification to inform implementation of professional clinical attire. ::: ::: ::: BACKGROUND ::: There is growing evidence of patient preference for and organizational implementation of professional clinical attire. ::: ::: ::: METHODS ::: A total of 350 randomly selected inpatients were surveyed using the professional image and patient preferences survey prior to a revision of the dress code for nursing. ::: ::: ::: RESULTS ::: Patients gave high scores for nursing image, appearance, and identification, with no support for color-coded uniforms. ::: ::: ::: CONCLUSIONS ::: Organizations should assess patient perceptions before implementation of a dress code for nursing.


title: A 3D steady-state model of a tendon-driven continuum soft manipulator inspired by the octopus arm.

abstract: Control and modelling of continuum robots are challenging tasks for robotic researchers. Most works on modelling are limited to piecewise constant curvature. In many cases they neglect to model the actuators or avoid a continuum approach. In particular, in the latter case this leads to a complex model hardly implemented. In this work, a geometrically exact steady-state model of a tendon-driven manipulator inspired by the octopus arm is presented. It takes a continuum approach, fast enough to be implemented in the control law, and includes a model of the actuation system. The model was experimentally validated and the results are reported. In conclusion, the model presented can be used as a tool for mechanical design of continuum tendon-driven manipulators, for planning control strategies or as internal model in an embedded system.


title: Neural Syntax: Cell Assemblies, Synapsembles, and Readers

abstract: A widely discussed hypothesis in neuroscience is that transiently active ensembles of neurons, known as "cell assemblies," underlie numerous operations of the brain, from encoding memories to reasoning. However, the mechanisms responsible for the formation and disbanding of cell assemblies and temporal evolution of cell assembly sequences are not well understood. I introduce and review three interconnected topics, which could facilitate progress in defining cell assemblies, identifying their neuronal organization, and revealing causal relationships between assembly organization and behavior. First, I hypothesize that cell assemblies are best understood in light of their output product, as detected by "reader-actuator" mechanisms. Second, I suggest that the hierarchical organization of cell assemblies may be regarded as a neural syntax. Third, constituents of the neural syntax are linked together by dynamically changing constellations of synaptic weights ("synapsembles"). The existing support for this tripartite framework is reviewed and strategies for experimental testing of its predictions are discussed.


title: Natural language arguments: a combined approach

abstract: With the growing use of the Social Web, an increasing number of applications for exchanging opinions with other people are becoming available online. These applications are widely adopted with the consequence that the number of opinions about the debated issues increases. In order to cut in on a debate, the participants need first to evaluate the opinions in favour or against the debated issue. Argumentation theory proposes algorithms and semantics to evaluate the set of accepted arguments, given the conflicts among them. The main problem is how to automatically generate the arguments from the natural language formulation of the opinions used in these applications. Our paper addresses this problem by proposing and evaluating the use of natural language techniques to generate the arguments. In particular, we adopt the textual entailment approach, a generic framework for applied semantics, where linguistic objects are mapped by means of semantic inferences at a textual level. We couple textual entailment together with a Dung-like argumentation system which allows us to identify the arguments that are accepted in the considered online debate. The originality of the proposed framework lies in the following point: natural language debates are analyzed and the arguments are automatically extracted.


title: Model based RGBD SLAM

abstract: In this paper we propose to improve the localization and the 3D mapping provided by an RGBD SLAM algorithm, using a prior knowledge of the 3D model of the environment. The proposed solution relies on an feature-based RGBD SLAM algorithm to localize the camera and update the 3D map of the scene. To improve the accuracy and the robustness of the localization, we propose to combine in a local bundle adjustment process, geometric information provided by a prior coarse 3D model of the scene (e.g. generated from the 2D plan of the building) with RGBD data. The proposed approach is evaluated on a public benchmark dataset as well as on a real scene acquired by a Kinect sensor.


title: Why Do Social Media Users Share Misinformation?

