title: A High-Quality Speech and Audio Codec With Less Than 10 ms Delay

abstract: With increasing quality requirements for multimedia communications, audio codecs must maintain both high quality and low delay. Typically, audio codecs offer either low delay or high quality, but rarely both. We propose a codec that simultaneously addresses both these requirements, with a delay of only 8.7 ms at 44.1 kHz. It uses gain-shape algebraic vector quantization in the frequency domain with time-domain pitch prediction. We demonstrate that the proposed codec operating at 48 kb/s and 64 kb/s out-performs both G.722.1C and MP3 and has quality comparable to AAC-LD, despite having less than one fourth of the algorithmic delay of these codecs.


nexpensive techniques to improve education : Applying cognitive psychology to nhance educational practice


Minorities , Media and Intercultural Dialogue : Opportunities and Challenges Expert Workshop Flensburg , Germany ; 24-25 January 2013


title: ECG-RNG: A Random Number Generator Based on ECG Signals and Suitable for Securing Wireless Sensor Networks

abstract: Wireless Sensor Networks (WSNs) are a promising technology with applications in many areas such as environment monitoring, agriculture, the military field or health-care, to name but a few. Unfortunately, the wireless connectivity of the sensors opens doors to many security threats, and therefore, cryptographic solutions must be included on-board these devices and preferably in their design phase. In this vein, Random Number Generators (RNGs) play a critical role in security solutions such as authentication protocols or key-generation algorithms. In this article is proposed an avant-garde proposal based on the cardiac signal generator we carry with us (our heart), which can be recorded with medical or even low-cost sensors with wireless connectivity. In particular, for the extraction of random bits, a multi-level decomposition has been performed by wavelet analysis. The proposal has been tested with one of the largest and most publicly available datasets of electrocardiogram signals (202 subjects and 24 h of recording time). Regarding the assessment, the proposed True Random Number Generator (TRNG) has been tested with the most demanding batteries of statistical tests (ENT, DIEHARDERand NIST), and this has been completed with a bias, distinctiveness and performance analysis. From the analysis conducted, it can be concluded that the output stream of our proposed TRNG behaves as a random variable and is suitable for securing WSNs.


title: Sarcasm Analysis Using Conversation Context

abstract: Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, the speaker’s sarcastic intent is not always apparent without additional context. Focusing on social media discussions, we investigate three issues: (1) does modeling conversation context help in sarcasm detection? (2) can we identify what part of conversation context triggered the sarcastic reply? and (3) given a sarcastic post that contains multiple sentences, can we identify the specific sentence that is sarcastic? To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the current turn. We show that LSTM networks with sentence-level attention on context and current turn, as well as the conditional LSTM network, outperform the LSTM model that reads only the current turn. As conversation context, we consider the prior turn, the succeeding turn, or both. Our computational models are tested on two types of social media platforms: Twitter and discussion forums. We discuss several differences between these data sets, ranging from their size to the nature of the gold-label annotations. To address the latter two issues, we present a qualitative analysis of the attention weights produced by the LSTM models (with attention) and discuss the results compared with human performance on the two tasks.


title: Temperature dependent Pspice model of silicon carbide power MOSFET

abstract: This paper provides a behavioral model in Pspice for a silicon carbide (SiC) power MOSFET rated at 1200 V / 30 A for a wide temperature range. The Pspice model was built using device parameters extracted through experiment. The static and dynamic behavior of the SiC power MOSFET is simulated and compared to the measured data to show the accuracy of the Pspice model. The temperature dependent behavior was simulated and analyzed. Also, the effect of the parasitics of the circuit on switching behavior was simulated and discussed.


title: Bioequivalence; Its History, Practice, and Future

abstract: The concept of BE has been adopted by the pharmaceutical industry and national regulatory authorities throughout the world for over 20 years. Because of this, thousands of generic drugs have been manufactured and marketed by the industry after regulatory approval. A lot of advances have been made during these years in developing various approaches to assess BE through research that would assure high quality interchangeable and affordable drugs. However, a lot remains to be done. There is a continuing attempt by national regulatory authorities, international public health organization, pharmaceutical, and basic scientists to understand and develop more efficient and scientifically valid approaches to assess bioequivalence of various dosage forms including some of the tough complex special dosage forms.


title: Low power parallel multiplier with column bypassing

abstract: Power management has become a great concern in VLSI design in recent years. In this paper, we present a low power parallel multiplier design, in which some columns in the multiplier array can be turned-off whenever their outputs are known. In this case, the columns are bypassed, and thus the switching power is saved. The advantage of this design is that it maintains the original array structure without introducing extra boundary cells, as did previous designs. Experimental results show that it saves 10% power for random inputs. Higher power reduction can be achieved if the operands contain more 0s than 1s. Compared with row-bypassing multipliers, this approach achieves higher power reduction with smaller area overhead.


title: BJR: A bipedal jumping robot using double-acting pneumatic cylinders and torsion springs

abstract: This paper presents a biped jumping robot "BJR", in which double-acting cylinders and torsion springs are used as the power units. BJR has two 3-DOF serial actuated legs, and each leg has a hip, a knee and an ankle joint. We conducted a dynamic simulation of jumping for BJR. Physical experiments were performed, in which the real robot jumped onto a chair. The experimental results show that the real jumping behaviours of BJR matches well with the simulation ones. BJR can jump as high as 0.5m, which is more than 50% of its body height. It is highly effective and feasible to utilize the collaborative role between double-acting cylinder and the torsion spring as the power element in the design of jumping robots.


title: Image Super-Resolution Based on Structure-Modulated Sparse Representation

abstract: Sparse representation has recently attracted enormous interests in the field of image restoration. The conventional sparsity-based methods enforce sparse coding on small image patches with certain constraints. However, they neglected the characteristics of image structures both within the same scale and across the different scales for the image sparse representation. This drawback limits the modeling capability of sparsity-based super-resolution methods, especially for the recovery of the observed low-resolution images. In this paper, we propose a joint super-resolution framework of structure-modulated sparse representations to improve the performance of sparsity-based image super-resolution. The proposed algorithm formulates the constrained optimization problem for high-resolution image recovery. The multistep magnification scheme with the ridge regression is first used to exploit the multiscale redundancy for the initial estimation of the high-resolution image. Then, the gradient histogram preservation is incorporated as a regularization term in sparse modeling of the image super-resolution problem. Finally, the numerical solution is provided to solve the super-resolution problem of model parameter estimation and sparse representation. Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed algorithm. Experimental results demonstrate that our proposed algorithm, which can recover more fine structures and details from an input low-resolution image, outperforms the state-of-the-art methods both subjectively and objectively in most cases.


title: The spread of low-credibility content by social bots

abstract: The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Little systematic, data-based evidence has been published to guide these efforts. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017. We find evidence that social bots played a disproportionate role in spreading articles from low-credibility sources. Bots amplify such content in the early spreading moments, before an article goes viral. They also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, resharing content posted by bots. Successful low-credibility sources are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.Online misinformation is a threat to a well-informed electorate and undermines democracy. Here, the authors analyse the spread of articles on Twitter, find that bots play a major role in the spread of low-credibility content and suggest control measures for limiting the spread of misinformation.


