title: SnipSuggest: Context-Aware Autocompletion for SQL

abstract: In this paper, we present SnipSuggest, a system that provides on-the-go, context-aware assistance in the SQL composition process. SnipSuggest aims to help the increasing population of non-expert database users, who need to perform complex analysis on their large-scale datasets, but have difficulty writing SQL queries. As a user types a query, SnipSuggest recommends possible additions to various clauses in the query using relevant snippets collected from a log of past queries. SnipSuggest's current capabilities include suggesting tables, views, and table-valued functions in the FROM clause, columns in the SELECT clause, predicates in the WHERE clause, columns in the GROUP BY clause, aggregates, and some support for sub-queries. SnipSuggest adjusts its recommendations according to the context: as the user writes more of the query, it is able to provide more accurate suggestions. ::: ::: We evaluate SnipSuggest over two query logs: one from an undergraduate database class and another from the Sloan Digital Sky Survey database. We show that SnipSuggest is able to recommend useful snippets with up to 93.7% average precision, at interactive speed. We also show that SnipSuggest outperforms naive approaches, such as recommending popular snippets.


title: Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders

abstract: Learning compressed representations of multivariate time series (MTS) facilitate the analysis and process of the data in presence of noise, redundant information, and large amount of variables and time steps. However, classic dimensionality reduction approaches are not designed to process sequential data, especially in the presence of missing values. In this work, we propose a novel autoencoder architecture based on recurrent neural networks to generate compressed representations of MTS, which may contain missing values and have variable lengths. Our autoencoder learns fixed-length vectorial representations, whose pairwise similarities are aligned with a kernel function that operates in input space and handles missing values. This, allows to preserve relationships in the low-dimensional vector space even in presence of missing values. To highlight the main features of the proposed autoencoder, we first investigate its performance in controlled experiments. Successively, we show how the learned representations can be exploited both in several benchmark and real-world classification tasks on medical data. Finally, based on the proposed architecture, we conceive a framework for one-class classification and imputation of missing data in time series extracted from ECG signals.


title: Coexistence of WBAN and WLAN in Medical Environments

abstract: In this work, we present a coexistence study for wireless body area networks (WBAN) and wireless local area networks (WLAN). The aim is to evaluate the feasibility of WBAN systems in realistic medical environments, assessing their robustness to interference. We focus on the evaluation of the impact of a collocated IEEE 802.11g WLAN interferer on the performance of IEEE 802.15.4/ZigBee transceivers, through a combination of physical (PHY) layer simulations and experi- mental interference tests in a hospital room. In order to have a better understanding of the underlying communication model, we have also conducted channel measurements, channel modeling and power spectrum measurements. The performance of ZigBee for different system configurations and node topologies is shown, providing insights into the main factors that affect the system performance in a hospital room. In addition, recommendations for high performance operation are provided.


Gallium Nitride RF Technology Advances and Applications


title: A novel inverse kinematics solution for a 7-DOF humanoid manipulator

abstract: Based on the special structure of a 7-DOF humanoid manipulator and its characteristics of self-motion, we propose a novel inverse kinematics solution using the geometric method at the level of position. The self-motion of the redundant manipulator is demonstrated and is used to solve the inverse kinematics. The method is concise and easy to realize, and the simulation results prove its efficiency.


title: Projection of RFID-Obtained Product Information on a Retail Stores Indoor Panoramas

abstract: RFID can be used to obtain information about objects present in a physical space, including their approximate location. Handheld RFID readers, smart shelves, zenithal antennas, and autonomous robots can obtain additional information with varying time and space resolutions. The authors present a system that projects this information on a panoramic view of a retail store, allowing users to virtually navigate the space and obtain quasi-real-time information about the products as they actually are in the store. For example, when a user clicks on the image of a shelf in the panorama, product information appears at or near that position from the last RFID-based inventory, which could be as recent as a few seconds ago.


title: FogMQ: A Message Broker System for Enabling Distributed, Internet-Scale IoT Applications over Heterogeneous Cloud Platforms

abstract: Excessive tail end-to-end latency occurs with conventional message brokers as a result of having massive numbers of geographically distributed devices communicate through a message broker. On the other hand, broker-less messaging systems, though ensure low latency, are highly dependent on the limitation of direct device-to-device (D2D) communication technologies, and cannot scale well as large numbers of resource-limited devices exchange messages. In this paper, we propose FogMQ, a cloud-based message broker system that overcomes the limitations of conventional systems by enabling autonomous discovery, self-deployment, and online migration of message brokers across heterogeneous cloud platforms. For each device, FogMQ provides a high capacity device cloning service that subscribes to device messages. The clones facilitate near-the-edge data analytics in resourceful cloud compute nodes. Clones in FogMQ apply Flock, an algorithm mimicking flocking-like behavior to allow clones to dynamically select and autonomously migrate to different heterogeneous cloud platforms in a distributed manner.


title: Pedestrian orientation classification utilizing single-chip coaxial RGB-ToF camera

abstract: This paper proposes a method for pedestrian orientation classification. In image recognition, the accuracy is often degraded by the influence of background. In addition, it is also difficult to remove the background and extract only the human body from an image. To overcome these problems, we utilize a single-chip RGB-ToF camera. This camera can acquire RGB and depth images along the same optical axis at the same moment, and thus segmentation of the RGB image becomes easier by using the coaxial depth image. Our proposed method segmented a human body from its background accurately, which lead to the improvement of the accuracy of pedestrian orientation classification.


title: Imaging characteristics of spindle cell lipoma and its variants

abstract: A spindle cell lipoma (SCL) is a relatively common tumor that can be challenging to the radiologist, pathologist, or surgeon to diagnose, particularly when internal fat content is scant or absent. Although these lesions may be found at various locations, the typical presentation for this lesion is a well-circumscribed and non-aggressive subcutaneous mass in the posterior neck presenting in a middle-aged to elderly man. In this article, the typical and atypical imaging characteristics of a spindle cell lipoma (SCL) will be reviewed. Knowledge of the common imaging and pathologic features of SCLs can help suggest the diagnosis and guide patient management.


title: A Versatile 10–80-Gb/s PRBS-Based Broadband Transmitter With Arbitrary 20–60-GHz Spectrum Shifting

abstract: A versatile pseudorandom bit sequence (PRBS)-based transmitter with the ability to mix the generated PRBS spectrum to arbitrary frequencies is presented. It incorporates a $2^{11} -1$ 80-Gb/s PRBS generator, employing parallel architecture. The generator is optimized to provide a high bit rate as well as a long sequence length for low power consumption without any need of inductive peaking, compared with the PRBS generators reported in the literature. The PRBS generator is capable of generating bit rates ranging from 10 up to 80 Gb/s. The transmitter can shift the generated PRBS spectrum to arbitrary frequencies ranging from 20 to 60 GHz via a broadband mixer of a double sideband output spectrum of 80-GHz bandwidth. A radar is realized using the presented transmitter, where a range resolution of 7.5 mm could be achieved. The transmitter consumes power ranging from 576 mW to 1 W, depending on the output bit rate of the integrated PRBS generator. The proposed circuit was manufactured in an 0.35- $\mu \text{m}$ SiGe technology with an $f_{t}$ of 200 GHz.


Basic Linear Algebra Subprograms for Fortran Usage


title: Privacy and analytics: it's a DELICATE issue a checklist for trusted learning analytics

abstract: The widespread adoption of Learning Analytics (LA) and Educational Data Mining (EDM) has somewhat stagnated recently, and in some prominent cases even been reversed following concerns by governments, stakeholders and civil rights groups about privacy and ethics applied to the handling of personal data. In this ongoing discussion, fears and realities are often indistinguishably mixed up, leading to an atmosphere of uncertainty among potential beneficiaries of Learning Analytics, as well as hesitations among institutional managers who aim to innovate their institution's learning support by implementing data and analytics with a view on improving student success. In this paper, we try to get to the heart of the matter, by analysing the most common views and the propositions made by the LA community to solve them. We conclude the paper with an eight-point checklist named DELICATE that can be applied by researchers, policy makers and institutional managers to facilitate a trusted implementation of Learning Analytics.


title: Multitemporal settlement and population mapping from Landsat using Google Earth Engine

abstract: As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.


title: An Approach for Anomaly Diagnosis Based on Hybrid Graph Model with Logs for Distributed Services

abstract: Detecting runtime anomalies is very important to monitoring and maintenance of distributed services. People often use execution logs for troubleshooting and problem diagnosis manually, which is time consuming and error-prone. In this paper, we propose an approach for automatic anomaly detection based on logs. We first mine a hybrid graph model that captures normal execution flows inter and intra services, and then raise anomaly alerts on observing deviations from the hybrid model. We evaluate the effectiveness of our approach by leveraging logs from an IBM public cloud production platform and two simulated systems in the lab environment. Evaluation results show that our hybrid graph model mining performs over 80% precision and 70% recall and anomaly detection performs nearly 90% precision and 80% recall on average.