abstract: Widespread misinformation on social media is a cause of concern. Currently, it is unclear what factors prompt regular social media users with no malicious intent to forward misinformation to their online networks. Using a questionnaire informed by the Uses and Gratifications theory and the literature on rumor research, this study asked university students in Singapore why they shared misinformation on social media. Gender differences were also tested. The study found that perceived information characteristics such as its ability to spark conversations and its catchiness were top factors. Self-expression and socializing motivations were also among the top reasons. Women reported a higher prevalence of misinformation sharing. The implications for the design of social media applications and information literacy training were discussed.


title: Deep Reinforcement Learning with a Natural Language Action Space

abstract: This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games. Termed a deep reinforcement relevance network (DRRN), the architecture represents action and state spaces with separate embedding vectors, which are combined with an interaction function to approximate the Q-function in reinforcement learning. We evaluate the DRRN on two popular text games, showing superior performance over other deep Q-learning architectures. Experiments with paraphrased action descriptions show that the model is extracting meaning rather than simply memorizing strings of text.


title: Semantic exploitation of implicit patent information

abstract: In recent years patents have become increasingly important for businesses to protect their intellectual capital and as a valuable source of information. Patent information is, however, not employed to its full potential and the interpretation of structured and unstructured patent information in large volumes remains a challenge. We address this by proposing an integrated interdisciplinary approach that uses natural language processing and machine learning techniques to formalize multilingual patent information in an ontology. The ontology further contains patent and domain specific knowledge, which allows for aligning patents with technological fields of interest and other business-related artifacts. Our empirical evaluation shows that for categorizing patents according to well-known technological fields of interest, the approach achieves high accuracy with selected feature sets compared to related work focussing on monolingual patents. We further show that combining OWL RL reasoning with SPARQL querying over the patent knowledge base allows for answering complex business queries and illustrate this with real-world use cases from the automotive domain.


title: Software stacks for mixed-critical applications: Consolidating IEEE 802.1 AVB and time-triggered ethernet in next-generation automotive electronics

abstract: Real-time Ethernet variants are expected to build the future communication infrastructure in cars. First camera based driver assistance functions will communicate using IEEE 802.1 AVBs credit-based shaping. But for the strict timing requirements of automotive control-traffic, AVBs current timing guarantees are insufficient. The upcoming IEEE 802.1Qbv standard proposes synchronous time-triggered traffic to overcome these limitations. This paper presents a low footprint microcontroller based communication architecture, that supports both traffic classes in parallel while using standard hardware components. It allows first realistic performance analyses of coexistent traffic shaping strategies in a software based implementation.


title: Model predictive attitude control of an unmanned Tilt-Rotor aircraft

abstract: The design of a Model Predictive Controller (MPC) for attitude maneuvers of an Unmanned Vertical Take Off and Landing (VTOL) Tilt-Rotor vehicle, flying in its helicopter mode is the subject of this article. Tilt-Rotor air vehicles combine the flight envelopes of: a)fixed wing aircrafts, and b) rotorcrafts, leading to systems with advanced flight characteristics. However, the highly nonlinear and underactuated dynamics of these systems, necessitate the design of advanced control schemes to achieve a satisfactory control performance. In the proposed control design approach, the Tilt-Rotor's nonlinear dynamics are linearized in various operating points, resulting in a set of multiple piecewise affine models that describe its helicopter's flying mode. The corresponding novel MP-controller is designed for set-point maneuvers taking into account the arbitrary switchings between the linear models as well as the constraints on the actuators, while extended simulation studies indicate the high overall efficiency of the proposed MPC scheme.


title: Binarized-BLSTM-RNN based Human Activity Recognition

abstract: High computational complexity hinders the widespread usage of neural networks, especially in mobile devices, which are often the basis of fine-grained localization technology for ubiquitous health monitoring, context awareness, and indoor location tracking. In this paper, we present a binarized recurrent neural network whose weight parameters, input, and intermediate hidden layer output signals, are all binary-valued, and require only basic bit logic for the evaluation and training process. The proposed Binarized Long Short-Term Memory Network (B-BLSTM-RNN) is especially suitable for resource-constrained environments since it replaces either floating or fixed-point arithmetic with significantly more efficient bitwise operations. The model is based on a bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN). Designed to take contextual information into account, the network can process data gathered from different positions, resulting in a system, that's invariant to transformations and distortions of the input patterns. During the forward pass, the B-BLSTM drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which is expected to substantially improve power-efficiency. The binarized network is simple, accurate, efficient, and works on challenging gesture recognition tasks using raw MEM data. To validate the effectiveness of the network we conduct three sets of experiments. We achieved a classification accuracy with a the proposed network of about 90% which only 2% less than the full-precision network. We also compare our method with recent methods and outperform these methods by large margins on the conducted datasets.