title: Steal my idea! Organizational adoption of user innovations from a user innovation community: A case study of Dell IdeaStorm

abstract: Open innovation models suggest that an organization's end users can be a potentially beneficial source for new ideas and innovations. However, incorporating end users raises some interesting challenges regarding how to balance the decision making power between a firm and the user community. Using Diffusion of Innovation Theory, we propose hypotheses based on the perceived attributes of end user ideas and end user promotion efforts. Our findings suggest that the decision to adopt a user innovation was based on the ability of the firm to understand the technical requirements and respond to community concerns regarding the innovation.


title: Optimizing Nutrition in Intensive Care Units: Empowering Critical Care Nurses to Be Effective Agents of Change

abstract: Observational studies have consistently revealed wide variation in nutritional practices across intensive care units and indicated that the provision of adequate nutrition to critically ill patients is suboptimal. To date, the potential role of critical care nurses in implementing nutritional guideline recommendations and improving nutritional therapy has received little consideration. Factors that influence nurses' nutritional practices include the lack of guidelines or conflicting evidence-based recommendations pertaining to nurses' practice, strategies for implementing guidelines that are not tailored to barriers nurses face when feeding patients, strategies to communicate best evidence that do not capitalize on nurses' preference for seeking information through social interaction, prioritization of nutrition in initial and continuing nursing education, and a lack of interdisciplinary team collaboration in the intensive care unit when decisions on how to feed patients are made. Future research and quality improvement strategies are required to correct these deficits and successfully empower nurses to become nutritional champions at the bedside. Using nurses as agents of change will help standardize nutritional practices and ensure that critically ill patients are optimally fed.


title: Achieving Fast and Lightweight SDN Updates with Segment Routing

abstract: In SDN, forwarding rules are frequently updated to adapt to network dynamics. During the procedure, path consistency needs to be preserved; otherwise, in-flight packets might meet with forwarding errors such as loops and black holes. Despite a large number of suggestions have been proposed, they take either a long duration or have high rule-space overheads, thus fail to be practical for large-scale high dynamic networks. In this paper, we propose FLUS, a Segment Routing (SR) based mechanism, to achieve fast and lightweight path updates. Basically, when a route needs a change, FLUS instantly employs SR to construct its desired new path by concatenating some fragments of the already existing paths. After the actual paths are established, FLUS then shifts incoming packets to them and disables the transitional ones. Such a design helps packets enjoy their new paths immediately without introducing rule-space overheads. This paper presents FLUS's segment allocation, path construction, and the corresponding optimal algorithms in detail. Our evaluation based on real and synthesized networks shows: FLUS can handle up to 92-100% updates using SR in real-time and save 72-88% rule overhead compared to prior methods.


title: Questions programmers ask during software evolution tasks

abstract: Though many tools are available to help programmers working on change tasks, and several studies have been conducted to understand how programmers comprehend systems, little is known about the specific kinds of questions programmers ask when evolving a code base. To fill this gap we conducted two qualitative studies of programmers performing change tasks to medium to large sized programs. One study involved newcomers working on assigned change tasks to a medium-sized code base. The other study involved industrial programmers working on their own change tasks on code with which they had experience. The focus of our analysis has been on what information a programmer needs to know about a code base while performing a change task and also on howthey go about discovering that information. Based on this analysis we catalog and categorize 44 different kinds of questions asked by our participants. We also describe important context for how those questions were answered by our participants, including their use of tools.


title: A rapid, non-invasive, PCR-based method for identification of sex of the endangered Old World vultures (white-backed and long-billed vultures) - : Implications for captive breeding programmes

abstract: The rapid and inexplicable decline of the Old World vulture populations across the Indian subcontinent is a cause of growing concern among ecologists and conservationists. Survival of these threatened birds may depend on breeding programmes where sex identification plays an important role. Here, a PCR-based method that amplifies a region of the chromo-helicase-DNA-binding gene was modified to identify the sex of individuals of two Gyps species. The results were further reconfirmed by amplification refractory mutation system. This study allowed for the rapid and safe assessment of sex and will facilitate all future efforts in the captive breeding of these birds.


title: A Process Semantics for BPMN

abstract: Business Process Modelling Notation (BPMN), developed by the Business Process Management Initiative (BPMI), intends to bridge the gap between business process design and implementation. However, the specification of the notation does not include a formal semantics. This paper shows how a subset of the BPMN can be given a process semantics in Communicating Sequential Processes. Such a semantics allows developers to formally analyse and compare BPMN diagrams. A simple example of a business process is included to demonstrate the application of the semantics; some theoretical results about the semantics are briefly discussed.


title: The Effect of a Classroom-Based Intensive Robotics and Programming Workshop on Sequencing Ability in Early Childhood

abstract: This paper examines the impact of programming robots on sequencing ability during a 1-week intensive robotics workshop at an early childhood STEM magnet school in the Harlem area of New York City. Children participated in computer programming activities using a developmentally appropriate tangible programming language CHERP, specifically designed to program a robot’s behaviors. The study assessed 27 participants’ sequencing skills before and after the programming and robotics curricular intervention using a picture-story sequencing task and compared those skills to a control group. Pre-test and post-test scores were compared using a paired sample t test. The group of children who participated in the 1-week robotics and programming workshop experienced significant increases in post-test compared to pre-test sequencing scores.


title: Deep Video Hashing

abstract: In this work, we propose a deep video hashing (DVH) method for scalable video search. Unlike most existing video hashing methods that first extract features for each single frame and then use conventional image hashing techniques, our DVH learns binary codes for the entire video with a deep learning framework so that both the temporal and discriminative information can be well exploited. Specifically, we fuse the temporal information across different frames within each video to learn the feature representation under two criteria: the distance between a feature pair obtained at the top layer is small if they are from the same class, and large if they are from different classes; and the quantization loss between the real-valued features and the binary codes is minimized. We exploit different deep architectures to utilize spatial-temporal information in different manners and compare them with single-frame-based deep models and state-of-the-art image hashing methods. Experimental results demonstrate the effectiveness of our proposed method.


title: Resting state EEG abnormalities in autism spectrum disorders

abstract: Autism spectrum disorders (ASD) are a group of complex and heterogeneous developmental disorders involving multiple neural system dysfunctions. In an effort to understand neurophysiological substrates, identify etiopathophysiologically distinct subgroups of patients, and track outcomes of novel treatments with translational biomarkers, EEG (electroencephalography) studies offer a promising research strategy in ASD. Resting-state EEG studies of ASD suggest a U-shaped profile of electrophysiological power alterations, with excessive power in low-frequency and high-frequency bands, abnormal functional connectivity, and enhanced power in the left hemisphere of the brain. In this review, we provide a summary of recent findings, discuss limitations in available research that may contribute to inconsistencies in the literature, and offer suggestions for future research in this area for advancing the understanding of ASD.