title: Fish intelligence, sentience and ethics

abstract: Fish are one of the most highly utilised vertebrate taxa by humans; they are harvested from wild stocks as part of global fishing industries, grown under intensive aquaculture conditions, are the most common pet and are widely used for scientific research. But fish are seldom afforded the same level of compassion or welfare as warm-blooded vertebrates. Part of the problem is the large gap between people’s perception of fish intelligence and the scientific reality. This is an important issue because public perception guides government policy. The perception of an animal’s intelligence often drives our decision whether or not to include them in our moral circle. From a welfare perspective, most researchers would suggest that if an animal is sentient, then it can most likely suffer and should therefore be offered some form of formal protection. There has been a debate about fish welfare for decades which centres on the question of whether they are sentient or conscious. The implications for affording the same level of protection to fish as other vertebrates are great, not least because of fishing-related industries. Here, I review the current state of knowledge of fish cognition starting with their sensory perception and moving on to cognition. The review reveals that fish perception and cognitive abilities often match or exceed other vertebrates. A review of the evidence for pain perception strongly suggests that fish experience pain in a manner similar to the rest of the vertebrates. Although scientists cannot provide a definitive answer on the level of consciousness for any non-human vertebrate, the extensive evidence of fish behavioural and cognitive sophistication and pain perception suggests that best practice would be to lend fish the same level of protection as any other vertebrate.


title: Hotels vs. Peer-to-Peer Accommodation Rentals: Text Analytics of Consumer Reviews in Portland, Oregon

abstract: Peer-to-peer (P2P) accommodation rentals continue to grow at a phenomenal rate. Examining how this business model affects the competitive landscape of accommodation services is of strategic importance to hotels and tourism destinations. This study explores the competitive edge of P2P accommodation in comparison to hotels by extracting key content and themes from online reviews to explain the key service attributes sought by guests. The results from text analytics using terminology extraction and word co-occurrence networks indicate that even though guests expect similar core services such as clean rooms and comfortable beds, different attributes support the competitive advantage of hotels and P2P rentals. While conveniences offered by hotels are unparalleled by P2P accommodation, the latter appeal to consumers driven by experiential and social motivations. Managerial implications for hotels and P2P accommodation are provided.


title: Direct answers for search queries in the long tail

abstract: Web search engines now offer more than ranked results. Queries on topics like weather, definitions, and movies may return inline results called answers that can resolve a searcher's information need without any additional interaction. Despite the usefulness of answers, they are limited to popular needs because each answer type is manually authored. To extend the reach of answers to thousands of new information needs, we introduce Tail Answers: a large collection of direct answers that are unpopular individually, but together address a large proportion of search traffic. These answers cover long-tail needs such as the average body temperature for a dog, substitutes for molasses, and the keyboard shortcut for a right-click. We introduce a combination of search log mining and paid crowdsourcing techniques to create Tail Answers. A user study with 361 participants suggests that Tail Answers significantly improved users' subjective ratings of search quality and their ability to solve needs without clicking through to a result. Our findings suggest that search engines can be extended to directly respond to a large new class of queries.


title: KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors

abstract: Keypoint detection is one of the most importantpre-processing steps in tasks such as face modeling, recognitionand verification. In this paper, we present an iterative methodfor Keypoint Estimation and Pose prediction of unconstrainedfaces by Learning Efficient H-CNN Regressors (KEPLER) foraddressing the face alignment problem. Recent state of the artmethods have shown improvements in face keypoint detectionby employing Convolution Neural Networks (CNNs). Althougha simple feed forward neural network can learn the mappingbetween input and output spaces, it cannot learn the inherentstructural dependencies. We present a novel architecture calledH-CNN (Heatmap-CNN) which captures structured global andlocal features and thus favors accurate keypoint detecion. H-CNNis jointly trained on the visibility, fiducials and 3D-pose of theface. As the iterations proceed, the error decreases making thegradients small and thus requiring efficient training of DCNNs tomitigate this. KEPLER performs global corrections in pose andfiducials for the first four iterations followed by local correctionsin a subsequent stage. As a by-product, KEPLER also provides3D pose (pitch, yaw and roll) of the face accurately. In thispaper, we show that without using any 3D information, KEPLERoutperforms state of the art methods for alignment on challengingdatasets such as AFW [38] and AFLW [17].


title: Emotional intelligence as a moderator in the stress–burnout relationship: a questionnaire study on nurses

abstract: Aims and objectives. To investigate inter-relationships between emotional intelligence (EI), work stress and burnout in a group of nurses in the Western Cape Province, South Africa. The moderating effect of EI in the stress–burnout relationship and group differences (nurses working in different wards) in burnout were also investigated. ::: ::: Background. Stress and subsequent burnout commonly threaten the occupational health and well-being of nurses in South Africa and elsewhere. Developing EI in nurses may increase individual stress resistance and combat burnout. ::: ::: Design. A cross-sectional research design with anonymous questionnaires was conducted. Self-report data were used. ::: ::: Methods. Survey data were collected from 122 nurses working in different wards at four hospitals from a private hospital group. The Swinburne University Emotional Intelligence Test, Sources of Work Stress Inventory and Maslach Burnout Inventory were used to measure EI, stress and burnout, respectively. ::: ::: Results. Consistent inverse relationships between emotional control and management as dimensions of EI, and stress and burnout emerged. A differential effect of high vs. low EI on the stress–burnout relationship was evident. Workload and the work/family interface emerged as significant predictors of burnout. Respondents working in maternity, paediatric and ER wards reported more feelings of personal accomplishment than those working in general wards. ::: ::: Conclusions. Higher EI is significantly related with lower stress and burnout in a sample of South African nurses. The moderator effect of EI in the stress–burnout relationship suggests that enhanced EI may help diminish burnout development when chronic stress is experienced. ::: ::: Relevance to clinical practice. EI developmental interventions, if introduced in nursing curricula, may increase emotional coping resources and enhanced social skills, which may benefit the long-term occupational health of nurses. This may be relevant in developing countries, where environmental stressors related to the organisational context (budget constraints) and wider social factors (shortage of qualified nurses) are difficult to address.


title: A survey on abstractive summarization techniques

abstract: Text Summarization solves climacteric problems in furnishing information to the necessities of user. Due to explosive growth of digital data on internet, information floods are the results to the user queries. This makes user impractical to read entire documents and select the desirables. To this problem summarization is a novel approach which surrogates the original document by not deviating from the theme helps the user to find documents easily. Summarization area was broadly spread over different research fields, Natural Language Processing (NLP), Machine Learning and Semantics etc… Summarization is classified mainly into two techniques Abstract and Extract. This article gives a deep review of Abstract summarization techniques.


title: Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice

abstract: One of the major challenges that autonomous cars are facing today is driving in urban environments. To make it a reality, autonomous vehicles require the ability to communicate with other road users and understand their intentions. Such interactions are essential between vehicles and pedestrians, the most vulnerable road users. Understanding pedestrian behavior, however, is not intuitive and depends on various factors, such as demographics of the pedestrians, traffic dynamics, environmental conditions, and so on. In this paper, we identify these factors by surveying pedestrian behavior studies, both the classical works on pedestrian-driver interaction and the modern ones that involve autonomous vehicles. To this end, we will discuss various methods of studying pedestrian behavior and analyze how the factors identified in the literature are interrelated. We will also review the practical applications aimed at solving the interaction problem, including design approaches for autonomous vehicles that communicate with pedestrians and visual perception and reasoning algorithms tailored to understanding pedestrian intention. Based on our findings, we will discuss the open problems and propose future research directions.


title: Omnia Mutantur, Nihil Interit: Connecting Past with Present by Finding Corresponding Terms across Time

abstract: In the current fast-paced world, people tend to possess limited knowledge about things from the past. For example, some young users may not know that Walkman played similar function as iPod does nowadays. In this paper, we approach the temporal correspondence problem in which, given an input term (e.g., iPod) and the target time (e.g. 1980s), the task is to find the counterpart of the query that existed in the target time. We propose an approach that transforms word contexts across time based on their neural network representations. We then experimentally demonstrate the effectiveness of our method on the New York Times Annotated Corpus.


title: Splash: Fast data dissemination with constructive interference in wireless sensor networks

abstract: It is well-known that the time taken for disseminating a large data object over a wireless sensor network is dominated by the overhead of resolving the contention for the underlying wireless channel. In this paper, we propose a new dissemination protocol called Splash, that eliminates the need for contention resolution by exploiting constructive interference and channel diversity to effectively create fast and parallel pipelines over multiple paths that cover all the nodes in a network. We call this tree pipelining. In order to ensure high reliability, Splash also incorporates several techniques, including exploiting transmission density diversity, opportunistic overhearing, channel-cycling and XOR coding. Our evaluation results on two large-scale testbeds show that Splash is more than an order of magnitude faster than state-of-the-art dissemination protocols and achieves a reduction in data dissemination time by a factor of more than 20 compared to DelugeT2.


title: Retinal blood vessel segmentation based on the Gaussian matched filter and U-net

abstract: The automatic segmentation of retinal vessels plays an important role in the early screening of eye diseases. However, pathological retinal images are difficult for us to segment the vessels. In this paper, we regard the vessels segmentation task as a multi-label problem and combine the preprocessed method Gaussian matched filter with a new U-shaped fully convolutional neural network called U-net to generate a blood vessels segmentation framework. The output of this model can distinguish the vessels from background although in the inadequate contrast regions and pathological regions. The proposed method is tested on a publicly available dataset of DRIVE. Sensitivity, Specificity, Accuracy and Precision are used to evaluate our method, and the average classification accuracy is 0.9636 on the dataset of DRIVE. Performance results show that our method outperforms the state-of-the-art method for automatic retinal blood segmentation.