title: Evolving Robot Morphology Facilitates the Evolution of Neural Modularity and Evolvability

abstract: Although recent work has demonstrated that modularity can increase evolvability in non-embodied systems, it remains to be seen how the morphologies of embodied agents influences the ability of an evolutionary algorithm to find useful and modular controllers for them. We hypothesize that a modular control system may enable different parts of a robot's body to sense and react to stimuli independently, enabling it to correctly recognize a seemingly novel environment as, in fact, a composition of familiar percepts and thus respond appropriately without need of further evolution. Here we provide evidence that supports this hypothesis: We found that such robots can indeed be evolved if (1) the robot's morphology is evolved along with its controller, (2) the fitness function selects for the desired behavior and (3) also selects for conservative and robust behavior. In addition, we show that if constraints (1) and (3) are relaxed, or structural modularity is selected for directly, the robots have too little or too much modularity and lower evolvability. Thus, we demonstrate a previously unknown relationship between modularity and embodied cognition: evolving morphology and control such that robots exhibit conservative behavior indirectly selects for appropriate modularity and, thus, increased evolvability.


title: A Comprehensive Survey of Trends in Oracles for Software Testing

abstract: Testing involves examining the behaviour of a system in order to discover potential faults. Determining the desired correct behaviour for a given input is called the “oracle problem”. Oracle automation is important to remove a current bottleneck which inhibits greater overall test automation; without oracle automation, the human has to determine whether observed behaviour is correct. The literature on oracles has introduced techniques for oracle automation, including modelling, specifications, contract-driven development and metamorphic testing. When none of these is completely adequate, the final source of oracle information remains the human, who may be aware of informal specifications, expectations, norms and domain specific information that provide informal oracle guidance. All forms of oracle, even the humble human, involve challenges of reducing cost and increasing benefit. This paper provides a comprehensive survey of current approaches to the oracle problem and an analysis of trends in this important area of software testing research and practice.


title: Monolithic implementation of phase shifted switched capacitor step-down DC-DC converter for portable power applications

abstract: This paper presents analysis and design of a reconfigurable switched capacitor (SC) step-down DC-DC converter architecture suitable for on-chip integrated portable power supplies. Comprising basic SC cells, the proposed DC-DC converter is modular in architecture, and can be viewed as a dual of the multiphase buck converter architecture. The SC converter with integrated capacitors is designed in a standard 180 nm CMOS process to operate in 1/3 and 1/2 conversion ratio modes. The converter is designed to operate with input voltage varying from 2.7V to 5.5V, and switching frequency varying from 10 MHz to 70 MHz, while delivering up to 250mA load current at 1.3V output voltage. SPICE simulation results based on detailed process models are presented. It is shown that efficiency is significantly higher than in a linear regulator operating over the complete specified input voltage range, and also reduces the off-chip input and output filter capacitor requirement.


title: Impact of a psycho-oncological rehabilitation intervention on psychological distress: the experience of CeRiOn (Oncological Rehabilitation Centre) Florence 2007–2010