title: Three-Phase Dual-Buck Inverter With Unified Pulsewidth Modulation

abstract: This paper presents a new type of three-phase voltage source inverter (VSI), called three-phase dual-buck inverter. The proposed inverter does not need dead time, and thus avoids the shoot-through problems of traditional VSIs, and leads to greatly enhanced system reliability. Though it is still a hard-switching inverter, the topology allows the use of power MOSFETs as the active devices instead of IGBTs typically employed by traditional hard-switching VSIs. As a result, the inverter has the benefit of lower switching loss, and it can be designed at higher switching frequency to reduce current ripple and the size of passive components. A unified pulsewidth modulation (PWM) is introduced to reduce computational burden in real-time implementation. Different PWM methods were applied to a three-phase dual-buck inverter, including sinusoidal PWM (SPWM), space vector PWM (SVPWM) and discontinuous space vector PWM (DSVPWM). A 2.5 kW prototype of a three-phase dual-buck inverter and its control system has been designed and tested under different dc bus voltage and modulation index conditions to verify the feasibility of the circuit, the effectiveness of the controller, and to compare the features of different PWMs. Efficiency measurement of different PWMs has been conducted, and the inverter sees peak efficiency of 98.8% with DSVPWM.


title: Condensed Memory Networks for Clinical Diagnostic Inferencing

abstract: Diagnosis of a clinical condition is a challenging task, which often requires significant medical investigation. Previous work related to diagnostic inferencing problems mostly consider multivariate observational data (e.g. physiological signals, lab tests etc.). In contrast, we explore the problem using free-text medical notes recorded in an electronic health record (EHR). Complex tasks like these can benefit from structured knowledge bases, but those are not scalable. We instead exploit raw text from Wikipedia as a knowledge source. Memory networks have been demonstrated to be effective in tasks which require comprehension of free-form text. They use the final iteration of the learned representation to predict probable classes. We introduce condensed memory neural networks (C-MemNNs), a novel model with iterative condensation of memory representations that preserves the hierarchy of features in the memory. Experiments on the MIMIC-III dataset show that the proposed model outperforms other variants of memory networks to predict the most probable diagnoses given a complex clinical scenario.


title: The multiple nicotinamide nucleotide-binding subunits of bovine heart mitochondrial NADH:ubiquinone oxidoreductase (complex I)

abstract: Direct photoaffinity labeling of purified bovine heart NADH:ubiquinone oxidoreductase (complex I) with 32P-labeled NAD(H), NADP(H) and ADP has shown that five polypeptides become labeled, with molecular masses of 51, 42, 39, 30, and 18–20 kDa. The 51 and the 30-kDa polypeptides were labeled with either [32P]NAD(H), [32P]NADP(H) or [β-32P]ADP. The 42-kDa polypeptide was labeled with [32P]NAD(H) and to a small extent with [β-32P]ADP. It was not labeled with [32P]NADP(H). The 39-kDa polypeptide was labeled with [32P]NADPH and to a small extent with [β-32P]ADP. Our previous studies had shown that this subunit also binds NADP, but not NAD(H) [Yamaguchi, M., Belogrudov, G.I. & Hatefi, Y. (1998) J. Biol. Chem.273, 8094–8098]. The 18–20-kDa polypeptide was labeled only with [32P]NADPH. Among these polypeptides, the 51-kDa subunit is known to contain FMN and a [4Fe–4S] cluster, and is the NAD(P)H-binding subunit of the primary dehydrogenase domain of complex I. The possible roles of the other nucleotide-binding subunits of complex I have been discussed.


title: Leveraging User Reviews to Improve Accuracy for Mobile App Retrieval

abstract: Smartphones and tablets with their apps pervaded our everyday life, leading to a new demand for search tools to help users find the right apps to satisfy their immediate needs. While there are a few commercial mobile app search engines available, the new task of mobile app retrieval has not yet been rigorously studied. Indeed, there does not yet exist a test collection for quantitatively evaluating this new retrieval task. In this paper, we first study the effectiveness of the state-of-the-art retrieval models for the app retrieval task using a new app retrieval test data we created. We then propose and study a novel approach that generates a new representation for each app. Our key idea is to leverage user reviews to find out important features of apps and bridge vocabulary gap between app developers and users. Specifically, we jointly model app descriptions and user reviews using topic model in order to generate app representations while excluding noise in reviews. Experiment results indicate that the proposed approach is effective and outperforms the state-of-the-art retrieval models for app retrieval.


title: Control Algorithm for an Independent Motor-Drive Vehicle

abstract: A control algorithm is proposed for an independent motor-drive vehicle. A sliding-mode control is designed based on the slip ratio, yaw rate, and lateral acceleration of the vehicle to reduce the motor torque at each wheel. The performance of the control algorithm is evaluated by MATLAB/Simulink-CarSim cosimulation and experiments. A test car with two independent drive motors at the rear wheels is developed. From the simulation and vehicle test, it is found that the control algorithm improves vehicle performance.


title: Active Estimation for Jump Markov Linear Systems

abstract: Jump Markov Linear Systems are convenient models for systems that exhibit both continuous dynamics and discrete mode changes. Estimating the hybrid discrete-continuous state of these systems is important for control and fault detection. Existing solutions for hybrid estimation approximate the belief state by maintaining a subset of the possible discrete mode sequences. This approximation can cause the estimator to lose track of the true mode sequence when the effects of discrete mode changes are subtle. In this paper, we present a method for active hybrid estimation, where control inputs can be designed to discriminate between possible mode sequences. By probing the system for the purposes of estimation, such a sequence of control inputs can greatly reduce the probability of losing the true mode sequence compared to a nominal control sequence. Furthermore, by using a constrained finite horizon optimization formulation, we are able to guarantee that a given control task is achieved, while optimally detecting the hybrid state. In order to achieve this, we present three main contributions. First, we develop a method by which a sequence of control inputs is designed in order to discriminate optimally between a finite number of linear dynamic system models. These control inputs minimize a novel, tractable upper bound on the probability of model selection error. Second, we extend this approach to develop an active estimation method for Jump Markov Linear Systems by relating the probability of model selection error to the probability of losing the true mode sequence. Finally, we make this method tractable using a principled pruning technique. Simulation results show that the new method applied to an aircraft fault detection problem significantly decreases the probability of a hybrid estimator losing the true mode sequence.


Self-perceptions and sport behavior.


title: Spectral and Energy-Efficient Wireless Powered IoT Networks: NOMA or TDMA?

abstract: Wireless powered communication networks (WPCNs), where multiple energy-limited devices first harvest energy in the downlink and then transmit information in the uplink, have been envisioned as a promising solution for the future Internet-of-Things (IoT). Meanwhile, non-orthogonal multiple access (NOMA) has been proposed to improve the system spectral efficiency (SE) of the fifth-generation (5G) networks by allowing concurrent transmissions of multiple users in the same spectrum. As such, NOMA has been recently considered for the uplink of WPCNs based IoT networks with a massive number of devices. However, simultaneous transmissions in NOMA may also incur more transmit energy consumption as well as circuit energy consumption in practice which is critical for energy constrained IoT devices. As a result, compared to orthogonal multiple access schemes such as time-division multiple access (TDMA), whether the SE can be improved and/or the total energy consumption can be reduced with NOMA in such a scenario still remains unknown. To answer this question, we first derive the optimal time allocations for maximizing the SE of a TDMA-based WPCN (T-WPCN) and a NOMA-based WPCN (N-WPCN), respectively. Subsequently, we analyze the total energy consumption as well as the maximum SE achieved by these two networks. Surprisingly, it is found that N-WPCN not only consumes more energy, but also is less spectral efficient than T-WPCN. Simulation results verify our theoretical findings and unveil the fundamental performance bottleneck, i.e., "worst user bottleneck problem", in multiuser NOMA systems.