Using Argument Mining to Assess the Argumentation Quality of Essays


title: Sweetened Beverage Consumption, Incident Coronary Heart Disease, and Biomarkers of Risk in Men

abstract: Background—Sugar-sweetened beverage consumption is associated with weight gain and risk of type 2 diabetes mellitus. Few studies have tested for a relationship with coronary heart disease (CHD) or intermediate biomarkers. The role of artificially sweetened beverages is also unclear. Methods and Results—We performed an analysis of the Health Professionals Follow-Up Study, a prospective cohort study including 42 883 men. Associations of cumulatively averaged sugar-sweetened (eg, sodas) and artificially sweetened (eg, diet sodas) beverage intake with incident fatal and nonfatal CHD (myocardial infarction) were examined with proportional hazard models. There were 3683 CHD cases over 22 years of follow-up. Participants in the top quartile of sugar-sweetened beverage intake had a 20% higher relative risk of CHD than those in the bottom quartile (relative risk=1.20; 95% confidence interval, 1.09–1.33; P for trend <0.01) after adjustment for age, smoking, physical activity, alcohol, multivitamins, family history,...


title: Topic Discovery for Short Texts Using Word Embeddings

abstract: Discovering topics in short texts, such as news titles and tweets, has become an important task for many content analysis applications. However, due to the lack of rich context information in short texts, the performance of conventional topic models on short texts is usually unsatisfying. In this paper, we propose a novel topic model for short text corpus using word embeddings. Continuous space word embeddings, which is proven effective at capturing regularities in language, is incorporated into our model to provide additional semantics. Thus we model each short document as a Gaussian topic over word embeddings in the vector space. In addition, considering that background words in a short text are usually not semantically related, we introduce a discrete background mode over word types to complement the continuous Gaussian topics. We evaluate our model on news titles from data sources like abcnews, showing that our model is able to extract more coherent topics from short texts compared with the baseline methods and learn better topic representation for each short document.


title: TriggerScope: Towards Detecting Logic Bombs in Android Applications

abstract: Android is the most popular mobile platform today, and it is also the mobile operating system that is most heavily targeted by malware. Existing static analyses are effective in detecting the presence of most malicious code and unwanted information flows. However, certain types of malice are very difficult to capture explicitly by modeling permission sets, suspicious API calls, or unwanted information flows. One important type of such malice is malicious application logic, where a program (often subtly) modifies its outputs or performs actions that violate the expectations of the user. Malicious application logic is very hard to identify without a specification of the "normal," expected functionality of the application. We refer to malicious application logic that is executed, or triggered, only under certain (often narrow) circumstances as a logic bomb. This is a powerful mechanism that is commonly employed by targeted malware, often used as part of APTs and state-sponsored attacks: in fact, in this scenario, the malware is designed to target specific victims and to only activate under certain circumstances. In this paper, we make a first step towards detecting logic bombs. In particular, we propose trigger analysis, a new static analysis technique that seeks to automatically identify triggers in Android applications. Our analysis combines symbolic execution, path predicate reconstruction and minimization, and inter-procedural control-dependency analysis to enable the precise detection and characterization of triggers, and it overcomes several limitations of existing approaches. We implemented a prototype of our analysis, called TriggerScope, and we evaluated it over a large corpus of 9,582 benign apps from the Google Play Store and a set of trigger-based malware, including the recently-discovered HackingTeam's RCSAndroid advanced malware. Our system is capable of automatically identify several interesting time-, location-, and SMS-related triggers, is affected by a low false positive rate (0.38%), and it achieves 100% detection rate on the malware set. We also show how existing approaches, specifically when tasked to detect logic bombs, are affected by either a very high false positive rate or false negative rate. Finally, we discuss the logic bombs identified by our analysis, including two previously-unknown backdoors in benign apps.


title: STELLAR: spatial-temporal latent ranking for successive point-of-interest recommendation

abstract: Successive point-of-interest (POI) recommendation in location-based social networks (LBSNs) becomes a significant task since it helps users to navigate a number of candidate POIs and provides the best POI recommendations based on users' most recent check-in knowledge. However, all existing methods for successive POI recommendation only focus on modeling the correlation between POIs based on users' check-in sequences, but ignore an important fact that successive POI recommendation is a time-subtle recommendation task. In fact, even with the same previous check-in information, users would prefer different successive POIs at different time. To capture the impact of time on successive POI recommendation, in this paper, we propose a spatial-temporal latent ranking (STELLAR) method to explicitly model the interactions among user, POI, and time. In particular, the proposed STELLAR model is built upon a ranking-based pairwise tensor factorization framework with a fine-grained modeling of user-POI, POI-time, and POI-POI interactions for successive POI recommendation. Moreover, we propose a new interval-aware weight utility function to differentiate successive check-ins' correlations, which breaks the time interval constraint in prior work. Evaluations on two real-world datasets demonstrate that the STELLAR model outperforms state-of-the-art successive POI recommendation model about 20% in [email protected] and [email protected]


title: Approximate clustering without the approximation

abstract: Approximation algorithms for clustering points in metric spaces is a flourishing area of research, with much research effort spent on getting a better understanding of the approximation guarantees possible for many objective functions such as k-median, k-means, and min-sum clustering. ::: ::: This quest for better approximation algorithms is further fueled by the implicit hope that these better approximations also yield more accurate clusterings. E.g., for many problems such as clustering proteins by function, or clustering images by subject, there is some unknown correct "target" clustering and the implicit hope is that approximately optimizing these objective functions will in fact produce a clustering that is close pointwise to the truth. ::: ::: In this paper, we show that if we make this implicit assumption explicit---that is, if we assume that any c-approximation to the given clustering objective φ is e-close to the target---then we can produce clusterings that are O(e)-close to the target, even for values c for which obtaining a c-approximation is NP-hard. In particular, for k-median and k-means objectives, we show that we can achieve this guarantee for any constant c > 1, and for the min-sum objective we can do this for any constant c > 2. ::: ::: Our results also highlight a surprising conceptual difference between assuming that the optimal solution to, say, the k-median objective is e-close to the target, and assuming that any approximately optimal solution is e-close to the target, even for approximation factor say c = 1.01. In the former case, the problem of finding a solution that is O(e)-close to the target remains computationally hard, and yet for the latter we have an efficient algorithm.


title: Haptic feedback for pen computing: directions and strategies

abstract: We seek to improve the xperience of using pen computing devices by augmenting them with haptic, tactile feedback displays. We present the design of the haptic display for pen computers, and explore interaction techniques that allow users to feel GUI elements, textures, photographs and other interface elements with a pen. We discuss research directions in haptic displays for pen devices and report results of an early experimental study that evaluated the benefits of tactile feedback in pen computing.


title: Socrates, Skinner, and Aristotle: Three Ways of Thinking About Culture in Action1

abstract: The cultural turn has been one of the major shifts in sociology over the last two decades. Though nearly everyone now agrees that culture matters, how it matters is not terribly clear. What, exactly, is culture supposed to do? In this essay, I articulate two ideal-typical—though often implicit—ways most sociologists have thought about culture’s role in action. Although no single sociologist or piece of research fully embodies either ideal type, I believe they are real tendencies in the field that have real consequences for how research is designed, undertaken, and understood. After outlining these approaches, I subject them to an engagement with cognitive science. This is not out a desire for reductionism but, as I will show, because a crucial difference between these perspectives is their implicit model of how human beings perceive, acquire, store, retrieve, and act on the symbolic information that surrounds them every day. This exercise will lead to the conclusion that both perspectives are incomplete and will point toward a synthetic approach that can shed new light on how culture matters for action.


title: Cisplatin enhances the anticancer effect of β-lapachone by upregulating NQO1

abstract: NAD(P)H:quinone oxidoreductase (NQO1) has been reported to play an important role in cell death caused by beta-lapachone (beta-lap), 3,4-dihydro-22,2-dimethyl-2H-naphthol[1,22b]pyran-5,6-dione. This study investigated whether cisplatin (cis-diamminedichloroplatinum) sensitizes cancer cells to beta-lap by upregulating NQO1. The cytotoxicity of cisplatin and beta-lap alone or in combination against FSaII fibrosarcoma cells of C3H mice in vitro was determined with a clonogenic survival assay and assessment of gamma-H2AX foci formation, a hallmark of DNA double-strand breaks. The cellular sensitivity to beta-lap progressively increased during the 24 h after cisplatin treatment. The expression and enzymatic activity of NQO1 also increased during the 24 h after cisplatin treatment, and dicoumarol, an inhibitor of NQO1, was found to nullify the cisplatin-induced increase in beta-lap sensitivity. The role of NQO1 in the cell death caused by beta-lap alone or in combination with cisplatin was further elucidated using NQO1-positive and NQO1-negative MDA-MB-231 human breast cancer cells. Cisplatin increased the sensitivity of the NQO1-positive but not the NQO1-negative MDA-MB-231 cells to beta-lap treatment. Combined treatment with cisplatin and beta-lap suppressed the growth of FSaII tumors in the legs of C3H mice in a manner greater than additive. It is concluded that cisplatin markedly increases the sensitivity of cancer to beta-lap in vitro and in vivo by upregulating NQO1.