abstract: BACKGROUND ::: Combining medical and psychological knowledge and accompanying patients in an individualised path, the Oncological Rehabilitation Centre of Florence (CeRiOn) aims at offering effective integrated rehabilitation interventions and at reducing psychological distress in cancer patients. In the present observational study, we measured the psychological distress at baseline and at the end of the completed rehabilitation processes. ::: ::: ::: METHODS ::: A total number of 627 cancer patients were treated by the psycho-oncological service at CeRiOn in 2007-2010. Among them, 99 (all women) participated in more than or equal to three sessions, were followed up for clinical and life status for at least 1 year and had a formal conclusion of their psychological rehabilitation process. For 98 cases, both a baseline and a follow-up measure of distress, by the Psychological Distress Inventory (PDI) and the Distress Thermometer (DT), were available. ::: ::: ::: RESULTS ::: Relevant before-after amelioration in this selected group of psychologically, highly suffering cancer patients has been shown. Almost all the patients (except 20%) received both group and individual psychological support. The average rehabilitation process lasted 1.7 years. Average distress evaluation decreased from a baseline of 34.7 to 26.4 (P < 0.001) according to PDI and from 5.9 to 2.2 according to DT (P < 0.001). ::: ::: ::: CONCLUSIONS ::: On average, quite a long psychological support time was necessary to complete the rehabilitation process. During this period, patients who had a formal conclusion of the psychological support received at CeRiOn showed a significant reduction of their distress.


Like Elephants Do : Sensing Bystanders During HMD Usage


title: Smartphones usage in the classrooms: Learning aid or interference?

abstract: Many educational institutions, especially higher education institutions, are considering to embrace smartphones as part of learning aids in classes as most students (in many cases all students) not only own them but also are also attached to them. The main question is whether embracing smartphones in classroom teaching enhances the learning or perhaps an interference. This paper presents the finding of our study on embracing smartphone in classroom teaching. The study was carried out through a survey and interview/discussion with a focus group of students. We found that they use their smartphones to access teaching materials or supporting information, which are normally accessible through the Internet. Students use smartphones as learning aids due many reasons such as they provide convenience, portability, comprehensive learning experiences, multi sources and multitasks, and environmentally friendly. They also use smartphones to interact with teachers outside classes and using smartphones to manage their group assignments. However, integrating smartphones in a classroom-teaching environment is a challenging task. Lecturers may need to incorporate smartphones in teaching and learning to create attractive teaching and optimum interaction with students in classes while mitigating or at least minimising distractions that can be created. Some of the challenges are distraction, dependency, lacking hands on skills, and the reduce quality of face-to-face interaction. To avoid any disturbances in using smartphones within a classroom environment, proper rules of using smartphones in class should be established before teaching, and students need to abide to these rules.


title: Are attractive men's faces masculine or feminine? The importance of type of facial stimuli.

abstract: The authors investigated whether differences in facial stimuli could explain the inconsistencies in the facial attractiveness literature regarding whether adults prefer more masculine- or more feminine-looking male faces. Their results demonstrated that use of a female average to dimorphically transform a male facial average produced stimuli that did not accurately reflect the relationship between masculinity and attractiveness. In contrast, use of averages of masculine males and averages of feminine males produced stimuli that did accurately reflect the relationship between masculinity and attractiveness. Their findings suggest that masculinity contributes more to male facial attractiveness than does femininity, but future research should investigate how various combinations of facial cues contribute to male facial attractiveness.


title: Social Signal Processing: Survey of an Emerging Domain

abstract: The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence - the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement - in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for social signal processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially aware computing.


title: A Mixture IRT Analysis of Risky Youth Behavior

abstract: The study reported in this manuscript used a mixture item response model with data from the Youth Risk Behavior Survey 2009 (N = 16,410) to identify subtypes of adolescents at-risk for engaging in unhealthy behaviors, and to find individual survey items that were most effective at identifying such students within each subtype. The goal of the manuscript is twofold: 1) To demonstrate the utility of the mixture item response theory model for identifying subgroups in the population and for highlighting the use of group specific item response parameters and 2) To identify typologies of adolescents based on their propensity for engaging in sexually and substance use risky behaviors. Results indicate that 4 classes of youth exist in the population, with differences in risky sexual behaviors and substance use. The first group had a greater propensity to engage in risky sexual behavior, while group 2 was more likely to smoke tobacco and drink alcohol. Group 3 was the most likely to use other substances, such as marijuana, methamphetamine, and other mind altering drugs, and group 4 had the lowest propensity for engaging in any of the sexual or substance use behaviors included in the survey. Finally, individual items were identified for each group that can be most effective at identifying individuals at greatest risk. Further proposed directions of research and the contribution of this analysis to the existing literature are discussed.