title: Penile Enlargement: From Medication to Surgery

abstract: Penis lengthening pills, stretch apparatus, vacuum pumps, silicone injections, and lengthening and thickening operations are available for men who worry about their penis size. Surgery is thus far the only proven scientific method for penile enlargement. In this article, we consider patient selection, outcome evaluation, and techniques applied. In our view, sexological counseling and detailed explanation of risks and complications are mandatory before any operative intervention.


title: SemEval-2007 Task 02: Evaluating Word Sense Induction and Discrimination Systems

abstract: The goal of this task is to allow for comparison across sense-induction and discrimination systems, and also to compare these systems to other supervised and knowledge-based systems. In total there were 6 participating systems. We reused the SemEval-2007 English lexical sample subtask of task 17, and set up both clustering-style unsupervised evaluation (using OntoNotes senses as gold-standard) and a supervised evaluation (using the part of the dataset for mapping). We provide a comparison to the results of the systems participating in the lexical sample subtask of task 17.


title: Nonisolated High Step-up Boost Converter Integrated With Sepic Converter

abstract: For a nonisolated high step-up converter, the combination of a boost converter with a series output module is investigated in this paper. As a solution to supplement the insufficient step-up ratio and distribute a voltage stress of a classical boost converter, a sepic-integrated boost (SIB) converter, which provides an additional step-up gain with the help of an isolated sepic converter, is proposed. Since the boost converter and the sepic converter share a boost inductor and a switch, its structure is simple. Moreover, the SIB converter needs no current snubber for the diodes, since the transformer leakage inductor alleviates the reverse recovery. The operational principle and characteristics of SIB converter are presented, and verified experimentally with a 200 W, 42 V input, 400 V output prototype converter.


title: A Zero Voltage Switching SVM (ZVS–SVM) Controlled Three-Phase Boost Rectifier

abstract: A zero voltage switching space vector modulation (ZVS-SVM) controlled three-phase boost rectifier is proposed. A branch composed of one active switch, one resonant inductor and one clamping capacitor is added to the six-switch boost rectifier. In addition, a ZVS-SVM for the rectifier is proposed. It can effectively suppress the reverse recovery process of the bridge switch anti-parallel diodes. All the switches are turned on under zero voltage condition, thus the switching loss is reduced, and electromagnetic inference can be lowered. The switching frequency of the rectifier is fixed. The auxiliary switch works at the same frequency with other switch. All the switches in the rectifier have lower voltage stress. A digital signal processor controlled 4-kW prototype is built to verify the theory


title: A Methodology for Formalizing Model-Inversion Attacks

abstract: Confidentiality of training data induced by releasing machine-learning models, and has recently received increasing attention. Motivated by existing MI attacks and other previous attacks that turn out to be MI "in disguise," this paper initiates a formal study of MI attacks by presenting a game-based methodology. Our methodology uncovers a number of subtle issues, and devising a rigorous game-based definition, analogous to those in cryptography, is an interesting avenue for future work. We describe methodologies for two types of attacks. The first is for black-box attacks, which consider an adversary who infers sensitive values with only oracle access to a model. The second methodology targets the white-box scenario where an adversary has some additional knowledge about the structure of a model. For the restricted class of Boolean models and black-box attacks, we characterize model invertibility using the concept of influence from Boolean analysis in the noiseless case, and connect model invertibility with stable influence in the noisy case. Interestingly, we also discovered an intriguing phenomenon, which we call "invertibility interference," where a highly invertible model quickly becomes highly non-invertible by adding little noise. For the white-box case, we consider a common phenomenon in machine-learning models where the model is a sequential composition of several sub-models. We show, quantitatively, that even very restricted communication between layers could leak a significant amount of information. Perhaps more importantly, our study also unveils unexpected computational power of these restricted communication channels, which, to the best of our knowledge, were not previously known.


title: Combining Big Data and Thick Data Analyses for Understanding Youth Learning Trajectories in a Summer Coding Camp

abstract: In this paper we explore how to assess novice youths' learning of programming in an open-ended, project-based learning environment. Our goal is to combine analysis of frequent, automated snapshots of programming (e.g., "big" data) within the "thick" social context of kids? learning for deeper insights into their programming trajectories. This paper focuses on the first stage of this endeavor: the development of exploratory quantitative measures of youths? learning of computer science concepts. Analyses focus on kids? learning in a series of three Scratch Camps where 64 campers aged 10-13 used Scratch 2.0 to make a series of creative projects over 30 hours in five days. In the discussion we consider the highlights of the insights-and blind spots-of each data source with regard to youths' learning.


title: Bootstrap estimated true and false positive rates and ROC curve

abstract: Diagnostic studies and new biomarkers are assessed by the estimated true and false positive rates of the classification rule. One diagnostic rule is considered for high-dimensional predictor data. Cross-validation and the leave-one-out bootstrap are discussed to estimate true and false positive rates of classifiers by the machine learning methods Adaboost, Bagging, Random Forest, (penalized) logistic regression and support vector machines. The .632+ bootstrap estimation of the misclassification error has been previously proposed to adjust the overfitting of the apparent error. This idea is generalized to the estimation of true and false positive rates. Tree-based simulation models with 8 and 50 binary non-informative variables are analysed to examine the properties of the estimators. Finally, a bootstrap estimation of receiver operating characteristic (ROC) curves is suggested and a .632+ bootstrap estimation of ROC curves is discussed. This approach is applied to high-dimensional gene expression data of leukemia and predictors of image data for glaucoma diagnosis.


title: A new microstrip array antenna with cosecant-squared beam shaping as a radiating column for SSR

abstract: Radiating Column of SSR antennas play an important role in final pattern formation. In this paper, a microstrip array antenna with modified cosecant squared pattern is designed to work as radiating column of SSR's antennas in L band. This simple structure consists of a feed network with 11 elements of half-wave printed dipoles as radiating elements. Synthesis pattern method used to obtain required amplitude and phase of elements for desired pattern. Wilkinson power dividers are used to prepare desired power amplitudes to feed elements. A special corporate feed network with constant group delay used to provide wide frequency bandwidth. Main beam of designed antenna has about 9 degrees tilt above the ground in the elevation plane, nearly 10 dB gain and 50 degree elevation beam width in −10 dBc. Return loss in center frequency (1060 MHz) is 22 dB. Rogers's RT Duroid 5880 with thickness of 31 mil is used as laminate in design. The proposed one-layer microstrip array with low complexity and low manufacturing and maintenance cost can be used in all SSR antennas.


