zeta-alpha-ai/NanoSCIDOCS huggingface dataset:
EMPIRICAL models of business investment rely generally on the assumption of a "representative firm" that responds to prices set in centralized securities markets. Indeed, if all firms have equal access to capital markets, firms' responses to changes in the cost of capital or tax-based investment incentives differ only because of differences in investment demand. A firm's financial structure is irrelevant to investment because external funds provide a perfect substitute for internal capital. In general, with perfect capital markets, a firm's investment decisions are independent of its financial condition. An alternative research agenda, however, has been based on the view that internal and external capital are not perfect substitutes. According to this view, investment may depend on financial factors, such as the availability of internal finance, access to new debt or equity finance, or the functioning of particular credit markets. For example, a firm's internal cash flow may affect investment spending because of a "financ-
The Internet and World Wide Web have brought us into a world of endless possibiliti es: interactive Web sites to experience, music to li sten to, conversations to participate in, and every conceivable consumer item to order. But this world also is one of endless choice: how can we select from a huge universe of items of widely varying quality? Computational recommender systems have emerged to address this issue. They enable people to share their opinions and benefit from each other’s experience. We present a framework for understanding recommender systems and survey a number of distinct approaches in terms of this framework. We also suggest two main research challenges: (1) helping people form communities of interest while respecting personal privacy, and (2) developing algorithms that combine multiple types of information to compute recommendations.
Continuous electrowetting (CEW) is demonstrated to be an effective actuation mechanism for reconfigurable radio frequency (RF) devices that use non-toxic liquid-metal tuning elements. Previous research has shown CEW is an efficient means of electrically inducing motion in a liquid-metal slug, but precise control of the slug's position within fluidic channels has not been demonstrated. Here, the precise positioning of liquid-metal slugs is achieved using CEW actuation in conjunction with channels designed to minimize the liquid-metal surface energy at discrete locations. This approach leverages the high surface tension of liquid metal to control its resting position with submillimeter accuracy. The CEW actuation and fluidic channel design were optimized to create reconfigurable RF devices. In addition, solutions for the reliable actuation of a gallium-based, non-toxic liquid-metal alloy (Galinstan) are presented that mitigate the tendency of the alloy to form a surface oxide layer capable of wetting to the channel walls, inhibiting motion. A reconfigurable slot antenna utilizing these techniques to achieve a 15.2% tunable frequency bandwidth is demonstrated.
References [1] Wang, P., Shen, C., van den Hengel, A., A fast semidefinite approach to solving binary quadratic problems. CVPR 2013. [2] Candes, E.J., Li, X., Soltanolkotabi, M., Phase retrieval via wirtinger flow: Theory and algorithms. IEEE Transaction on Information Theory, 2015. [3] Zheng, Q., Lafferty, J., A convergent gradient descent algorithm for rank minimization and semidefinite programming from random linear measurements. NIPS 2015. [4] Netrapalli, P., Jain, P., Sanghavi, S., Phase retrieval using alternating minimization. NIPS 2013. Many important problems requires solving for low rank SDPs with PSD constraint matrices.
In this work we describe a Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max and min pooling, two fully connected layers and an output node. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating image quality. This approach achieves state of the art performance on the LIVE dataset and shows excellent generalization ability in cross dataset experiments. Further experiments on images with local distortions demonstrate the local quality estimation ability of our CNN, which is rarely reported in previous literature.
Fingerprint matching is affected by the nonlinear distortion introduced in fingerprint impressions during the image acquisition process. This nonlinear deformation causes fingerprint features such as minutiae points and ridge curves to be distorted in a complex manner. In this paper we develop an average deformation model for a fingerprint impression (baseline impression) by observing its relative distortion with respect to several other impressions of the same finger. The deformation is computed using a Thin Plate Spline (TPS) model that relies on ridge curve correspondences between image pairs. The estimated average deformation is used to distort the minutiae template of the baseline impression prior to matching. An index of deformation has been proposed to select the average deformation model with the least variability corresponding to a finger. Preliminary results indicate that the average deformation model can improve the matching performance of a fingerprint matcher.