title: Efficient Public-Key Cryptography in the Presence of Key Leakage. Cryptology ePrint Archive, Report 2010/154

abstract: We study the design of cryptographic primitives resistant to a large class of side-channel attacks, called “memory attacks”, where an attacker can repeatedly and adaptively learn information about the secret key, subject only to the constraint that the overall amount of such information is bounded by some parameter l. Although the study of such primitives was initiated only recently by Akavia et al. [2], subsequent work already produced many such “leakage-resilient” primitives [48,4,42], including signature, encryption, identification (ID) and authenticated key agreement (AKA) schemes. Unfortunately, every existing scheme, — for any of the four fundamental primitives above, — fails to satisfy at least one of the following desirable properties:


title: Led-based optical cochlear implant on highly flexible triple layer polyimide substrates

abstract: This paper reports on the design, fabrication, assembly, as well as the optical, mechanical and thermal characterization of a novel MEMS-based optical cochlear implant (OCI). Building on advances in optogenetics, it will enable the optical stimulation of neural activity in the auditory pathway at 10 independently controlled spots. The optical stimulation of the spiral ganglion neurons (SGNs) promises a pronounced increase in the number of discernible acoustic frequency channels in comparison with commercial cochlear implants based on the electrical stimulation. Ten high-efficiency light-emitting diodes are integrated as a linear array onto an only 12-μm-thick highly flexible polyimide substrate with three metal and three polyimide layers. The high mechanical flexibility of this novel OCI enables its insertion into a 300 μm wide channel with an outer bending radius of 1 mm. The 2 cm long and only 240 μm wide OCI is electrically passivated with a thin layer of Cy-top™.


title: BLEST: Blocking estimation-based MPTCP scheduler for heterogeneous networks

abstract: With the widespread availability of multi-homed devices, multipath transport protocols such as MPTCP are becoming increasingly relevant to support better use of multiple connectivity through capacity aggregation and seamless failover. However, capacity aggregation over heterogeneous paths, such as offered by cellular and Wi-Fi networks, is problematic. It causes packet reordering leading to head-of-line (HoL) blocking at the receiver, increased end-to-end delays and lower application goodput. MPTCP tackles this issue by penalising the use of longer paths, and increasing buffer sizes. This, however, results in suboptimal resource usage. In this paper, we first evaluate and compare the performance of default MPTCP and alternative state-of-the-art schedulers, all implemented in the Linux kernel, for a range of traffic patterns and network environments. This allows us to identify shortcomings of various approaches. We then propose a send-window BLocking ESTimation scheduler, BLEST, which aims to minimise HoL-blocking in heterogeneous networks, thereby increasing the potential for capacity aggregation by reducing the number of spurious retransmissions. The resulting scheduler allows an increase by 12% in application goodput with bulk traffic while reducing unnecessary retransmissions by 80% as compared to default MPTCP and other schedulers.


title: An Empirical Comparison of Term Association and Knowledge Graphs for Query Expansion

abstract: Term graphs constructed from document collections as well as external resources, such as encyclopedias (DBpedia) and knowledge bases (Freebase and ConceptNet), have been individually shown to be effective sources of semantically related terms for query expansion, particularly in case of difficult queries. However, it is not known how they compare with each other in terms of retrieval effectiveness. In this work, we use standard TREC collections to empirically compare the retrieval effectiveness of these types of term graphs for regular and difficult queries. Our results indicate that the term association graphs constructed from document collections using information theoretic measures are nearly as effective as knowledge graphs for Web collections, while the term graphs derived from DBpedia, Freebase and ConceptNet are more effective than term association graphs for newswire collections. We also found out that the term graphs derived from ConceptNet generally outperformed the term graphs derived from DBpedia and Freebase.