title: Social Comparison as a Predictor of Body Dissatisfaction: A Meta-Analytic Review

abstract: The relationship between social comparison and body dissatisfaction was examined using meta-analysis. Several demographic and methodological variables were examined as potential moderators. Data from 156 studies (189 effect sizes) showed that social comparison was related to higher levels of body dissatisfaction. The effect for social comparison and body dissatisfaction was stronger for women than men and inversely related to age. This effect was stronger when social comparison was directly measured rather than inferred. No differences emerged for the presence of eating psychopathology, study design, or object of comparison. Results confirm theory and research suggesting that comparing oneself unfavorably to another on the basis of appearance may lead to dissatisfaction with one's own appearance. Moderator variables refine our understanding of the social comparison―body dissatisfaction relationship. These constructs and their relationship should be explored further in future studies.


title: Exploring Teenagers' Motivation to Exercise through Technology Probes. In: 25th British Computer Society Conference on Human Computer Interaction: HCI2011

abstract: This study used existing digital technologies as probes to explore teenager's use of exercise-related technology, their usage contexts, and their ideas for the design of ubiquitous technology that would persuade their peers to be more active in their daily lives. The teenagers formed two groups using two different types of step counters as their data collection device. Both groups used the same social website as their data logging tool and for social interaction. The one-week baseline plus six-weeks longitudinal study incorporated innovation workshops for generating user-centred design ideas: analysis of the findings drew out important lessons for the design of future devices. Key among these were: the impact of authority figures in restricting teenagers' use of technologies, teenagers' openness to sharing (privacy is not a concern), that data collection technologies must be ubiquitous but invisible, social interaction via technology is expected and positive messages reinforcing attainments of goals are valued (negative feedback is seen as demotivating).


title: A Naive Bayes and Maximum Entropy approach to sentiment analysis: Capturing domain-specific data in Weibo

abstract: Since social media have become very popular during the past few years, researchers have been focusing on being able to automatically process and extract sentiments information from large volume of social media data. This paper contributes to the topic, by focusing on sentiment analysis for Chinese social media. In this paper, we propose to rely on Part of Speech (POS) tags in order to extract unigrams and bigrams features. Bigrams are generated according to the grammatical relation between consecutive words. With those features, we have shown that focusing on a specific topic allows to reach higher estimation accuracy.


title: Recent approaches to non-intrusive load monitoring techniques in residential settings

abstract: The concept of Smart Grids is closely related to energy conservation and load shedding concepts. However, it is difficult to quantify the effectiveness of energy conservation efforts in residential settings without any sort of end-use energy information as feedback. In order to achieve that, load monitoring methods are normally used. In recent years, non-intrusive load monitoring (NILM) approaches are gaining popularity due to their minimal installation requirements and cost effectiveness. For a NILM system to work, only one sensor at the entry point to a home is required. Fluctuations in the aggregate power consumption signals are used to mathematically estimate the composition of operation of appliances. This approach eliminates the requirement of installing plug-meters for every appliance in the house. In this paper, we provide a review of recent research efforts on state-of-the-art NILM algorithms before concluding with a baseline and overall vision for our future research direction.


title: Serotonin, noradrenaline, dopamine metabolites in transcendental meditation-technique

abstract: The highly significant increase of 5-HIAA (5-hydroxyindole-3-acetic acid) in Transcendental Meditation technique suggests systemic serotonin as “rest and fulfillment hormone” of deactivation-relaxation.


title: Illuminating light: an optical design tool with a luminous-tangible interface

abstract: We describe a novel system for rapid prototyping of laserbased optical and holographic layouts. Users of this optical prototyping tool ‐ called the Illuminating Light system ‐ move physical representations of various optical elements about a workspace, while the system tracks these components and projects back onto the workspace surface the simulated propagation of laser light through the evolving layout. This application is built atop the Luminous Room infrastructure, an aggregate of interlinked, computer-controlled projector-camera units called I/O Bulbs. Philosophically, the work embodies the emerging ideas of the Luminous Room and builds on the notions of ‘graspable media’. We briefly introduce the I/O Bulb and Luminous Room concepts and discuss their current implementations. After an overview of the optical domain that the Illuminating Light system is designed to address, we present the overall system design and implementation, including that of an intermediary toolkit called voodoo which provides a general facility for object identification and tracking.


title: Vehicle pose estimation in cluttered urban environments using multilayer adaptive Monte Carlo localization

abstract: In this contribution, we propose multilayer adaptive Monte Carlo localization (ML-AMCL) in combination with 3D point registration algorithms as a GPS-independent framework for precise global vehicle pose estimation in challenging urban environments. Scans from a 3D LIDAR sensor are split into a set of horizontal layers which are then used for localization with separate instances of an AMCL algorithm. A consistency check is performed for the obtained pose estimates in every time step and feasible results are fused. It is shown, that ML-AMCL is superior to existing localization approaches and is well suited as a prior for 3D point registration algorithms for the refinement of the pose estimate. Our key contributions are: i) proposal of ML-AMCL. ii) Incorporation of prior information from ML-AMCL into different pose refinement procedures, i.e. ICP variants and normal distribution transform (NDT), for precise vehicle localization. By means of experimental evaluation with a challenging real data set, performance of the reference localization system is verified. The proposed localization framework achieves a mean Euclidean measurement error of 0:2m under severe adverse environment conditions. The latter include high clutter densities in the sensor measurements and semi-static objects in the localization map.


title: Leveraging Legacy Code to Deploy Desktop Applications on the Web

abstract: Xax is a browser plugin model that enables developers to leverage existing tools, libraries, and entire programs to deliver feature-rich applications on the web. Xax employs a novel combination of mechanisms that collectively provide security, OS-independence, performance, and support for legacy code. These mechanisms include memory-isolated native code execution behind a narrow syscall interface, an abstraction layer that provides a consistent binary interface across operating systems, system services via hooks to existing browser mechanisms, and lightweight modifications to existing tool chains and code bases. We demonstrate a variety of applications and libraries from existing code bases, in several languages, produced with various tool chains, running in multiple browsers on multiple operating systems. With roughly two person-weeks of effort, we ported 3.3 million lines of code to Xax, including a PDF viewer, a Python interpreter, a speech synthesizer, and an OpenGL pipeline.


title: Recommendation System for Outfit Selection (RSOS)

abstract: We propose a system which will be able to recommend the user to choose appropriate outfits suits to their personality. The necessity of this system is to reduce the outfit selection and purchasing time; this will also help to create tailor made outfits as per the personality traits. The guidelines for selection of their respective outfits are based upon various bodily parameters that evolve with the learning of available labeled and unlabeled data. The system is based on two modules of processes; first one is to recognize the features for usage of outfits like traditional, western, functional, daytime or night etc, second is to calculate the body measurement parameters. The proposed system will have image capturing by using HAAR feature or input device for getting body parameters. We intend to classify and extract the best possible outfits from the system by using HIGEN MINER algorithm. The applications of outfit selection will be ranging from manual gender selection, image processing with body feature extractions, Value comparison with database by using different statistical techniques and data mining algorithms. After that it will recommend best outfits as per body parameters, inputs and availability


title: Metaphor comprehension: What makes a metaphor difficult to understand?

abstract: Comprehension difficulty was rated for metaphors of the form NOUN1 IS A NOUN2; in addition, participants completed frames of the form NOUN1 IS ________ with their literal interpretation of the metaphor. Metaphor comprehension was simulated with a computational model based on latent semantic analysis (LSA). The model matched participants' interpretations for both easy and difficult metaphors. When interpreting easy metaphors, both the participants and the model generated highly consistent responses. When interpreting difficult metaphors, both the participants and the model generated disparate responses.