Nowadays smartphones are ubiquitous in various aspects of our lives. The processing power, communication bandwidth, and the memory capacity of these devices have surged considerably in recent years. Besides, the variety of sensor types, such as accelerometer, gyroscope, humidity sensor, and bio-sensors, which are embedded in these devices, opens a new horizon in self-monitoring of physical daily activities. One of the primary steps for any research in the area of detecting daily life activities is to test a detection method on benchmark datasets. Most of the early datasets limited their work to collecting only a single type of sensor data such as accelerometer data. While some others do not consider age, weight, and gender of the subjects who have participated in collecting their activity data. Finally, part of the previous works collected data without considering the smartphone's position. In this paper, we introduce a new dataset, called Position-Aware Multi-Sensor (PAMS). The dataset contains both accelerometer and gyroscope data. The gyroscope data boosts the accuracy of activity recognition methods as well as enabling them to detect a wider range of activities. We also take the user information into account. Based on the biometric attributes of the participants, a separate learned model is generated to analyze their activities. We concentrate on several major activities, including sitting, standing, walking, running, ascending/descending stairs, and cycling. To evaluate the dataset, we use various classifiers, and the outputs are compared to the WISDM. The results show that using aforementioned classifiers, the average precision for all activities is above 88.5%. Besides, we measure the CPU, memory, and bandwidth usage of the application collecting data on the smartphone.
The emerging discipline of algorithm engineering has primarily focussed on transforming pencil-and-paper sequential algorithms into robust, efficient, well tested, and easily used implementations. As parallel computing becomes ubiquitous, we need to extend algorithm engineering techniques to parallel computation. Such an extension adds significant complications. After a short review of algorithm engineering achievements for sequential computing, we review the various complications caused by parallel computing, present some examples of successful efforts, and give a personal view of possible future research.
Article history: Received 1 August 2007 Received in revised form 22 May 2009 Accepted 26 May 2009
The skyline of a d-dimensional dataset contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in the database community, especially for progressive methods that can quickly return the initial results without reading the entire database. All the existing algorithms, however, have some serious shortcomings which limit their applicability in practice. In this article we develop branch-and-bound skyline (BBS), an algorithm based on nearest-neighbor search, which is I/O optimal, that is, it performs a single access only to those nodes that may contain skyline points. BBS is simple to implement and supports all types of progressive processing (e.g., user preferences, arbitrary dimensionality, etc). Furthermore, we propose several interesting variations of skyline computation, and show how BBS can be applied for their efficient processing.
Fog Computing (FC) has extended the services of cloud computing to the edge of the network. It inherits some of the characteristics from cloud computing but FC also have some distinguished features such as geo-distribution, location awareness and low latency. Along with the inherited characteristics, it also inherits the issues and problems of cloud computing like energy efficiency, resource management and security issues. This paper presents the critical analysis of the fog architecture with respect to security. The state of the art work done since 2012 is critical analyzed on the bases of security techniques and security threats. We grouped the existing security techniques on the bases of security goals achieved by each. It will provide a clear and comprehensive distinction between the security areas explored and those, which still need researchers' attention.
Learning from expert demonstrations has received a lot of attention in artificial intelligence and machine learning. The goal is to infer the underlying reward function that an agent is optimizing given a set of observations of the agent’s behavior over time in a variety of circumstances, the system state trajectories, and a plant model specifying the evolution of the system state for different agent’s actions. The system is often modeled as a Markov decision process (Puterman 2014), that is, the next state depends only on the current state and agent’s action, and the the agent’s choice of action depends only on the current state. While the former is a Markovian assumption on the evolution of system state, the later assumes that the target reward function is itself Markovian. In this work, we explore learning a class of non-Markovian reward functions, known in the formal methods literature as specifications. These specifications offer better composition, transferability, and interpretability. We then show that inferring the specification can be done efficiently without unrolling the transition system. We demonstrate on a 2-d grid world example.
Simulators have played a critical role in robotics research as tools for quick and efficient testing of new concepts, strategies, and algorithms. To date, most simulators have been restricted to 2D worlds, and few have matured to the point where they are both highly capable and easily adaptable. Gazebo is designed to fill this niche by creating a 3D dynamic multi-robot environment capable of recreating the complex worlds that would be encountered by the next generation of mobile robots. Its open source status, fine grained control, and high fidelity place Gazebo in a unique position to become more than just a stepping stone between the drawing board and real hardware: data visualization, simulation of remote environments, and even reverse engineering of blackbox systems are all possible applications. Gazebo is developed in cooperation with the Player and Stage projects (Gerkey, B. P., et al., July 2003), (Gerkey, B. P., et al., May 2001), (Vaughan, R. T., et al., Oct. 2003), and is available from http://playerstage.sourceforge.net/gazebo/ gazebo.html.
Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques for clustering when the dataset has either (1) a limited number of clusters, (2) a low feature dimensionality, or (3) a small number of data points. However, there has been much less work on methods of efficiently clustering datasets that are large in all three ways at once—for example, having millions of data points that exist in many thousands of dimensions representing many thousands of clusters. We present a new technique for clustering these large, highdimensional datasets. The key idea involves using a cheap, approximate distance measure to efficiently divide the data into overlapping subsets we call canopies. Then clustering is performed by measuring exact distances only between points that occur in a common canopy. Using canopies, large clustering problems that were formerly impossible become practical. Under reasonable assumptions about the cheap distance metric, this reduction in computational cost comes without any loss in clustering accuracy. Canopies can be applied to many domains and used with a variety of clustering approaches, including Greedy Agglomerative Clustering, K-means and Expectation-Maximization. We present experimental results on grouping bibliographic citations from the reference sections of research papers. Here the canopy approach reduces computation time over a traditional clustering approach by more than an order of magnitude and decreases error in comparison to a previously used algorithm by 25%.
Graph mining and management has become a popular area of research in recent years because of its numerous applications in a wide variety of practical fields, including computational biology, software bug localization and computer networking. Different applications result in graphs of different sizes and complexities. Correspondingly, the applications have different requirements for the underlying mining algorithms. In this chapter, we will provide a survey of different kinds of graph mining and management algorithms. We will also discuss a number of applications, which are dependent upon graph representations. We will discuss how the different graph mining algorithms can be adapted for different applications. Finally, we will discuss important avenues of future research
This paper presents a high step-down tranformerless single-stage single-switch ac/dc converter suitable for universal line applications (90-270 Vrms) . The topology integrates a buck-type power-factor correction (PFC) cell with a buck-boost dc/dc cell and part of the input power is coupled to the output directly after the first power processing. With this direct power transfer feature and sharing capacitor voltages, the converter is able to achieve efficient power conversion, high power factor, low voltage stress on intermediate bus (less than 130 V) and low output voltage without a high step-down transformer. The absence of transformer reduces the component counts and cost of the converter. Unlike most of the boost-type PFC cell, the main switch of the proposed converter only handles the peak inductor current of dc/dc cell rather than the superposition of both inductor currents. Detailed analysis and design procedures of the proposed circuit are given and verified by experimental results.
The large scale of Q&A archives accumulated in community based question answering (CQA) servivces are important information and knowledge resource on the web. Question and answer matching task has been attached much importance to for its ability to reuse knowledge stored in these systems: it can be useful in enhancing user experience with recurrent questions. In this paper, a Word Embedding based Correlation (WEC) model is proposed by integrating advantages of both the translation model and word embedding. Given a random pair of words, WEC can score their co-occurrence probability in Q&A pairs, while it can also leverage the continuity and smoothness of continuous space word representation to deal with new pairs of words that are rare in the training parallel text. An experimental study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new method’s promising
Face representation is a crucial step of face recognition systems. An optimal face representation should be discriminative, robust, compact, and very easyto-implement. While numerous hand-crafted and learning-based representations have been proposed, considerable room for improvement is still present. In this paper, we present a very easy-to-implement deep learning framework for face representation. Our method bases on a new structure of deep network (called Pyramid CNN). The proposed Pyramid CNN adopts a greedy-filter-and-down-sample operation, which enables the training procedure to be very fast and computationefficient. In addition, the structure of Pyramid CNN can naturally incorporate feature sharing across multi-scale face representations, increasing the discriminative ability of resulting representation. Our basic network is capable of achieving high recognition accuracy (85.8% on LFW benchmark) with only 8 dimension representation. When extended to feature-sharing Pyramid CNN, our system achieves the state-of-the-art performance (97.3%) on LFW benchmark. We also introduce a new benchmark of realistic face images on social network and validate our proposed representation has a good ability of generalization.
Recent years have witnessed significant progress in example-based texture synthesis algorithms. Given an example texture, these methods produce a larger texture that is tailored to the user’s needs. In this state-of-the-art report, we aim to achieve three goals: (1) provide a tutorial that is easy to follow for readers who are not already familiar with the subject, (2) make a comprehensive survey and comparisons of different methods, and (3) sketch a vision for future work that can help motivate and guide readers that are interested in texture synthesis research. We cover fundamental algorithms as well as extensions and applications of texture synthesis.
Fundamental movement competency is essential for participation in physical activity and for mitigating the risk of injury, which are both key elements of health throughout life. The squat movement pattern is arguably one of the most primal and critical fundamental movements necessary to improve sport performance, to reduce injury risk and to support lifelong physical activity. Based on current evidence, this first (1 of 2) report deconstructs the technical performance of the back squat as a foundation training exercise and presents a novel dynamic screening tool that incorporates identification techniques for functional deficits that limit squat performance and injury resilience. The follow-up report will outline targeted corrective methodology for each of the functional deficits presented in the assessment tool.