title: Two-Stream Transformer Networks for Video-Based Face Alignment

abstract: In this paper, we propose a two-stream transformer networks (TSTN) approach for video-based face alignment. Unlike conventional image-based face alignment approaches which cannot explicitly model the temporal dependency in videos and motivated by the fact that consistent movements of facial landmarks usually occur across consecutive frames, our TSTN aims to capture the complementary information of both the spatial appearance on still frames and the temporal consistency information across frames. To achieve this, we develop a two-stream architecture, which decomposes the video-based face alignment into spatial and temporal streams accordingly. Specifically, the spatial stream aims to transform the facial image to the landmark positions by preserving the holistic facial shape structure. Accordingly, the temporal stream encodes the video input as active appearance codes, where the temporal consistency information across frames is captured to help shape refinements. Experimental results on the benchmarking video-based face alignment datasets show very competitive performance of our method in comparisons to the state-of-the-arts.


title: Cuteness and Disgust: The Humanizing and Dehumanizing Effects of Emotion

abstract: Moral emotions are evolved mechanisms that function in part to optimize social relationships. We discuss two moral emotions-- disgust and the "cuteness response"--which modulate social-engagement motives in opposite directions, changing the degree to which the eliciting entity is imbued with mental states (i.e., mentalized). Disgust-inducing entities are hypo-mentalized (i.e., dehumanized); cute entities are hyper-mentalized (i.e., "humanized"). This view of cuteness--which challenges the prevailing view that cuteness is a releaser of parental instincts (Lorenz, 1950/1971)--explains (a) the broad range of affiliative behaviors elicited by cuteness, (b) the marketing of cuteness to children (by toy makers and animators) to elicit play, and (c) the apparent ease and frequency with which cute things are anthropomorphized.


Load Balancing in Cloud Using Enhanced Genetic Algorithm


title: MMCF: Multimodal Collaborative Filtering for Automatic Playlist Continuation

abstract: Automatic playlist continuation (APC) is a common task of music recommender systems, enabling the automatic discovery of tracks that fit into a given playlist. To recommend a coherent list of tracks to users, it is important to capture the underlying characteristics of a playlist. Unfortunately, existing recommender models suffer from several problems: (1) They tend to misinterpret tracks that appear rarely in a playlist (popularity bias) (2) they cannot extend user's playlist that consists of very few tracks (cold-start problem), and (3) they neglect the context of a playlist such as the sequence of tracks or playlist title (context-aware continuation). This year's ACM RecSys Challenge'18 aimed to find new solutions to tackle these problems. In this paper, we propose a multimodal collaborative filtering model to deal effectively with diverse data. This consists of two components: (1) an autoencoder using both the playlist and its categorical contents and (2) a character-level convolutional neural network using the playlist title only. By simultaneously analyzing the playlist and the categorical contents, our model successfully addresses the cold-start and popularity bias problems. In addition, we consider the context of a playlist by utilizing its title, thus enhancing the prediction of well-suited tracks. In the challenge, our team "hello world!" was ranked the 2nd place, scoring 0.224, 0.394, and 1.928 for the three evaluation metrics, respectively. Our implementation code is publicly available at https://github.com/hojinYang/spotify_recSys_challenge_2018.


title: Correlates of hair cortisol concentrations in disadvantaged young children

abstract: © 2018 John Wiley & Sons, Ltd. Children from highly disadvantaged families tend to experience worse health, educational, and job outcomes than less disadvantaged peers. However, the mechanisms underlying these relationships remain to be explicated. In particular, few studies have investigated the relationships between the psychosocial influences that children are exposed to early in life and longer term cortisol output. This study aims to contribute to the literature by exploring how disadvantaged young children's experiences of family adversity, and parenting and family functioning, are related to their long-term cortisol levels. A sample of 60 children (26 males, mean = 4.25 years, SD = 1.68) and their mothers (mean = 34.18 years, SD = 7.11) from a low-income population took part in a single assessment. Mothers completed questionnaires on the family environment, parenting practices, and child behaviour. Children provided a hair sample for cortisol assay and anthropometric measures. A parsimonious multivariate regression model (including potential predictors identified by a selection algorithm) was used to investigate the correlates of hair cortisol concentration (HCC) in children. Higher levels of social exclusion, being male, and younger age were each associated with higher HCC. Maternal nurturing and emotion coaching were associated with lower HCC. Findings suggest that chronic stress may underlie relationships between adversity and its long-term effects and that HCC offers a promising method for examining chronic stress in children and evaluating interventions by which it can be ameliorated.


title: High quality, low latency in-home streaming of multimedia applications for mobile devices

abstract: Today, mobile devices like smartphones and tablets are becoming more powerful and exhibit enhanced 3D graphics performance. However, the overall computing power of these devices is still limited regarding usage scenarios like photo-realistic gaming, enabling an immersive virtual reality experience or real-time processing and visualization of big data. To overcome these limitations application streaming solutions are constantly gaining focus. The idea is to transfer the graphics output of an application running on a server or even a cluster to a mobile device, conveying the impression that the application is running locally. User inputs on the mobile client side are processed and sent back to the server. The main criteria for successful application streaming are low latency, since users want to interact with the scene in near real-time, as well as high image quality. Here, we present a novel application framework suitable for streaming applications from high-end machines to mobile devices. Using real-time ETC1 compression in combination with a distributed rendering architecture we fully leverage recent progress in wireless computer networking standards (IEEE 802.11ac) for mobile devices, achieving much higher image quality at half the latency compared to other inhome streaming solutions.


title: Transitioning to DNA genomes in an RNA world

abstract: The unexpected ability of an RNA polymerase ribozyme to copy RNA into DNA has ramifications for understanding how DNA genomes evolved.


Effect of CaO on Dephosphorising Ability of Deoxidation Slag for Effective Utilisation of Phosphorus in Steel


title: A wideband circularly polarized stacked slotted microstrip patch antenna

abstract: A wideband, single-feed, circularly polarized, stacked, square, slotted microstrip patch antenna is proposed for radio-frequency-identification-reader applications. The proposed antenna consists of an upper square asymmetric slotted patch, stacked over a lower square asymmetric slotted radiating patch and a microstrip stub, along with a coaxial feed. The wideband circularly polarized radiation of the antenna is achieved using a microstrip stub in the lower patch and a stacked slotted square patch. The proposed antenna is designed using a combination of low-cost FR4 substrate and foam. The measured 10 dB return-loss bandwidth of the antenna was around 21% (833 MHz to 1033 MHz), and the 3 dB axial-ratio bandwidth of the antenna was around 13.4% (835 MHz to 955 MHz). The gain was around 7.0 dBic over the 3 dB axial-ratio bandwidth. The overall antenna volume was 0.601?0 × 0.601?0 × 0.895?0 (where ?0 is the free-space wavelength at 900 MHz).


title: Development of a high-performance magnetic gear

abstract: This paper presents calculation and measurement results of a high-performance permanent-magnetic gear. The analyzed permanent-magnetic gear has a gear ratio of 5.5 and is able to deliver 27 N/spl middot/m. The analysis has shown that special attention needs to be paid to the system where the gear is to be installed because of a low natural torsion spring constant. The analyzed gear was also constructed in practice in order to validate the analysis and predict the efficiency. The measured torque from the magnetic gear was only 16 N/spl middot/m reduced by the large end-effects. A systematic analysis of the loss components in the magnetic gear is also performed in order to figure out why the efficiency for the actual construction was only 81%. A large magnetic loss component originated in the bearings, where an unplanned extra bearing was necessary due to mechanical problems. Without the losses of magnetic origin in the bearings and less end-effects caused by relatively short stack, an impressive efficiency estimated at 96% can be obtained. Comparison with classical mechanical gears has shown that the magnetic gear has a better efficiency and a comparable torque per volume density. Finally, it is concluded that the results in this paper may help to initiate a shift from mechanical gears to magnetic gears.


title: Theory of mind (ToM) on robots: a functional neuroimaging study

abstract: Theory of Mind (ToM) is not only a key capability for cognitive development but also for successful social interaction. In order for a robot to interact successfully with a human both interaction partners need to have an adequate representation of the other's actions. In this paper we address the question of how a robot's actions are perceived and represented in a human subject interacting with the robot and how this perception is influenced by the appearance of the robot. We present the preliminary results of an fMRI-study in which participants had to play a version of the classical Prisoners' Dilemma Game (PDG) against four opponents: a human partner (HP), an anthropomorphic robot (AR), a functional robot (FR), and a computer (CP). The PDG scenario enables to implicitly measure mentalizing or Theory of Mind (ToM) abilities, a technique commonly applied in functional imaging. As the responses of each game partner were randomized unknowingly to the participants, the attribution of intention or will to an opponent (i.e. HP, AR, FR or CP) was based purely on differences in the perception of shape and embodiment. The present study is the first to apply functional neuroimaging methods to study human-robot interaction on a higher cognitive level such as ToM. We hypothesize that the degree of anthropomorphism and embodiment of the game partner will modulate cortical activity in previously detected ToM networks as the medial prefrontal lobe and anterior cingulate cortex.