title: Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations

abstract: This article concerns the expressive power of depth in neural nets with ReLU activations and a bounded width. We are particularly interested in the following questions: What is the minimal width w min ( d ) so that ReLU nets of width w min ( d ) (and arbitrary depth) can approximate any continuous function on the unit cube [ 0 , 1 ] d arbitrarily well? For ReLU nets near this minimal width, what can one say about the depth necessary to approximate a given function? We obtain an essentially complete answer to these questions for convex functions. Our approach is based on the observation that, due to the convexity of the ReLU activation, ReLU nets are particularly well suited to represent convex functions. In particular, we prove that ReLU nets with width d + 1 can approximate any continuous convex function of d variables arbitrarily well. These results then give quantitative depth estimates for the rate of approximation of any continuous scalar function on the d-dimensional cube [ 0 , 1 ] d by ReLU nets with width d + 3 .


title: Sex-Related Differences in Hematological Stress Indices of Breeding Paedomorphic Mole Salamanders

abstract: Abstract Reproduction in amphibians is stressful and perhaps more so in females than males because of the higher energetic costs of producing eggs than sperm. The ratio of two white blood cells, neutrophils and lymphocytes, has been shown in birds, turtles, and amphibians to increase with stress. We captured breeding and nonbreeding, paedomorphic Mole Salamanders (Ambystoma talpoideum), to determine whether the stress of reproduction is reflected by neutrophil:lymphocyte ratios in this species. In blood smears of all individuals, we observed approximately 25.5 leukocytes per 1000 erythrocytes, with 13% of the leukocytes being neutrophils, 41% lymphocytes, and 46% eosinophils. Less than 1% of leukocytes were basophils and monocytes. Neutrophil:lymphocyte ratios of gravid females were significantly higher (and also more variable) than those of reproductive males and of nonreproductive individuals, indicating a higher degree of physiological stress in reproductive females. Reproductive males did not have hig...


title: An analysis of quiz in MOOC

abstract: Quiz is an assessment component in Massive Open Online Course (MOOC) for assessing knowledge of the learner and to provide feedback to help them in constructing their own learning path. MOOC is offered by the institutions on an existing platform — with or without customization, or a new platform may be developed; in all cases, there is a need to specify the features required for quiz in MOOC. In this paper, we present a checklist of features for quiz incorporated in MOOC, from the learner's perspective. The use-case based approach has been followed for identifying the features of quiz in MOOC. The checklist helps during requirement specification of quiz in MOOC, as the provider can select the desired features from the checklist.


title: Collaborative Layer-wise Discriminative Learning in Deep Neural Networks

abstract: Intermediate features at different layers of a deep neural network are known to be discriminative for visual patterns of different complexities. However, most existing works ignore such cross-layer heterogeneities when classifying samples of different complexities. For example, if a training sample has already been correctly classified at a specific layer with high confidence, we argue that it is unnecessary to enforce rest layers to classify this sample correctly and a better strategy is to encourage those layers to focus on other samples. ::: In this paper, we propose a layer-wise discriminative learning method to enhance the discriminative capability of a deep network by allowing its layers to work collaboratively for classification. Towards this target, we introduce multiple classifiers on top of multiple layers. Each classifier not only tries to correctly classify the features from its input layer, but also coordinates with other classifiers to jointly maximize the final classification performance. Guided by the other companion classifiers, each classifier learns to concentrate on certain training examples and boosts the overall performance. Allowing for end-to-end training, our method can be conveniently embedded into state-of-the-art deep networks. Experiments with multiple popular deep networks, including Network in Network, GoogLeNet and VGGNet, on scale-various object classification benchmarks, including CIFAR100, MNIST and ImageNet, and scene classification benchmarks, including MIT67, SUN397 and Places205, demonstrate the effectiveness of our method. In addition, we also analyze the relationship between the proposed method and classical conditional random fields models.


title: Determination Inventory Level for Aircraft Spare Parts Using Continuous Review Model

abstract: In this paper, we determine ordering quantity and reorder point for aircraft consumable spare parts. We use continuous review model to propose a spare part inventory policy that can be used in a aircraft maintenance company in Indonesia. We employ ABC classification system to categorize the spare parts based on their dollar contribution. We focus our research on managing the inventory level for spare parts on class A and B which commonly known as important classes. The result from the research indicates that the continuous review policy gives a significant amount of saving compared to an existing policy used by the company.