Semantic analysis of multimodal video aims to index segments of interest at a conceptual level. In reaching this goal, it requires an analysis of several information streams. At some point in the analysis these streams need to be fused. In this paper, we consider two classes of fusion schemes, namely early fusion and late fusion. The former fuses modalities in feature space, the latter fuses modalities in semantic space. We show by experiment on 184 hours of broadcast video data and for 20 semantic concepts, that late fusion tends to give slightly better performance for most concepts. However, for those concepts where early fusion performs better the difference is more significant.
This paper proposes an interleaved zero voltage transition (ZVT) PWM buck converter. The proposed converter consists of two identical buck converter modules and an auxiliary circuit. The auxiliary circuit provides zero voltage switching condition for the main switches. Also, zero current switching condition for the auxiliary switch and diodes is achieved. The interleaved buck converter with the proposed auxiliary switch is analyzed. The simulation results of a 300W ZVT interleaved buck converter operating at 100 KHz are presented to justify the validity of theoretical analysis.
This paper asks how complexity theory informs the role of leadership in organizations. Complexity theory is a science of complexly interacting systems; it explores the nature of interaction and adaptation in such systems and how they influence such things as emergence, innovation, and fitness. We argue that complexity theory focuses leadership efforts on behaviors that enable organizational effectiveness, as opposed to determining or guiding effectiveness. Complexity science broadens conceptualizations of leadership from perspectives that are heavily invested in psychology and social psychology (e.g., human relations models) to include processes for managing dynamic systems and interconnectivity. We develop a definition of organizational complexity and apply it to leadership science, discuss strategies for enabling complexity and effectiveness, and delve into the relationship between complexity theory and other currently important leadership theories. The paper concludes with a discussion of possible implications for research strategies in the social
Theoretical performance limits of twophase switched-capacitor (SC) dc-dc converters are discussed in this paper. For a given number of capacitors k, the complete set of attainable dc conversion ratios is found. The maximum step-up or stepdown ratio is given by the k t h Fibonacci number, while the bound on the number of switches required in any SC circuit is 3k 2. Practical implications, illustrated by several SC converter examples, include savings in the number of components required for a given application, and the ability to construct SC converters that can maintain the output voltage regulation and high conversion efficiency over a wide range of input voltage variations. Limits found for the output resistance and efflciency can be used for selection and comparison of SC converters.
The paper describes switched-capacitor dc-dc converters (charge pumps) suitable for on-chip, low-power applications. The proposed configurations are based on connecting two identical but opposite-phase SC converters in parallel, thus eliminating the need for separate bootstrap gate drivers. We focus on emerging very low-power VLSI applications such as batterypowered or self-powered signal processors where high power conversion efficiency is important and where power levels are in the milliwatt range. Conduction and switching losses are considered to allow design optimization in terms of switching frequency and component sizes. Open-loop and closed-loop operation of an experimental, fully integrated, 10MHz voltage doubler is described. The doubler has 2V or 3V input and generates 3.3V or 5V output at up to 5mW load. The converter circuit fabricated in a standard 1.2μ CMOS technology takes 0.7mm of the chip area.
A compact circularly polarized (CP) co-designed filtering antenna is reported. The device is based on a patch radiator seamlessly integrated with a bandpass filter composed of coupled stripline open-loop resonators, which are designed together as a system. In the proposed design, the patch functions simultaneously as the radiator and the last stage resonator of the filter, resulting in a low-profile integrated radiating and filtering module with a small overall form factor of 0.53λ0 × 0.53λ0 × 0.07λ0. It is shown that the filtering circuit not only ensures frequency selectivity but also provides impedance matching functionality, which serves to broaden both the impedance and axial ratio bandwidths. The designed filtering antenna was fabricated and measured, experimentally achieving an S11 <; -13.5 dB, an axial ratio of less than 3 dB and a gain higher than 5.2 dBi over a bandwidth from 3.77 to 4.26 GHz, i.e., around 12.2%, which makes it an excellent candidate for integration into a variety of wireless systems. A linearly polarized version of the integrated filtering antenna was also demonstrated. In addition, further full-wave simulations and experiments were carried out to verify that the designed CP filtering antenna maintains its properties even when mounted on different positions of the human body with various body gestures. The stable impedance and radiation properties also make it a suitable candidate as a wearable antenna for off-body wireless communications.