title: Implementation and performance analysis of non-blocking collective operations for MPI

abstract: Collective operations and non-blocking point-to-point operations have always been part of MPI. Although non-blocking collective operations are an obvious extension to MPI, there have been no comprehensive studies of this functionality. In this paper we present LibNBC, a portable high-performance library for implementing non-blocking collective MPI communication operations. LibNBC provides non-blocking versions of all MPI collective operations, is layered on top of MPI-1, and is portable to nearly all parallel architectures. To measure the performance characteristics of our implementation, we also present a microbenchmark for measuring both latency and overlap of computation and communication. Experimental results demonstrate that the blocking performance of the collective operations in our library is comparable to that of collective operations in other high-performance MPI implementations. Our library introduces a very low overhead between the application and the underlying MPI and thus, in conjunction with the potential to overlap communication with computation, offers the potential for optimizing real-world applications.


title: Assessment of multi-camera calibration algorithms for two-dimensional camera arrays relative to ground truth position and direction

abstract: Camera calibration methods are commonly evaluated on cumula-tive reprojection error metrics, on disparate one-dimensional da-tasets. To evaluate calibration of cameras in two-dimensional ar-rays, assessments need to be made on two-dimensional datasets with constraints on camera parameters. In this study, accuracy of several multi-camera calibration methods has been evaluated on camera parameters that are affecting view projection the most. As input data, we used a 15-viewpoint two-dimensional dataset with intrinsic and extrinsic parameter constraints and extrinsic ground truth. The assessment showed that self-calibration methods using structure-from-motion reach equal intrinsic and extrinsic parame-ter estimation accuracy with standard checkerboard calibration al-gorithm, and surpass a well-known self-calibration toolbox, BlueCCal. These results show that self-calibration is a viable ap-proach to calibrating two-dimensional camera arrays, but im-provements to state-of-art multi-camera feature matching are nec-essary to make BlueCCal as accurate as other self-calibration methods for two-dimensional camera arrays.


Bell's Theorem without Free Will


Kinetics of endothermic decomposition reactions. 2. Effects of the solid and gaseous products


title: Social Touch in Human–Computer Interaction

abstract: Touch is our primary non-verbal communication channel for conveying intimate emotions and as such essential for our physical and emotional wellbeing. In our digital age, human social interaction is often mediated. However, even though there is increasing evidence that mediated touch affords affective communication, current communication systems (such as videoconferencing) still do not support communication through the sense of touch. As a result, mediated communication does not provide the intense affective experience of co-located communication. The need for ICT mediated or generated touch as an intuitive way of social communication is even further emphasized by the growing interest in the use of touch-enabled agents and robots for healthcare, teaching, and telepresence applications. Here, we review the important role of social touch in our daily life and the available evidence that affective touch can be mediated reliably between humans and between humans and digital agents. We base our observations on evidence from psychology, computer science, sociology, and neuroscience with focus on the first two. Our review shows that mediated affective touch can modulate physiological responses, increase trust and affection, help to establish bonds between humans and avatars or robots, and initiate pro-social behavior. We argue that ICT mediated or generated social touch can (a) intensify the perceived social presence of remote communication partners and (b) enable computer systems to more effectively convey affective information. However, this research field on the crossroads of ICT and psychology is still embryonic and we identify several topics that can help to mature the field in the following areas: establishing an overarching theoretical framework, employing better research methodologies, developing basic social touch building blocks, and solving specific ICT challenges.


title: Image quality assessment and human visual system

abstract: This paper summaries the state-of-the-art of image quality assessment (IQA) and human visual system (HVS). IQA provides an objective index or real value to measure the quality of the specified image. Since human beings are the ultimate receivers of visual information in practical applications, the most reliable IQA is to build a computational model to mimic the HVS. According to the properties and cognitive mechanism of the HVS, the available HVS-based IQA methods can be divided into two categories, i.e., bionics methods and engineering methods. This paper briefly introduces the basic theories and development histories of the above two kinds of HVS-based IQA methods. Finally, some promising research issues are pointed out in the end of the paper.


title: ROS-based mapping, localization and autonomous navigation using a Pioneer 3-DX robot and their relevant issues

abstract: The Robot Operating System (ROS) provides operating system-like services to operate robots. Mapping, localization, and autonomous navigation in an indoor environment are popular issues in the field of autonomous robots. Autonomous navigation in a dynamic environment is not only challenging but also uncovers many indoor environmental factors which affect the process of mapping and navigation. The presented work describes how a ROS-based control system is used with a Pioneer 3-DX robot for indoor mapping, localization, and autonomous navigation. Mapping of different challenging environments is presented in this work. Moreover, some factors associated with indoor environments that can affect mapping, localization, and automatic navigation, are also presented. For experiments, three environments (one artificial and two real) have been tested. Some implementation was done in C and Python.


title: An information complexity approach to extended formulations

abstract: We prove an unconditional lower bound that any linear program that achieves an O(n1-e) approximation for clique has size 2Ω(ne). There has been considerable recent interest in proving unconditional lower bounds against any linear program. Fiorini et al. proved that there is no polynomial sized linear program for traveling salesman. Braun et al. proved that there is no polynomial sized O(n1/2 - e)-approximate linear program for clique. Here we prove an optimal and unconditional lower bound against linear programs for clique that matches Hastad's celebrated hardness result. Interestingly, the techniques used to prove such lower bounds have closely followed the progression of techniques used in communication complexity. Here we develop an information theoretic framework to approach these questions, and we use it to prove our main result. Also we resolve a related question: How many bits of communication are needed to get e-advantage over random guessing for disjointness? Kalyanasundaram and Schnitger proved that a protocol that gets constant advantage requires Ω(n) bits of communication. This result in conjunction with amplification implies that any protocol that gets e-advantage requires Ω(e2 n) bits of communication. Here we improve this bound to Ω(e n), which is optimal for any ε > 0.


title: Nutritional planning for Nellore heifers post-weaning to conception at 15 months of age: performance and nutritional, metabolic, and reproductive responses

abstract: The objective of this study was to evaluate the effects of strategic supplementation in the dry period and dry/rainy transition period on the performance and nutritional, metabolic, and reproductive responses of Nellore heifers grazing Urochloa decumbens. Forty-eight Nellore heifers with age and body weight (BW) of eight months and 235 ± 3.3 kg, respectively, were used. The experimental design was a completely randomized design, with four treatments, all with 12 replications. The evaluated strategies were as follows: low supplementation (LOHI; 0.2% of BW/heifer/day) in the first 90 days and high supplementation (0.6% of BW/heifer/day) in the 90 days thereafter; average supplementation (AVER) with 0.4% of BW/heifer/day for 180 days; high supplementation (HILO; 0.6% of BW/heifer/day) in the first 90 days and low supplementation (0.2% of BW/heifer/day) in the 90 days thereafter; only mineral mix (MM) ad libitum during the 180 days. Data were evaluated using orthogonal contrasts. Supplementation improved the performance of the animals during of dry period (P < 0.05) and dry/rainy transition period (P < 0.05). Supplemented animals had higher longissimus muscle area (LMA) and subcutaneous fat thickness (SFT) at the end of the experiment (P < 0.05). Multiple supplementation increased intake of dry matter (DM), organic matter (OM), and crude protein (CP) in kg/day throughout the experiment. The supplementation increased the digestibility of DM, OM, CP, apNDF, and TDN (P < 0.05). Serum urea nitrogen (SUN), glucose (GLUC), insulin (INS), and progesterone (PROG) were higher in supplemented heifers (P < 0.05). Supplementation reduced the concentrations of non-esterified fatty acids (NEFA) (P < 0.05) and increased conception rate (P < 0.05). In summary, the supplementation strategies adopted in this study improve the performance, metabolic status, and carcass traits of heifers under grazing, allowing an improvement in the conception rate of 15-month-old Nellore heifers.


title: LEADER-MEMBER EXCHANGE AS A MEDIATOR OF THE RELATIONSHIP BETWEEN TRANSFORMATIONAL LEADERSHIP AND FOLLOWERS' PERFORMANCE AND ORGANIZATIONAL CITIZENSHIP BEHAVIOR.

abstract: We developed a model in which leader-member exchange mediated between perceived transformational leadership behaviors and followers' task performance and organizational citizenship behaviors. Our sample comprised 162 leader-follower dyads within organizations situated throughout the People's Republic of China. We showed that leader-member exchange fully mediated between transformational leadership and task performance as well as organizational citizenship behaviors. Implications for the theory and practice of leadership are discussed, and future research directions offered.


title: O- and S-methylated bromothiocatechols.

abstract: Bromobenzene is metabolized by the Hartley guinea pig to two different bromothiocatechols, 4-bromo-2-hydroxythiophenol and 5-bromo-2-hydroxythiophenol. Both the thiol and phenol functional groups of thiocatechol undergo biological methylation. Methylation at the thiol group leads to the formation of (methylthio)bromophenol (S-methylated bromothiocatechol), while methylation of the phenol group leads to methoxybromothiophenol (O-methylated bromothiocatechol). This resulted in the urinary excretion of four O- and S-methylated bromothiocatechols. Bromothiocatechols could be formed by dehydrogenation of their corresponding bromodihydrobenzene thiolols. Both the 3-S- and 4-S-bromodihydrobenzene thiolols, as S-methylated products, were found as urinary metabolites of bromobenzene in the Hartley guinea pig. All four O- and S-methylated bromothiocatechols and two S-methylated bromodihydrobenzene thiolols were also found as urinary metabolites of bromobenzene in the golden Syrian hamster.