title: A gas sensor using double split-ring resonator coated with conducting polymer at microwave frequncies

abstract: In this research, a gas sensor using double split-ring resonator (DSRR) incorporated with conducting polymer (CP) is proposed at microwave frequencies (Ku-band). The DSRR fabricated on printed circuit board (PCB) is excited by a high-impedance microstrip line, and the CP is coated inside of an inner circle of the DSRR. Electrical characteristics of the CP can be deviated by an interaction between CP and a target gas, and then this deviation of electrical characteristic is demonstrated by S21 frequency response of the DSRR. To examine the performance of the proposed sensor, 100 ppm ethanol (C2H5OH) gas is exposed at room temperature. According to the measured result, the S21 resonance frequency of the DSRR is shifted by 220 MHz and simultaneously, the resonance amplitude is changed by 0.79 dB level. It is clearly found that the DSRR with CP material can be a good candidate for a sensitive gas sensor operating at microwave frequencies.


title: HiWorD: a Petri net-based hierarchical workflow designer

abstract: Much work is being conducted in the area of business process modeling using workflow technology. HiWorD is a hierarchical workflow modeling prototype with simulation capability. It models business processes using Petri nets in a hierarchical manner and implements recovery transitions as a technique to recover from exceptions. The workflow hierarchy is created by refining places and transitions using predefined patterns. By using these patterns, it is proven that the resulting workflow will be sound.


title: Influences on cooperation in BitTorrent communities

abstract: We collect BitTorrent usage data across multiple file-sharing communities and analyze the factors that affect users' cooperative behavior. We find evidence that the design of the BitTorrent protocol results in increased cooperative behavior over other P2P protocols used to share similar content (e.g. Gnutella). We also investigate two additional community-specific mechanisms that foster even more cooperation.


title: A Unified Analysis of IEEE 802.11 DCF Networks: Stability, Throughput, and Delay

abstract: In this paper, a unified analytical framework is established to study the stability, throughput, and delay performance of homogeneous buffered IEEE 802.11 networks with Distributed Coordination Function (DCF). Two steady-state operating points are characterized using the limiting probability of successful transmission of Head-of-Line (HOL) packets p given that the network is in unsaturated or saturated conditions. The analysis shows that a buffered IEEE 802.11 DCF network operates at the desired stable point p=pL if it is unsaturated. pL does not vary with backoff parameters, and a stable throughput can be always achieved at pL. If the network becomes saturated, in contrast, it operates at the undesired stable point p=pA, and a stable throughput can be achieved at pA if and only if the backoff parameters are properly selected. The stable regions of the backoff factor q and the initial backoff window size W are derived, and illustrated in cases of the basic access mechanism and the request-to-send/clear-to-send (RTS/CTS) mechanism. It is shown that the stable regions are significantly enlarged with the RTS/CTS mechanism, indicating that networks in the RTS/CTS mode are much more robust. Nevertheless, the delay analysis further reveals that lower access delay is incurred in the basic access mode for unsaturated networks. If the network becomes saturated, the delay performance deteriorates regardless of which mode is chosen. Both the first and the second moments of access delay at pA are sensitive to the backoff parameters, and shown to be effectively reduced by enlarging the initial backoff window size W.


title: Pathologic Features of Infectious Gastritis

abstract: This manuscript presents a review of infectious causes of gastritis aimed at the practicing anatomic pathologist. We shall highlight unique histologic findings and clinical attributes that will assist those analyzing endoscopically obtained mucosal biopsies of the stomach or resection specimens.


title: Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer

abstract: Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability , under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases the computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control.


title: AMP-Inspired Deep Networks for Sparse Linear Inverse Problems

abstract: Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem, where one seeks to recover a sparse signal from a few noisy linear measurements. In this paper, we propose two novel neural-network architectures that decouple prediction errors across layers in the same way that the approximate message passing (AMP) algorithms decouple them across iterations: through Onsager correction. First, we propose a “learned AMP” network that significantly improves upon Gregor and LeCun's “learned ISTA.” Second, inspired by the recently proposed “vector AMP” (VAMP) algorithm, we propose a “learned VAMP” network that offers increased robustness to deviations in the measurement matrix from i.i.d. Gaussian. In both cases, we jointly learn the linear transforms and scalar nonlinearities of the network. Interestingly, with i.i.d. signals, the linear transforms and scalar nonlinearities prescribed by the VAMP algorithm coincide with the values learned through back-propagation, leading to an intuitive interpretation of learned VAMP. Finally, we apply our methods to two problems from 5G wireless communications: compressive random access and massive-MIMO channel estimation.


title: Emotional responses to pleasant and unpleasant music correlate with activity in paralimbic brain regions

abstract: Neural correlates of the often-powerful emotional responses to music are poorly understood. Here we used positron emission tomography to examine cerebral blood flow (CBF) changes related to affective responses to music. Ten volunteers were scanned while listening to six versions of a novel musical passage varying systematically in degree of dissonance. Reciprocal CBF covariations were observed in several distinct paralimbic and neocortical regions as a function of dissonance and of perceived pleasantness/unpleasantness. The findings suggest that music may recruit neural mechanisms similar to those previously associated with pleasant/unpleasant emotional states, but different from those underlying other components of music perception, and other emotions such as fear. articles


title: Use of Optical Motion Capture in Sports Science: A Case Study of Golf Swing

abstract: The research aimed to explore and demonstrate the useful technique in creating the motion picture of golf swing movement for golf beginner utilizing the motion capture tool. The sample consists of three experts chosen by purposive sampling, while, the research tools applied in this research includes: 1) the golf swing movement and gesture for golf beginner demonstrated by the motion capture tool, and 2) the questionnaire with five-level rating-scale on the proper movement of golf swing movement which was verified by the experts for its reliability. In particular, the research outcome indicated that the creation of the motion picture of golf swing for the beginner comprises of 3 major parts as follows: 1) the motion capture tool setup, suitable and sufficient space for working, camera positioning, and the right number of reflex spots on the actor's body, 2) the golf swing movement capture divided into 3 patterns by the number of the reflex spots and each pattern was done for at least 5 times by golf professionals for the movement capture, and 3) the three 3D characters and motion picture in which the researchers created with similar ratio of real human and applied the captured movement data to construct the animation character skeleton before obtaining three sets of the golf swing animation divided by the number of reflex spots. Surprisingly, the research revealed the golf professionals' suggestion that most of the current movement of golf swing has been performed in improper manners. In this regard, this research suggested that to demonstrate the proper golf swing movement for golf beginner, the key factors was not only the number and position of reflex spots on the actor's body but also the certain regulation in utilizing the motion capture tools, and that the 3D characters, in most cases, should be created with similar ratio of body as a real human.


title: A Polynomial-Time Algorithm for the Knapsack Problem with Two Variables

abstract: The general knapsack problem is known to be NP-complete. In this paper a very special knapsack problem ia studied, namely, one with only two variables. A polynomial-time algorithm is presented and analyzed. However, it remains an open problem that for any fixed n > 2, the knapsack problem with n variables can be solved in polynomial time.