Millimeter-wave MIMO systems are one of the candidate schemes for 5G wireless standardization efforts. In this context, the main contributions of this article are three-fold. First, we describe parallel sets of measurements at identical transmit-receive location pairs with 2.9, 29 and 61 GHz carrier frequencies in indoor office, shopping mall, and outdoor settings. These measurements provide insights on propagation, blockage and material penetration losses, and the key elements necessary in system design to make mm-Wave systems viable in practice. Second, one of these elements is hybrid beamforming necessary for better link margins by reaping the array gain with large antenna dimensions. From the class of fully-flexible hybrid beamformers, we describe a robust class of directional beamformers toward meeting the high data-rate requirements of mm-Wave systems. Third, leveraging these design insights, we then describe an experimental prototype system at 28 GHz that realizes high data rates on both the downlink and uplink and robustly maintains these rates in outdoor and indoor mobility scenarios. In addition to maintaining large signal constellation sizes in spite of radio frequency challenges, this prototype leverages the directional nature of the mm-Wave channel to perform seamless beam switching and handover across mm-Wave base stations, thereby overcoming the path losses in non-line-of-sight links and blockages encountered at mm-Wave frequencies.
The interworking of the LTE system and WLAN technologies has drawn much attention lately, due to the growing demands for various multimedia services and large data traffic in hotspot areas. Existing research studies have mostly investigated the coupling architectures for these two wireless communication standards at the network layer. However, in the current architectures, many important coordination functions and joint optimizations cannot be accomplished efficiently. To tackle this problem, a new CBS solution is proposed, which integrates different RATs at layer 2 in the true sense of convergence. We design a unified protocol stack that includes all the original functions of both LTE and WLAN systems. Then we propose a convergence architecture, the RMC sublayer, for joint management of these two RATs. The proposed CBS solution can support seamless offloading through soft handover, guaranteed QoS, forwarding management by a single IP address, and customized bandwidth aggregation service. Finally, our simulation and initial experiment results demonstrate the feasibility and efficiency of the CBS solution in future mobile converged networks.
Equivalent circuits for piezoelectric devices and ultrasonic transmission media can be used to cosimulate electronics and ultrasound parts in simulators originally intended for electronics. To achieve efficient system- level optimization, it is important to simulate correct, absolute amplitude of the ultrasound signal in the system, as this determines the requirements on the electronics regarding dynamic range, circuit noise, and power consumption. This paper presents methods to achieve correct, absolute amplitude of an ultrasound signal in a simulation of a pulse-echo system using equivalent circuits. This is achieved by taking into consideration loss due to diffraction and the effect of the cable that connects the electronics and the piezoelectric transducer. The conductive loss in the transmission line that models the propagation media of the ultrasound pulse is used to model the loss due to diffraction. Results show that the simulated amplitude of the echo follows measured values well in both near and far fields, with an offset of about 10%. The use of a coaxial cable introduces inductance and capacitance that affect the amplitude of a received echo. Amplitude variations of 60% were observed when the cable length was varied between 0.07 m and 2.3 m, with simulations predicting similar variations. The high precision in the achieved results show that electronic design and system optimization can rely on system simulations alone. This will simplify the development of integrated electronics aimed at ultrasound systems.
A 9-year-old girl developed hidradenitis suppurativa 3 months after the first signs of adrenarche. Such a close temporal relationship is consistent with the hypothesis that the disease is androgen dependent. Less than 2% of patients have onset of the disease before the age of 11 years. The exceptionally early age of onset in our patient may be partly explained by the fact that she had an early puberty.
Hidradenitis suppurativa (HS) is a chronic suppurative scarring disease of apocrine sweat gland-bearing skin in the axillary, anogenital, and, rarely, the breast and scalp regions. Females are more commonly affected than males and it is usually seen at puberty or later. We report two girls with prepubertal hidradenitis suppurativa whose initial presentation predated any signs of puberty. This early onset is very rare and its etiology remains unknown. Severe disease can be seen in prepubertal children and surgical intervention is effective in these cases.
IMPORTANCE
Hidradenitis suppurativa (HS) is a chronic debilitating cutaneous disease for which there is no universally effective treatment. Patients typically present at puberty with tender subcutaneous nodules that can progress to dermal abscess formation. Antiandrogens have been used in the treatment of HS, and studies have primarily focused on adult patients.


OBSERVATIONS
We present a case series of 3 pediatric patients with HS who were successfully treated with oral finasteride, resulting in decreased frequency and severity of disease flares with no significant adverse effects.


CONCLUSIONS AND RELEVANCE
Finasteride is a therapeutic option that provides benefit for pediatric patients with HS. Further prospective data and randomized controlled studies will provide helpful information in the management of this disease.