title: A Regularization Approach to Learning Task Relationships in Multitask Learning

abstract: Multitask learning is a learning paradigm that seeks to improve the generalization performance of a learning task with the help of some other related tasks. In this article, we propose a regularization approach to learning the relationships between tasks in multitask learning. This approach can be viewed as a novel generalization of the regularized formulation for single-task learning. Besides modeling positive task correlation, our approach—multitask relationship learning (MTRL)—can also describe negative task correlation and identify outlier tasks based on the same underlying principle. By utilizing a matrix-variate normal distribution as a prior on the model parameters of all tasks, our MTRL method has a jointly convex objective function. For efficiency, we use an alternating method to learn the optimal model parameters for each task as well as the relationships between tasks. We study MTRL in the symmetric multitask learning setting and then generalize it to the asymmetric setting as well. We also discuss some variants of the regularization approach to demonstrate the use of other matrix-variate priors for learning task relationships. Moreover, to gain more insight into our model, we also study the relationships between MTRL and some existing multitask learning methods. Experiments conducted on a toy problem as well as several benchmark datasets demonstrate the effectiveness of MTRL as well as its high interpretability revealed by the task covariance matrix.


title: Learning to Rank Academic Experts in the DBLP Dataset

abstract: Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state-of-the-art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph-structure of the citation patterns for the community of experts and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combining all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state-of-the-art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.


title: Age-related change in executive function: Developmental trends and a latent variable analysis

abstract: This study examined the developmental trajectories of three frequently postulated executive function (EF) components, Working Memory, Shifting, and Inhibition of responses, and their relation to performance on standard, but complex, neuropsychological EF tasks, the Wisconsin Card Sorting Task (WCST), and the Tower of London (ToL). Participants in four age groups (7-, 11-, 15-, and 21-year olds) carried out nine basic experimental tasks (three tasks for each EF), the WCST, and the ToL. Analyses were done in two steps: (1) analyses of (co)variance to examine developmental trends in individual EF tasks while correcting for basic processing speed, (2) confirmatory factor analysis to extract latent variables from the nine basic EF tasks, and to explain variance in the performance on WCST and ToL, using these latent variables. Analyses of (co)variance revealed a continuation of EF development into adolescence. Confirmatory factor analysis yielded two common factors: Working Memory and Shifting. However, the variables assumed to tap Inhibition proved unrelated. At a latent level, again correcting for basic processing speed, the development of Shifting was seen to continue into adolescence, while Working Memory continued to develop into young-adulthood. Regression analyses revealed that Working Memory contributed most strongly to WCST performance in all age groups. These results suggest that EF component processes develop at different rates, and that it is important to recognize both the unity and diversity of EF component processes in studying the development of EF.


title: Immune attack: the role of inflammation in Alzheimer disease

abstract: The past two decades of research into the pathogenesis of Alzheimer disease (AD) have been driven largely by the amyloid hypothesis; the neuroinflammation that is associated with AD has been assumed to be merely a response to pathophysiological events. However, new data from preclinical and clinical studies have established that immune system-mediated actions in fact contribute to and drive AD pathogenesis. These insights have suggested both novel and well-defined potential therapeutic targets for AD, including microglia and several cytokines. In addition, as inflammation in AD primarily concerns the innate immune system - unlike in 'typical' neuroinflammatory diseases such as multiple sclerosis and encephalitides - the concept of neuroinflammation in AD may need refinement.


title: Beam Steering Transmitarray Using Tunable Frequency Selective Surface With Integrated Ferroelectric Varactors

abstract: A tunable frequency selective surface (FSS) with beam steering capability is presented. The FSS is used as a transmitarray with a bandpass characteristic in Ku-band. The periodic sub-wavelength (λ0/25) unit-cells are composed of capacitive and inductive structures creating a bandpass for an incident wave. By patterning the capacitive elements on a screen-printed barium-strontium-titanate (BST) thick-film ceramic, the resonant frequency of the FSS can be tuned. This technology offers a simple and cost effective way for integrating varactors into the FSS and is particularly attractive for microwave circuits with a high varactor density. A prototype is fabricated including two capacitive layers with an overall size of 40 mm × 40 mm. Each layer includes a total of 1600 integrated BST varactors that are fabricated by using a patterning and metallization process. A transmitted wave passing through the FSS will experience a phase shift, which can be tuned by tuning the passband. This allows steering the transmitted wave to a certain direction by applying a phase gradient along the FSS interface. A phase shift range of 360° can be covered by cascading several FSS panels. The prototyped tunable FSS demonstrates the feasibility of the proposed technology and its potential for beam steering.