Cancer Cell Targeting With Functionalized Quantum Dot-Encoded Polyelectrolyte Microcapsules


title: Medical decision-making in children and adolescents: developmental and neuroscientific aspects

abstract: Various international laws and guidelines stress the importance of respecting the developing autonomy of children and involving minors in decision-making regarding treatment and research participation. However, no universal agreement exists as to at what age minors should be deemed decision-making competent. Minors of the same age may show different levels of maturity. In addition, patients deemed rational conversation-partners as a child can suddenly become noncompliant as an adolescent. Age, context and development all play a role in decision-making competence. In this article we adopt a perspective on competence that specifically focuses on the impact of brain development on the child’s decision-making process. We believe that the discussion on decision-making competence of minors can greatly benefit from a multidisciplinary approach. We adopted such an approach in order to contribute to the understanding on how to deal with children in decision-making situations. Evidence emerging from neuroscience research concerning the developing brain structures in minors is combined with insights from various other fields, such as psychology, decision-making science and ethics. Four capacities have been described that are required for (medical) decision-making: (1) communicating a choice; (2) understanding; (3) reasoning; and (4) appreciation. Each capacity is related to a number of specific skills and abilities that need to be sufficiently developed to support the capacity. Based on this approach it can be concluded that at the age of 12 children can have the capacity to be decision-making competent. However, this age coincides with the onset of adolescence. Early development of the brain’s reward system combined with late development of the control system diminishes decision-making competence in adolescents in specific contexts. We conclude that even adolescents possessing capacities required for decision-making, may need support of facilitating environmental factors. This paper intends to offer insight in neuroscientific mechanisms underlying the medical decision-making capacities in minors and to stimulate practices for optimal involvement of minors. Developing minors become increasingly capable of decision-making, but the neurobiological development in adolescence affects competence in specific contexts. Adequate support should be offered in order to create a context in which minors can make competently make decisions.


title: A 160 mV Robust Schmitt Trigger Based Subthreshold SRAM

abstract: We propose a novel Schmitt trigger (ST) based differential 10-transistor SRAM (static random access memory) bitcell suitable for subthreshold operation. The proposed Schmitt trigger based bitcell achieves 1.56 x higher read static noise margin (SNM) ( Vdd = 400 mV) compared to the conventional 6T cell. The robust Schmitt trigger based memory cell exhibits built-in process variation tolerance that gives tight SNM distribution across the process corners. It utilizes differential operation and hence does not require any architectural changes from the present 6T architecture. At iso-area and iso-read-failure probability the proposed memory bitcell operates at a lower (175 mV) Vdd with 18% reduction in leakage and 50% reduction in read/write power compared to the conventional 6T cell. Simulation results show that the proposed memory bitcell retains data at a supply voltage of 150 mV. Functional SRAM with the proposed memory bitcell is demonstrated at 160 mV in 0.13 mum CMOS technology.


title: Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

abstract: Semantic image segmentation is an essential component of modern autonomous driving systems, as an accurate understanding of the surrounding scene is crucial to navigation and action planning. Current state-of-the-art approaches in semantic image segmentation rely on pre-trained networks that were initially developed for classifying images as a whole. While these networks exhibit outstanding recognition performance (i.e., what is visible?), they lack localization accuracy (i.e., where precisely is something located?). Therefore, additional processing steps have to be performed in order to obtain pixel-accurate segmentation masks at the full image resolution. To alleviate this problem we propose a novel ResNet-like architecture that exhibits strong localization and recognition performance. We combine multi-scale context with pixel-level accuracy by using two processing streams within our network: One stream carries information at the full image resolution, enabling precise adherence to segment boundaries. The other stream undergoes a sequence of pooling operations to obtain robust features for recognition. The two streams are coupled at the full image resolution using residuals. Without additional processing steps and without pre-training, our approach achieves an intersection-over-union score of 71.8% on the Cityscapes dataset.


title: Assessing observational studies of medical treatments

abstract: BackgroundPrevious studies have assessed the validity of the observational study design by comparing results of studies using this design to results from randomized controlled trials. The present study examined design features of observational studies that could have influenced these comparisons.MethodsTo find at least 4 observational studies that evaluated the same treatment, we reviewed meta-analyses comparing observational studies and randomized controlled trials for the assessment of medical treatments. Details critical for interpretation of these studies were abstracted and analyzed qualitatively.ResultsIndividual articles reviewed included 61 observational studies that assessed 10 treatment comparisons evaluated in two studies comparing randomized controlled trials and observational studies. The majority of studies did not report the following information: details of primary and ancillary treatments, outcome definitions, length of follow-up, inclusion/exclusion criteria, patient characteristics relevant to prognosis or treatment response, or assessment of possible confounding. When information was reported, variations in treatment specifics, outcome definition or confounding were identified as possible causes of differences between observational studies and randomized controlled trials, and of heterogeneity in observational studies.ConclusionReporting of observational studies of medical treatments was often inadequate to compare study designs or allow other meaningful interpretation of results. All observational studies should report details of treatment, outcome assessment, patient characteristics, and confounding assessment.


title: Phase separation in oxygen deficient gallium oxide films grown by pulsed-laser deposition

abstract: The oxygen pressure and the substrate temperature during pulsed-laser deposition play a major role on the nature and properties of gallium oxide films. At moderate substrate temperature (673 K) and under high vacuum (10−7 mbar) a nanocomposite film composed of Ga metallic clusters embedded in a stoichiometric Ga2O3 matrix may be obtained without postdeposition annealing. The growth of such films is due to a phase separation of largely oxygen deficient metastable gallium oxide films Ga2Ox (x = 2.3) into the most stable phases (Ga and Ga2O3) and occurs for particular growth conditions. The composition and the surface morphology of films as well as their electrical behaviour are interpreted according to the effects of the parameters governing this phase separation (oxygen deficiency and temperature). It is suggested that the initial step in the disproportionation reaction is the formation of stoichiometric Ga2O3 nanocrystallites in the metastable sub-oxide Ga2Ox phase. The crystallization of such nanosize particles is governed by the local distribution of oxygen and gallium species impinging the substrate during the growth and allowing nucleation centre with the Ga2O3 composition. © 2012 Elsevier B.V. All rights reserved.


title: Path dependence and the validation of agent‐based spatial models of land use

abstract: In this paper, we identify two distinct notions of accuracy of land-use models and highlight a tension between them. A model can have predictive accuracy: its predicted land-use pattern can be highly correlated with the actual land-use pattern. A model can also have process accuracy: the process by which locations or land-use patterns are determined can be consistent with real world processes. To balance these two potentially conflicting motivations, we introduce the concept of the invariant region, i.e., the area where land-use type is almost certain, and thus path independent; and the variant region, i.e., the area where land use depends on a particular series of events, and is thus path dependent. We demonstrate our methods using an agent-based land-use model and using multitemporal land-use data collected for Washtenaw County, Michigan, USA. The results indicate that, using the methods we describe, researchers can improve their ability to communicate how well their model performs, the situations or instances in which it does not perform well, and the cases in which it is relatively unlikely to predict well because of either path dependence or stochastic uncertainty.