Hidradenitis suppurativa (HS) is a burdensome disease and has the potential to affect the life course of patients. It is a rare disease in children, and the recorded literature is correspondingly scarce. This article reviews the therapeutic options for HS in children and adolescents, and highlights particular differences or challenges with treating patients in this age group compared with adults. The work-up of paediatric patients with HS should include considerations of possible endocrine co-morbidities and obesity. Medical therapy of lesions may include topical clindamycin. Systemic therapy may include analgesics, clindamycin and rifampicin, finasteride, corticosteroids or tumour necrosis factor alpha (TNFα) blockers. Superinfections should be appropriately treated. Scarring lesions generally require surgery.
BACKGROUND
Although skin diseases are often immediately visible to both patients and society, the morbidity they cause is only poorly defined. It has been suggested that quality-of-life measures may be a relevant surrogate measure of skin disease. Hidradenitis suppurativa (HS) leads to painful eruptions and malodorous discharge and is assumed to cause a significant degree of morbidity. The resulting impairment of life quality has not previously been quantitatively assessed, although such an assessment may form a pertinent measure of disease severity in HS.


OBJECTIVES
To measure the impairment of life quality in patients with HS.


METHODS
In total, 160 patients suffering from HS were approached. The following data were gathered: quality-of-life data (Dermatology Life Quality Index, DLQI questionnaire), basic demographic data, age at onset of the condition and the average number of painful lesions per month.


RESULTS
One hundred and fourteen patients participated in the study. The mean +/- SD age of the patients was 40.9 +/- 11.7 years, the mean +/- SD age at onset 21.8 +/- 9.9 years and the mean +/- SD duration of the disease 18.8 +/- 11.4 years. Patients had a mean +/- SD DLQI score of 8.9 +/- 8.3 points. The highest mean score out of the 10 DLQI questions was recorded for question 1, which measures the level of pain, soreness, stinging or itching (mean 1.55 points, median 2 points). Patients experienced a mean of 5.1 lesions per month.


CONCLUSIONS
HS causes a high degree of morbidity, with the highest scores obtained for the level of pain caused by the disease. The mean DLQI score for HS was higher than for previously studied skin diseases, and correlated with disease intensity as expressed by lesions per month. This suggests that the DLQI may be a relevant outcome measure in future therapeutic trials in HS.
Mobile location-based services are thriving, providing an unprecedented opportunity to collect fine grained spatio-temporal data about the places users visit. This multi-dimensional source of data offers new possibilities to tackle established research problems on human mobility, but it also opens avenues for the development of novel mobile applications and services. In this work we study the problem of predicting the next venue a mobile user will visit, by exploring the predictive power offered by different facets of user behavior. We first analyze about 35 million check-ins made by about 1 million Foursquare users in over 5 million venues across the globe, spanning a period of five months. We then propose a set of features that aim to capture the factors that may drive users' movements. Our features exploit information on transitions between types of places, mobility flows between venues, and spatio-temporal characteristics of user check-in patterns. We further extend our study combining all individual features in two supervised learning models, based on linear regression and M5 model trees, resulting in a higher overall prediction accuracy. We find that the supervised methodology based on the combination of multiple features offers the highest levels of prediction accuracy: M5 model trees are able to rank in the top fifty venues one in two user check-ins, amongst thousands of candidate items in the prediction list.
The growing volume and variety of data presents both opportunities and challenges for visual analytics. Addressing these challenges is needed for big data to provide valuable insights and novel solutions for business, security, social media, and healthcare. In the case of temporal event sequence analytics it is the number of events in the data and variety of temporal sequence patterns that challenges users of visual analytic tools. This paper describes 15 strategies for sharpening analytic focus that analysts can use to reduce the data volume and pattern variety. Four groups of strategies are proposed: (1) extraction strategies, (2) temporal folding, (3) pattern simplification strategies, and (4) iterative strategies. For each strategy, we provide examples of the use and impact of this strategy on volume and/or variety. Examples are selected from 20 case studies gathered from either our own work, the literature, or based on email interviews with individuals who conducted the analyses and developers who observed analysts using the tools. Finally, we discuss how these strategies might be combined and report on the feedback from 10 senior event sequence analysts.
With a stringent input-to-output conversion ratio (CR) of 20 (24V input and 1.2V output) for a DC-DC converter, two stage cascaded architectures are easy to implement, but suffer from poor efficiency and doubled number of power components. This paper presents a single-stage solution with a proposed Adaptive ON-OFF Time (AO2T) control. In steady state, the control works as an adaptive ON-time valley current mode control to accommodate the large CR. During load transient periods, both ON- and OFF-time are adaptively adjustable to instantaneous load change, in order to accomplish fast load transient response within one switching cycle. To facilitate the high speed current mode control, a sensorless current detection circuit is also proposed. Operating at 5MHz, the converter achieves a maximum efficiency of 89.8% at 700mA and an efficiency of 85% at 2A full load. During a load current slew rate of 1.8A/200ns, the undershoot/overshoot voltages at VO are 23mV and 37mV respectively.