title: Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression

abstract: 3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the availability of multiple facial images (sometimes from the same subject) as input, and must address a number of methodological challenges such as establishing dense correspondences across large facial poses, expressions, and non-uniform illumination. In general these methods require complex and inefficient pipelines for model building and fitting. In this work, we propose to address many of these limitations by training a Convolutional Neural Network (CNN) on an appropriate dataset consisting of 2D images and 3D facial models or scans. Our CNN works with just a single 2D facial image, does not require accurate alignment nor establishes dense correspondence between images, works for arbitrary facial poses and expressions, and can be used to reconstruct the whole 3D facial geometry (including the non-visible parts of the face) bypassing the construction (during training) and fitting (during testing) of a 3D Morphable Model. We achieve this via a simple CNN architecture that performs direct regression of a volumetric representation of the 3D facial geometry from a single 2D image. We also demonstrate how the related task of facial landmark localization can be incorporated into the proposed framework and help improve reconstruction quality, especially for the cases of large poses and facial expressions. Code and models will be made available at http://aaronsplace.co.uk


title: NCBI GEO: mining millions of expression profiles—database and tools

abstract: The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest fully public repository for high-throughput molecularabundancedata,primarilygeneexpression data.Thedatabasehasaflexibleandopendesignthat allows the submission, storage and retrieval of many data types. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA and protein molecules, as well as nonarray-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. GEO currently holds over 30000 submissions representing approximately half a billion individual molecular abundance measurements, for over 100 organisms. Here, we describe recentdatabasedevelopmentsthatfacilitateeffective mining and visualization of these data. Features are provided to examine data from both experimentand gene-centric perspectives using user-friendly Web-based interfaces accessible to those without computational or microarray-related analytical expertise. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi. nlm.nih.gov/geo.


title: AngularJS Performance: A Survey Study

abstract: AngularJS is a popular JavaScript framework based on the model–view–controller pattern to construct single-page web apps. Researchers surveyed 95 professional developers regarding the performance problems of AngularJS applications. They determined the common practices the developers followed to avoid the problems (for example, using third-party or custom components), the problems’ general causes (for example, inadequate application architectures), and the problems’ technical causes (for example, unnecessary processing in the digest cycle, which is the internal computation that automatically updates the view with changes detected in the model).


title: A Robot and Control Algorithm That Can Synchronously Assist in Naturalistic Motion During Body-Weight-Supported Gait Training Following Neurologic Injury

abstract: Locomotor training using body weight support on a treadmill and manual assistance is a promising rehabilitation technique following neurological injuries, such as spinal cord injury (SCI) and stroke. Previous robots that automate this technique impose constraints on naturalistic walking due to their kinematic structure, and are typically operated in a stiff mode, limiting the ability of the patient or human trainer to influence the stepping pattern. We developed a pneumatic gait training robot that allows for a full range of natural motion of the legs and pelvis during treadmill walking, and provides compliant assistance. However, we observed an unexpected consequence of the device's compliance: unimpaired and SCI individuals invariably began walking out-of-phase with the device. Thus, the robot perturbed rather than assisted stepping. To address this problem, we developed a novel algorithm that synchronizes the device in real-time to the actual motion of the individual by sensing the state error and adjusting the replay timing to reduce this error. This paper describes data from experiments with individuals with SCI that demonstrate the effectiveness of the synchronization algorithm, and the potential of the device for relieving the trainers of strenuous work while maintaining naturalistic stepping.


Using New York Times Picks to Identify Constructive Comments.