title: Core algorithms of the Maui scheduler

abstract: The Maui scheduler has received wide acceptance in the HPC community as a highly configurable and effective batch scheduler. It is currently in use on hundreds of SP, O2K, and Linux cluster systems throughout the world including a high percentage of the largest and most cutting edge research sites. While the algorithms used within Maui have proven themselves effective, nothing has been published to date documenting these algorithms nor the configurable aspects they support. This paper focuses on three areas of Maui scheduling, specifically, backfill, job prioritization, and fairshare. It briefly discusses the goals of each component, the issues and corresponding design decisions, and the algorithms enabling the Maui policies. It also covers the configurable aspects of each algorithm and the impact of various parameter selections.


title: Document overlap detection system for distributed digital libraries

abstract: In this paper we introduce the MatchDetectReveal(MDR) system, which is capable of identifying overlapping and plagiarised documents. Each component of the system is briefly described. The matching-engine component uses a modified suffix tree representation, which is able to identify the exact overlapping chunks and its performance is also presented.


title: Simulation on Photosynthetically Active Radiation Distributing in Rice Canopy With Rolled Leaves and Its Optimum Leaf Rolling Index

abstract: By replacing leaf area index(LAI)with effective leaf area index(ELAI)through introduction of leaf rolling index(LRI),the distribution of photosynthetically active radiation(PAR)in the canopy of three hybrid rice combinations,Liangyou E32 with high leaf rolling index,Liangyoupeijiu with medium leaf rolling index and Shanyou 63 with non-rolled(normal)leaves,was simulated.The results showed that the model based on ELAI could predict more accurately than that based on LAI.PAR interception,conversion and utilization efficiency of the three combinations were studied and their each optimal LRI and LAI were evaluated.The PAR utilization efficiency of Liangyou E32 was lower due to excessive rolled leaves and less ELAI,and that of Shanyou 63 was also lower because of the faulty PAR interception and lower photosynthetic rate and saturation point at lower layer in the canopy.Compared with the other two combinations,Liangyoupeijiu showed more appropriate distribution of PAR interception conversion efficiency in canopy with higher PAR utilization efficiency.The optimal LRI and LAI for Liangyoupeijiu were 0.11 and 7.6,respectively,which were close to the observed values,0.11 and 7.9,respectively.However,the optimum LAI of 9.8 for Liangyou E32 and 6.2 for Shanyou 63 were larger or smaller than those under the current plant density,which led to lower PAR utilization efficiency.The optimum LRI of the three hybrids was concluded as 0.08-0.12,close to the actual LRI of Liangyoupeijiu.


title: Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learner

abstract: Abstract Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which ’reverse engineering’ language development, i.e., building an effective system that mimics infant’s achievements, can contribute to our scientific understanding of early language development. We argue that, on the computational side, it is important to move from toy problems to the full complexity of the learning situation, and take as input as faithful reconstructions of the sensory signals available to infants as possible. On the data side, accessible but privacy-preserving repositories of home data have to be setup. On the psycholinguistic side, specific tests have to be constructed to benchmark humans and machines at different linguistic levels. We discuss the feasibility of this approach and present an overview of current results.


Compact Rectangular Microstrip Patch Antenna with Defected Ground Structure ( DGS ) of Swastik Shape for LTE Applications


title: Design and optimization of broadband asymmetrical multisection wilkinson power divider

abstract: In this paper, a numerical algorithm based on the method of least squares (MLS) is presented for the design and optimization of a Wilkinson power divider for arbitrary power division between its outputs in a specified frequency bandwidth incorporating source and load impedance matching. At first, its transmission, admittance, and scattering matrices are determined successively by considering the dispersion and dissipation models of substrate and metallic strips. An error function is then constructed for the desired output power division, maximum level of isolation between them, and the least amount of reflected power. Minimization of the error function with respect to the widths and lengths of strips and values of resistors provides their optimum values. A procedure is provided for the initial design and determination of initial values of parameters of a Wilkinson power divider based on the method of even- and odd-mode analysis. The optimum design of the Wilkinson power divider by the proposed MLS procedure is validated by available full-wave analysis software and fabrication and measurement of two samples.


title: An empirical examination of factors contributing to the creation of successful e-learning environments

abstract: Although existing models of e-learning effectiveness in information systems (IS) have increased our understanding of how technology can support and enhance learning, most of our models do not take into account the importance of social presence. Thus, this study extends previous research by developing a model of e-learning effectiveness which adds social presence to other oft studied variables including application-specific computer self-efficacy (AS-CSE), perceived usefulness, course interaction, and e-learning effectiveness. Using data from 345 individuals, this model was validated through a field study in an introductory IS survey course. Results indicate that AS-CSE and perceived usefulness were related to course performance, course satisfaction, and course instrumentality. In addition, course interaction was related to course performance and satisfaction. Finally, social presence was related to course satisfaction and course instrumentality. Implications for research and practice are discussed.


title: The bayesian lasso

abstract: The Lasso estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regression parameters have independent Laplace (i.e., double-exponential) priors. Gibbs sampling from this posterior is possible using an expanded hierarchy with conjugate normal priors for the regression parameters and independent exponential priors on their variances. A connection with the inverse-Gaussian distribution provides tractable full conditional distributions. The Bayesian Lasso provides interval estimates (Bayesian credible intervals) that can guide variable selection. Moreover, the structure of the hierarchical model provides both Bayesian and likelihood methods for selecting the Lasso parameter. Slight modifications lead to Bayesian versions of other Lasso-related estimation methods, including bridge regression and a robust variant.


title: Tobacco use among college students

abstract: Revolvable, hollow spheroid has a pair of tubes communicating with its interior 180 DEG apart. The free ends of the tubes may be closed with caps to prevent the animal from escaping. A housing mount rotatably mounts the spheroid and serves as an access opening for an animal of the rodent type. The animal may rotate the spheroid by running on its inner surface or by entering one of the tubes. The housing mount includes a fitting for connecting the spheroid to a tube communicating with an animal cage.


title: Rapid non-genomic effects of corticosteroids and their role in the central stress response

abstract: In response to a stressful encounter, the brain activates a comprehensive stress system that engages the organism in an adaptive response to the threatening situation. This stress system acts on multiple peripheral tissues and feeds back to the brain; one of its key players is the family of corticosteroid hormones. Corticosteroids affect brain functioning through both delayed, genomic and rapid, non-genomic mechanisms. The latter mode of action has long been known, but it is only in recent years that the physiological basis in the brain is beginning to be unravelled. We now know that corticosteroids exert rapid, non-genomic effects on the excitability and activation of neurons in (amongst others) the hypothalamus, hippocampus, amygdala and prefrontal cortex. In addition, corticosteroids affect cognition, adaptive behaviour and neuroendocrine output within minutes. Knowledge on the identity of the receptors and secondary pathways mediating the non-genomic effects of corticosteroids on a cellular level is accumulating. Interestingly, in many cases, an essential role for the 'classical' mineralocorticoid and glucocorticoid receptors in a novel membrane-associated mechanism is found. Here, we systematically review the recent literature on non-genomic actions of corticosteroids on neuronal activity and functioning in selected limbic brain targets. Further, we discuss the relevance of these permissive effects for cognition and neuroendocrine control, and the integration of this novel mode of action into the complex balanced pattern of stress effects in the brain.


A Look at Online Targeted Advertising in Information Systems Research


The Effects of Cell Phone Distractions on Cognitive Flexibility


title: Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation

abstract: This paper proposes a novel approach to generate multiple color palettes that reflect the semantics of input text and then colorize a given grayscale image according to the generated color palette. In contrast to existing approaches, our model can understand rich text, whether it is a single word, a phrase, or a sentence, and generate multiple possible palettes from it. For this task, we introduce our manually curated dataset called Palette-and-Text (PAT). Our proposed model called Text2Colors consists of two conditional generative adversarial networks: the text-to-palette generation networks and the palette-based colorization networks. The former captures the semantics of the text input and produce relevant color palettes. The latter colorizes a grayscale image using the generated color palette. Our evaluation results show that people preferred our generated palettes over ground truth palettes and that our model can effectively reflect the given palette when colorizing an image.


title: Supervised image classification by contextual AdaBoost based on posteriors in neighborhoods

abstract: AdaBoost, a machine learning technique, is employed for supervised classification of land-cover categories of geostatistical data. We introduce contextual classifiers based on neighboring pixels. First, posterior probabilities are calculated at all pixels. Then, averages of the log posteriors are calculated in different neighborhoods and are then used as contextual classification functions. Weights for the classification functions can be determined by minimizing the empirical risk with multiclass. Finally, a convex combination of classification functions is obtained. The classification is performed by a noniterative maximization procedure. The proposed method is applied to artificial multispectral images and benchmark datasets. The performance of the proposed method is excellent and is similar to the Markov-random-field-based classifier, which requires an iterative maximization procedure.