This article presents an algorithm for inducing multiclass decision trees with multivariate tests at internal decision nodes. Each test is constructed by training a linear machine and eliminating variables in a controlled manner. Empirical results demonstrate that the algorithm builds small accurate trees across a variety of tasks.
We introduce a new descriptor for images which allows the construction of efficient and compact classifiers with good accuracy on object category recognition. The descriptor is the output of a large number of weakly trained object category classifiers on the image. The trained categories are selected from an ontology of visual concepts, but the intention is not to encode an explicit decomposition of the scene. Rather, we accept that existing object category classifiers often encode not the category per se but ancillary image characteristics; and that these ancillary characteristics can combine to represent visual classes unrelated to the constituent categories’ semantic meanings. The advantage of this descriptor is that it allows object-category queries to be made against image databases using efficient classifiers (efficient at test time) such as linear support vector machines, and allows these queries to be for novel categories. Even when the representation is reduced to 200 bytes per image, classification accuracy on object category recognition is comparable with the state of the art (36% versus 42%), but at orders of magnitude lower computational cost.
Most research to date has approached employee empowerment as an individual-level phenomenon. In this study we proposed a work-unit-level construct, empowerment climate, and tested a multiple-level model integrating macro and micro approaches to empowerment. Empowerment climate was shown to be empirically distinct from psychological empowerment and positively related to manager ratings of work-unit performance. A cross-level mediation analysis using hierarchical linear modeling showed that psychological empowerment mediated the relationships between empowerment climate and individual performance and job satisfaction.
Many hierarchical multi-label classification systems predict a real valued score for every (instance, class) couple, with a higher score reflecting more confidence that the instance belongs to that class. These classifiers leave the conversion of these scores to an actual label set to the user, who applies a cut-off value to the scores. The predictive performance of these classifiers is usually evaluated using threshold independent measures like precision-recall curves. However, several applications require actual label sets, and thus an automatic labelling strategy. In this article, we present and evaluate different alternatives to perform the actual labelling in hierarchical multi-label classification. We investigate the selection of both single and multiple thresholds. Despite the existence of multiple threshold selection strategies in non-hierarchical multi-label classification, they can not be applied directly to the hierarchical context. The proposed strategies are implemented within two main approaches: optimisation of a certain performance measure of interest (such as F-measure or hierarchical loss), and simulating training set properties (such as class distribution or label cardinality) in the predictions. We assess the performance of the proposed labelling schemes on 10 datasets from different application domains. Our results show that selecting multiple thresholds may result in ∗Corresponding author. Tel : +32(0)9 331 36 93 Fax : +32(0)9 221 76 73 Email addresses: Isaac.Triguero@irc.vib-UGent.be (Isaac Triguero), Celine.Vens@kuleuven-kulak.be (Celine Vens) Preprint submitted to Elsevier January 29, 2016 an efficient and effective solution for hierarchical multi-label problems.
Future computing environments will free the user from the constraints of the desktop. Applications for a mobile environment should take advantage of contextual information, such as position, to o er greater services to the user. In this paper, we present the Cyberguide project, in which we are building prototypes of a mobile context-aware tour guide. Knowledge of the user's current location, as well as a history of past locations, are used to provide more of the kind of services that we come to expect from a real tour guide. We describe the architecture and features of a variety of Cyberguide prototypes developed for indoor and outdoor use on a number of di erent hand-held platforms. We also discuss the general research issues that have emerged in our context-aware applications development in a mobile environment.
Corresponding Author: Michelle Lim Sern Mi Institute of Microengineering and Nanoelectronics (IMEN), UKM, 43600 Bangi, Selangor, Malaysia Email: mlsm_2002@yahoo.com Abstract: This paper reviews CMOS based charge pump topologies used within autonomous embedded micro-systems. These charge pump structures have evolved from its simplistic diode-tied, single-branches with major threshold drops to exponential type, dual-branches with sophisticated gate and substrate control for lower voltage operation. Published charge pumps are grouped based on architecture, operation principles and pump optimization techniques with their pros and cons compared and results contrasted. The various charge pump topologies and schemes used are considered based on pumping efficiency, power efficiency, charge transferability, circuit complexity, pumping capacitors, form factor and minimum supply voltages with an optimum load. This article concludes with an overview of suitable techniques and recommendations that will aid a designer in selecting the most suitable charge pump topology especially for low ambient micro energy harvesting applications.
This paper presents the findings of a steerable higher order mode (TE<inline-formula> <tex-math notation="LaTeX">$^{\mathrm {y}}_{1\delta 3}$ </tex-math></inline-formula>) dielectric resonator antenna with parasitic elements. The beam steering was successfully achieved by switching the termination capacitor on the parasitic element. In this light, all of the dielectric resonator antennas (DRAs) have the same dielectric permittivity similar to that of ten and excited by a <inline-formula> <tex-math notation="LaTeX">$50\Omega $ </tex-math></inline-formula> microstrip with a narrow aperture. The effect of the mutual coupling on the radiation pattern and the reflection coefficient, as well as the array factor, was investigated clearly using MATLAB version 2014b and ANSYS HFSS version 16. As the result, the antenna beam of the proposed DRA array managed to steer from −32° to +32° at 15 GHz. Furthermore, the measured antenna array showed the maximum gain of 9.25 dBi and the reflection coefficients which are less than −10 dB with the bandwidth more than 1.3 GHz, which is viewed as desirable for device-to-device communication in 5G Internet of Things applications.
This communication presents a 32 × 32 high-gain patch array antenna fed by the substrate integrated waveguide (SIW) structure at W-band. The array antenna consists of two layers to achieve a compact topology, which allows for mass production using a standard PCB fabrication process. The wideband feeding network is placed in the bottom layer while the radiating patches are on the top layer. This configuration also resolves the trade-off between gain and bandwidth of conventional SIW array antennas. Measured gain of the 32 × 32 antenna array is within the range 28.81-29.97 dBi in the working bandwidth of 91-97 GHz. Measured impedance bandwidth covers the same frequency band for . The cross-polarization of the antenna array is less than 40 dB at the beam direction. Good agreement between the simulated and measured results validates our design.
The most common way of constructing a hash function (e.g., SHA-1) is to iterate a compression function on the input message. The compression function is usually designed from scratch or made out of a block-cipher. In this paper, we introduce a new security notion for hash-functions, stronger than collision-resistance. Under this notion, the arbitrary length hash function H must behave as a random oracle when the fixed-length building block is viewed as an ideal primitive. This enables to eliminate all possible generic attacks against iterative hash-functions. In this paper, we show that the current design principle behind hash functions such as SHA-1 and MD5 — the (strengthened) Merkle-Damg̊ard transformation — does not satisfy this security notion. We provide several constructions that provably satisfy this notion; those new constructions introduce minimal changes to the plain Merkle-Damg̊ard construction and are easily implementable in practice. This paper is a modified version of a paper to appear at Crypto 2005.
As the development of mobile devices and applications, mobile privacy has become a very important issue. Current researches on mobile privacy mainly focus on potential leakages on a particular device. However, leakage of sensitive data on a mobile device not only violates the privacy of the phone (or data) owner, but also violates the privacy of many other people whose information are contained in the data directly or indirectly (they are called data involvers). To address such problems, we introduce a collaborative privacy management framework, which aims to provide fine-grained data privacy protection for both data owners and data involvers in a distributed manner. Based on individual privacy policies specified by each user, a collaborative privacy policy is generated and enforced on different devices automatically. As a proof-of-concept prototype, we implement the proposed framework on Android and demonstrate its applicability with two case studies.
To save resources for Internet of Things (IoT) devices, a proposed approach segments operands and corresponding basic arithmetic operations that can be carried out by approximate function units for almost all applications. The approach also increases the security of IoT devices by hiding information for IP watermarking, digital fingerprinting, and lightweight encryption.
doesn't need to be secure? Almost every softwarecontrolled system faces threats from potential adversaries, from Internet-aware client applications running on PCs, to complex telecommunications and power systems accessible over the Internet, to commodity software with copy protection mechanisms. Software engineers must be cognizant of these threats and engineer systems with credible defenses, while still delivering value to customers. In this paper, we present our perspectives on the research issues that arise in the interactions between software engineering and security.
The OpenStreetMap project is a knowledge collective that provides user-generated street maps. OSM follows the peer production model that created Wikipedia; its aim is to create a set of map data that's free to use, editable, and licensed under new copyright schemes. A considerable number of contributors edit the world map collaboratively using the OSM technical infrastructure, and a core group, estimated at approximately 40 volunteers, dedicate their time to creating and improving OSM's infrastructure, including maintaining the server, writing the core software that handles the transactions with the server, and creating cartographical outputs. There's also a growing community of software developers who develop software tools to make OSM data available for further use across different application domains, software platforms, and hardware devices. The OSM project's hub is the main OSM Web site.