title: Patient and prescriber perspectives on long-acting injectable (LAI) antipsychotics and analysis of in-office discussion regarding LAI treatment for schizophrenia

abstract: The research goal is to better understand prescriber, patient, and caregiver perspectives about long-acting injectable (LAI) antipsychotic therapy and how these perspectives affect LAI use. Addressing these perspectives in the clinic may lead to greater success in achieving therapeutic goals for the patient with schizophrenia. Ethnographic information was collected from a non-random sample of 69 prescriber-patient conversations (60 with community mental health center [CMHC] psychiatrists; 9 with nurse-practitioners) recorded during treatment visits from August 2011 to February 2012, transcribed and analyzed. Discussions were categorized according to 11 predetermined CMHC topics. In-person observations were also conducted at 4 CMHCs, including home visits by researchers (n = 15 patients) prior to the CMHC visit and observations of patients receiving injections and interacting with staff. Telephone in-depth interviews with psychiatrists, patients, and caregivers to gather additional information on LAI discussion, prescription, or use were conducted. Antipsychotic treatment decisions were made without patient or caregiver input in 40 of 60 (67%) of psychiatrist-patient conversations. Involvement of patients or caregivers in treatment decisions was greater when discussing LAI (15 of 60 [25%]) vs oral antipsychotic treatment (5 of 60 [8%]). LAIs were not discussed by psychiatrists in 11 of 22 (50%) patients taking oral antipsychotics. When offered, more LAI-naïve patients expressed neutral (9 of 19 [47%]) rather than favorable (3 of 19 [16%]) or unfavorable (7 of 19 [37%]) responses. Prescribers were most concerned about potentially damaging the therapeutic relationship and side-effects when discussing LAIs while patient resistance was often related to negative feelings about injections. Psychiatrists had some success in overcoming patient objections to LAIs by addressing and decomposing initial resistance. More than half (11 of 19 [58%]) of LAI-naïve patients agreed to start LAI treatment following office visits. Patient-described benefits of LAIs vs orals included perceived rapid symptom improvement and greater overall efficacy. In this study, many psychiatrists did not offer LAIs and most patients and caregivers were not involved in antipsychotic treatment decision making. Opportunities to increase active patient engagement, address resistances, guide patient drug-formulation selection, and provide better LAI-relevant information for more individualized approaches to treating the patient with schizophrenia were present.


title: Kinematics of parallel kinematic machine Exechon

abstract: Due to the potential advantages of heavy load, high speed, and high precision, hundreds of parallel kinematic machines have been proposed. However, only a few of them have been practically applied, and one of the recently-developed parallel kinematic machines is the Exechon machine tool. It is based on the success of the Tricept, and its inventor has made tremendous efforts to promote its machining applications in aerospace and automotive manufacturing. The current control system of the Exechon machine tool has been developed by Siemens. However, quantifiable theoretical studies have not yet conducted to prove its super performance, and its kinematic model has been publically available. In this paper, the kinematic characteristics of this new machine tool is investigated, the concise models of forward and inverse kinematics have been developed. These models can be used as a tool or references for the evaluation of the performances of the Exechon machine tool or other over-constrained parallel kinematic machines.


ProcNets: Learning to Segment Procedures in Untrimmed and Unconstrained Videos.


title: "It's alive, it's magic, it's in love with you": opportunities, challenges and open questions for actuated interfaces

abstract: Actuated Interfaces are receiving a great deal of interest from the research community. The field can now present a range of point designs, illustrating the potential design space of Actuated Interfaces. However, despite the increasing interest in Actuated Interfaces, the research carried out is nevertheless primarily preoccupied with the technical challenges and potential application areas, rather than how users actually approach, experience, interpret and understand Actuated Interfaces. Based on three case studies, investigating how people experience Actuated Interfaces, we point to; magic, movement and ambiguity as fruitful perspectives for understanding users' experiences with Actuated Interfaces. The three perspectives are employed to reflect upon opportunities and challenges, as well as point to open questions and relevant areas for future research for Actuated Interfaces.


title: Relationship status and drinking-related outcomes in a community sample of lesbian and bisexual women

abstract: Although marriage tends to be protective against hazardous drinking among women in the general population, few studies have compared drinking rates, levels, or problems based on relationship status among sexual minority women (SMW; lesbian, bisexual). We examined associations between relationship status (committed relationship/cohabiting; committed/not cohabiting; single) and past-year drinking outcomes using data from a diverse sample of 696 SMW interviewed in wave 3 of the 17-year longitudinal Chicago Health and Life Experiences of Women study. The mean age of SMW in the sample was 40.01 (SD = 14.15; range 18–82). A little more than one-third (37%) of the sample was White, 36% was African American, and 23% Latina; 4% reported another or multirace/ethnicity. Compared to SMW in committed cohabiting relationships, single SMW were significantly more likely to be heavy drinkers. SMW in committed noncohabiting relationships were more likely to report alcohol-related problem consequences, and both single SMW a...


title: Cerebellar Changes in Guinea Pig Offspring Following Suppression of Neurosteroid Synthesis During Late Gestation

abstract: Elevated gestational concentrations of allopregnanolone are essential for the development and neuroprotection of the foetal brain. Preterm birth deprives the foetus of these high levels of allopregnanolone, which may contribute to the associated adverse effects on cerebellar development. Preterm birth alters expression of GABAA receptor subunit composition, which may further limit neurosteroid action. The objective of this study was to determine the effects of suppression of allopregnanolone levels on the markers of development and functional outcome. Pregnant guinea pigs were treated with finasteride at a dose (25 mg/kg maternal weight) shown to suppress allopregnanolone between 60 days of gestation until delivery (term ∼71 days). The cerebella from neonates, whose mothers were treated with finasteride or vehicle during pregnancy, were collected at postnatal age 8. Pups that received finasteride displayed significantly greater glial fibrillary acid protein area coverage and reduced GABAA receptor α6-subunit messenger RNA within the cerebellum than pups that were exposed to vehicle. These findings indicate that loss of neurosteroid action on the foetal brain in late gestation produces prolonged astrocyte activation and reductions in GABAA receptor α6-subunit expression. These changes may contribute to the long-term changes in function associated with preterm birth.


title: Extracting and Modeling Product Line Functional Requirements

abstract: We introduce an extractive approach to building a product line's requirements assets. We define the functional requirements profiles (FRPs) according to the linguistic characterization of a domain's action-oriented concerns, and show that FRPs can be extracted from a document based on domain-aware lexical affinities that bear a 'verb - direct object' relation. The validated FRPs are then amenable to semantic case analysis so as to uncover the variation structures. Finally, merging FRPs helps discover the requirements interdependencies. We use orthogonal variability modeling to represent the product line's external variability and constraints. We apply our approach to an auto-marker product line. The study shows our approach complements domain analysis by quickly offering insights into system functionalities and product line variabilities.


title: Recognition of Multiple-Food Images by Detecting Candidate Regions

abstract: In this paper, we propose a two-step method to recognize multiple-food images by detecting candidate regions with several methods and classifying them with various kinds of features. In the first step, we detect several candidate regions by fusing outputs of several region detectors including Felzenszwalb's deformable part model (DPM) [1], a circle detector and the JSEG region segmentation. In the second step, we apply a feature-fusion-based food recognition method for bounding boxes of the candidate regions with various kinds of visual features including bag-of-features of SIFT and CSIFT with spatial pyramid (SP-BoF), histogram of oriented gradient (HoG), and Gabor texture features. In the experiments, we estimated ten food candidates for multiple-food images in the descending order of the confidence scores. As results, we have achieved the 55.8% classification rate, which improved the baseline result in case of using only DPM by 14.3 points, for a multiple-food image data set. This demonstrates that the proposed two-step method is effective for recognition of multiple-food images.


title: Light-weight bounds checking

abstract: Memory errors in C and C++ programs continue to be one of the dominant sources of security problems, accounting for over a third of the high severity vulnerabilities reported in 2011. Wide-spread deployment of defenses such as address-space layout randomization (ASLR) have made memory exploit development more difficult, but recent trends indicate that attacks are evolving to overcome this defense. Techniques for systematic detection and blocking of memory errors can provide more comprehensive protection that can stand up to skilled adversaries, but unfortunately, these techniques introduce much higher overheads and provide significantly less compatibility than ASLR. We propose a new memory error detection technique that explores a part of the design space that trades off some ability to detect bounds errors in order to obtain good performance and excellent backwards compatibility. On the SPECINT 2000 benchmark, the runtime overheads of our technique is about half of that reported by the fastest previous bounds-checking technique. On the compatibility front, our technique has been tested on over 7 million lines of code, which is much larger than that reported for previous bounds-checking techniques.


title: PATRIC, the bacterial bioinformatics database and analysis resource

abstract: The Pathosystems Resource Integration Center (PATRIC) is the all-bacterial Bioinformatics Resource Center (BRC) (http://www.patricbrc.org). A joint effort by two of the original National Institute of Allergy and Infectious Diseases-funded BRCs, PATRIC provides researchers with an online resource that stores and integrates a variety of data types [e.g. genomics, transcriptomics, protein–protein interactions (PPIs), three-dimensional protein structures and sequence typing data] and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes in PATRIC, currently more than 10 000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. All the data and integrated analysis and visualization tools are freely available. This manuscript describes updates to the PATRIC since its initial report in the 2007 NAR Database Issue.


title: Development of a compact wrist mechanism with high torque density

abstract: This paper proposes a new wrist mechanism for robot manipulation. To develop multi-dof wrist mechanisms that can emulate human wrists, compactness and high torque density are the major challenges. Traditional wrist mechanisms consist of series of rotary motors that require gearing to amplify the output torque. This often results in a bulky wrist mechanism. Instead, large linear force can be easily realized in a compact space by using leadscrew motors. Inspired by the muscle-tendon actuation pattern, the proposed mechanism consists of two parallel placed linear motors. Their linear motions are transmitted to two perpendicular rotations through a spherical mechanism and two slider crank mechanisms. High torque density can be achieved. Static and dynamic models are developed to design the wrist mechanism. A wrist prototype and its position control experiments will be presented with results discussed. The novel mechanism is expected to serve as an alternative for robot manipulators in applications that require human-friendly interactions.


title: Delaying Shoulder Motion and Strengthening and Increasing Achilles Allograft Thickness for Glenoid Resurfacing Did Not Improve the Outcome for a 30-Year-Old Patient with Postarthroscopic Glenohumeral Chondrolysis

abstract: Although interposition soft-tissue (biologic) resurfacing of the glenoid with humeral hemiarthroplasty has been considered an option for end-stage glenohumeral arthritis, the results of this procedure are highly unsatisfactory in patients less than 40 years old. Achilles tendon allograft is popular for glenoid resurfacing because it can be made robust by folding it. But one reason that the procedure might fail in younger patients is that the graft is not initially thick enough for the young active patient. Most authors report folding the graft only once to achieve two-layer thickness. We report the case of a 30-year-old male who had postarthroscopic glenohumeral chondrolysis that was treated with Achilles tendon allograft resurfacing of the glenoid and humeral hemiarthroplasty. An important aspect of our case is that the tendon was folded so that it was 50–100% thicker than most allograft constructs reported previously. We also used additional measures to enhance allograft resiliency and bone incorporation: (1) multiple nonresorbable sutures to attach the adjacent graft layers, (2) additional resorbable suture anchors and nonresorbable sutures in order to more robustly secure the graft to the glenoid, and (3) delaying postoperative motion and strengthening. However, despite these additional measures, our patient did not have an improved outcome.


title: Using occupancy grids for mobile robot perception and navigation

abstract: An approach to robot perception and world modeling that uses a probabilistic tesselated representation of spatial information called the occupancy grid is reviewed. The occupancy grid is a multidimensional random field that maintains stochastic estimates of the occupancy state of the cells in a spatial lattice. To construct a sensor-derived map of the robot's world, the cell state estimates are obtained by interpreting the incoming range readings using probabilistic sensor models. Bayesian estimation procedures allow the incremental updating of the occupancy grid, using readings taken from several sensors over multiple points of view. The use of occupancy grids from mapping and for navigation is examined. Operations on occupancy grids and extensions of the occupancy grid framework are briefly considered. >


title: Parkinson disease: Parkinson disease clinical subtypes and their implications

abstract: Despite the emergence of genetic and molecular approaches, disease categorization on the basis of clinical features remains an appealing approach, especially where the molecular mechanisms are poorly understood. A new study supports distinct clinical subtypes of Parkinson disease that could have implications for understanding the etiology of this condition.


title: Understanding the determinants of mobile payment continuance usage

abstract: Purpose – The purpose of this paper is to identify the factors affecting continuance usage of mobile payment. Design/methodology/approach – Based on the valid responses collected from a survey questionnaire, structural equation modeling (SEM) technology was employed to examine the research model. Findings – The results indicated that performance expectancy, trust in mobile payment and flow affect continuance usage. Among them, flow has a relatively larger effect. In addition, system quality has strong effects on performance expectancy and flow. Research limitations/implications – This research is conducted in China, where mobile internet is still in its early stage. Thus, the results need to be generalized to other countries that have developed mobile internet. Originality/value – Extant research has mainly focussed on the effects of instrumental beliefs such as perceived usefulness on mobile payment user behavior, and has seldom considered the effect of flow experience. This research tries to fill the gap.


title: Programming‐by‐demonstration in the coworker scenario for SMEs

abstract: Purpose – The purpose of this paper is to report a collection of developments that enable users to program industrial robots using speech, several device interfaces, force control and code generation techniques.Design/methodology/approach – The reported system is explained in detail and a few practical examples are given that demonstrate its usefulness for small to medium‐sized enterprises (SMEs), where robots and humans need to cooperate to achieve a common goal (coworker scenario). The paper also explores the user interface software adapted for use by non‐experts.Findings – The programming‐by‐demonstration (PbD) system presented proved to be very efficient with the task of programming entirely new features to an industrial robotic system. The system uses a speech interface for user command, and a force‐controlled guiding system for teaching the robot the details about the task being programmed. With only a small set of implemented robot instructions it was fairly easy to teach the robot system a new tas...


title: Speed sensorless control of an interior PM synchronous motor

abstract: This paper presents an adaptive observer for speed sensorless field-oriented control of interior permanent magnet (IPM) synchronous motors. The observer detects the rotor magnet flux components in the two-phase stationary reference frame using the motor electrical equations. The motor speed is identified by a model reference adaptive scheme using an additional equation obtained by a Lyapunov function. The analytical development of the observer is fully explained. The convergence of the estimates at low speeds and standstill is achieved through the assistance of a high frequency signal injection technique. Realistic simulations accounting for the inaccuracy of an actual digital signal processor (DSP) implementation are presented and discussed.


title: Dual-camera design for coded aperture snapshot spectral imaging

abstract: Coded aperture snapshot spectral imaging (CASSI) provides an efficient mechanism for recovering 3D spectral data from a single 2D measurement. However, since the reconstruction problem is severely underdetermined, the quality of recovered spectral data is usually limited. In this paper we propose a novel dual-camera design to improve the performance of CASSI while maintaining its snapshot advantage. Specifically, a beam splitter is placed in front of the objective lens of CASSI, which allows the same scene to be simultaneously captured by a grayscale camera. This uncoded grayscale measurement, in conjunction with the coded CASSI measurement, greatly eases the reconstruction problem and yields high-quality 3D spectral data. Both simulation and experimental results demonstrate the effectiveness of the proposed method.


title: Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods

abstract: Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Different than others, in this paper, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of deep learning methods are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.


title: Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis

abstract: This paper analyzes the classification of hyperspectral remote sensing images with linear discriminant analysis (LDA) in the presence of a small ratio between the number of training samples and the number of spectral features. In these particular ill-posed problems, a reliable LDA requires one to introduce regularization for problem solving. Nonetheless, in such a challenging scenario, the resulting regularized LDA (RLDA) is highly sensitive to the tuning of the regularization parameter. In this context, we introduce in the remote sensing community an efficient version of the RLDA recently presented by Ye to cope with critical ill-posed problems. In addition, several LDA-based classifiers (i.e., penalized LDA, orthogonal LDA, and uncorrelated LDA) are compared theoretically and experimentally with the standard LDA and the RLDA. Method differences are highlighted through toy examples and are exhaustively tested on several ill-posed problems related to the classification of hyperspectral remote sensing images. Experimental results confirm the effectiveness of the presented RLDA technique and point out the main properties of other analyzed LDA techniques in critical ill-posed hyperspectral image classification problems.


title: A four-channel bi-directional CMOS core chip for X-band phased array T/R modules

abstract: This paper presents a four-channel bi-directional core chip in 0.13 um CMOS for X-band phased array Transmit/Receive (T/R) module. Each channel consists of a 5-bit step attenuator, a 6-bit phase shifter, bi-directional gain blocks (BDGB), and a bi-directional amplifier (BDA). Additional circuits such as low drop out (LDO) regulator, bias circuits with band-gap reference (BGR), and serial to parallel interface (SPI) are integrated for stable biasing and ease of interface. The chip size is 6.9 × 1.6 mm2 including pads which corresponds to 2.8 mm2 per channel. The phase and attenuation coverage is 360° with the LSB of 5.625°, and 31dB with the LSB of 1dB, respectively. The RMS phase error is better than 2.3°, and the RMS attenuation error is better than 0.25 dB at 9–10 GHz. The Tx mode reference-state gain in each channel is 11.3–12.2 dB including the 4-way power combiner insertion losses ideally 6 dB, and the Rx mode gain is 8.6–9.5 dB at 9–10 GHz. The output P1dB in Tx mode is > 11 dBm at 9–10 GHz. To the best of authors' knowledge, this is the smallest size per channel X-band core chip in CMOS technology with bi-directional operation and competitive RF performance to-date.


title: "It's Definitely Been a Journey": A Qualitative Study on How Women with Eating Disorders Use Weight Loss Apps

abstract: Technology is often viewed as either positive or negative. On one hand, in HCI, weight loss apps are usually seen as a positive influence on users. From the sociocultural perspective, on the other hand, media and technology negatively impact body satisfaction and contribute to eating disorders; however, these studies fail to include weight loss apps. While these apps can be beneficial to users, they can also have negative effects on users with eating disorder behaviors. Yet few research studies have looked at weight loss apps in relation to eating disorders. In order to fill this gap, we conducted interviews with 16 women with a history of eating disorders who use(d) weight loss apps. While our findings suggest these apps can contribute to and exacerbate eating disorder behaviors, they also reveal a more complex picture of app usage. Women's use and perceptions of weight loss apps shift as they experience life and move to and from stages of change. This research troubles the binary view of technology and emphasizes the importance of looking at technology use as a dynamic process. Our study contributes to our understanding of weight loss app design.