title: Analysis of banking data using machine learning

abstract: In today's world large amount of data is generated in every field and banking industry is one of them. This data contains valuable information. Hence, it is very important to store, process, manage and analyze this data to extract knowledge from it. It helps to increase business profit. Banking industry plays very important role in economy of country. Customers are the main asset of the bank. Hence it is necessary to focus on problems faced by the banks. Here, we are working on customer retention and fraud detection. In this work, supervised artificial neural network algorithm is implemented for classification purpose.


title: Resource-Aware Photo Crowdsourcing Through Disruption Tolerant Networks

abstract: Photo crowdsourcing with smartphone has attracted considerable attention recently due to the prevalence of smartphones and the rich information provided by photos. In scenarios such as disaster recovery or battlefield, where the cellular network is partly damaged or severely overloaded, Disruption Tolerant Networks (DTNs) become the best way to deliver the crowdsourced photos. Since the bandwidth and storage resources in DTN are very limited and not enough to deliver all the crowdsourced photos, it is important to prioritize more valuable photos to use the limited resources. In this paper, we design a resource-aware photo crowdsourcing framework in DTN, which uses photo metadata including the smartphone's location, orientation, and other built-in camera's parameters, to estimate the value of photos. We propose a photo selection algorithm to maximize the value of photos delivered to the command center considering bandwidth and storage constraints. Both prototype implementation and trace-driven simulations demonstrate the effectiveness of our design.


title: How Public Is My Private Life?

abstract: Online dating services let users expand their dating pool beyond their social network and specify important characteristics of potential partners. To assess compatibility, users share personal information -- e.g., identifying details or sensitive opinions about sexual preferences or worldviews -- in profiles or in one-on-one communication. Thus, participating in online dating poses inherent privacy risks. How people reason about these privacy risks in modern online dating ecosystems has not been extensively studied. We present the results of a survey we designed to examine privacy-related risks, practices, and expectations of people who use or have used online dating, then delve deeper using semi-structured interviews. We additionally analyzed 400 Tinder profiles to explore how these issues manifest in practice. Our results reveal tensions between privacy and competing user values and goals, and we demonstrate how these results can inform future designs.


title: Multi-Document Summarization Of Evaluative Text

abstract: In many decision-making scenarios, people can benefit from knowing what other people's opinions are. As more and more evaluative documents are posted on the Web, summarizing these useful resources becomes a critical task for many organizations and individuals. This paper presents a framework for summarizing a corpus of evaluative documents about a single entity by a natural language summary. We propose two summarizers: an extractive summarizer and an abstractive one. As an additional contribution, we show how our abstractive summarizer can be modified to generate summaries tailored to a model of the user preferences that is solidly grounded in decision theory and can be effectively elicited from users. We have tested our framework in three user studies. In the first one, we compared the two summarizers. They performed equally well relative to each other quantitatively, while significantly outperforming a baseline standard approach to multidocument summarization. Trends in the results as well as qualitative comments from participants suggest that the summarizers have different strengths and weaknesses. After this initial user study, we realized that the diversity of opinions expressed in the corpus (i.e., its controversiality) might play a critical role in comparing abstraction versus extraction. To clearly pinpoint the role of controversiality, we ran a second user study in which we controlled for the degree of controversiality of the corpora that were summarized for the participants. The outcome of this study indicates that for evaluative text abstraction tends to be more effective than extraction, particularly when the corpus is controversial. In the third user study we assessed the effectiveness of our user tailoring strategy. The results of this experiment confirm that user tailored summaries are more informative than untailored ones.


title: Bayesian Methods for Data Analysis in the Organizational

abstract: The use of Bayesian methods for data analysis is creating a revolution in fields ranging from genetics to marketing. Yet, results of our literature review, including more than 10,000 articles publi...


Bayesian Optimization with Empirical Constraints ( PhD Proposal )


Compact Feedforward Sequential Memory Networks for Large Vocabulary Continuous Speech Recognition.


title: A two-layer optimization framework for UAV path planning with interval uncertainties

abstract: We propose a two-layer optimization framework for the unmanned aerial vehicle path planning problem to handle interval uncertainties that exist in the combat field. When evaluating a candidate flight path, we first calculate the interval response (i.e., the upper and lower bounds) of the candidate flight path within the inner layer of the framework using a collocation interval analysis method (CIAM). Then, in the outer layer, we introduce a novel criterion for interval response comparison. The artificial bee colony algorithm is used to search for the optimal flight path according to this new criterion. Our experimental results show that the CIAM adopted is a feasible option, which largely eases the computational burden. Moreover, our derived flight paths can effectively handle bounded uncertainties without knowing the corresponding uncertainty distributions.


title: The Reduced Nearest Neighbor Rule

abstract: A further modification to Cover and Hart's nearest neighbor decision rule, the reduced nearest neighbor rule, is introduced. Experimental results demonstrate its accuracy and efficiency.


title: A stable multi-scale kernel for topological machine learning

abstract: Topological data analysis offers a rich source of valuable information to study vision problems. Yet, so far we lack a theoretically sound connection to popular kernel-based learning techniques, such as kernel SVMs or kernel PCA. In this work, we establish such a connection by designing a multi-scale kernel for persistence diagrams, a stable summary representation of topological features in data. We show that this kernel is positive definite and prove its stability with respect to the 1-Wasserstein distance. Experiments on two benchmark datasets for 3D shape classification/retrieval and texture recognition show considerable performance gains of the proposed method compared to an alternative approach that is based on the recently introduced persistence landscapes.


title: Interference of adulterants in thin layer chromatography method for drugs of abuse

abstract: Urine screening for determining illicit drug use has become a necessary adjunct to treat drug addicts. Some of the laboratories use thin layer chromatography (TLC) for screening drugs of abuse in urine. Six adulterants viz. sodium chloride, sodium bicarbonate, liquid detergent soap (Labolene), bleacing powder, vinegar, ascorbic acid, gave false negative results for morphine, diazepam and phenobarbital drug assays when screened by TLC. Of the three drugs tested, morphine was found to be most sensitive to all adulterants. Adulterants interfered at lower concentration for morphine assay when analysed by TLC methodology than the reported EMIT technique.


title: Detecting and tracking all moving objects in wide-area aerial video

abstract: Multi-megapixel cameras are transforming airborne video surveillance by enabling persistent imaging of extremely large areas while providing sufficient pixel density to resolve both vehicles and pedestrians. The sheer spatial and temporal volume of data has rendered human scanning of expansive images for miniscule moving objects intractable, underscoring the importance of automated detection and tracking systems. Existing algorithms, however, are generally designed for stationary cameras and moderately-sized objects. This paper presents one of the first systems for reliably detecting and tracking low-resolution objects of varying size and shape in challenging wide-area aerial video. Significant contributions include a simple, fast approach for robust motion detection with parallax handling; spatial-temporal filtering for quickly discarding spurious detections; adaptive shape learning for unusually-shaped objects; and multi-cue fusion for state evolution that enables tracking through confusion, occlusions, and stops. Our system is highly efficient and parallelizable, processing 1-megapixel image streams in real time on a single CPU core. Experiments on a variety of data sets demonstrate that the system outperforms more traditional detection and tracking approaches, and is able to find pedestrians missed by human ground truthers despite tiny size, poor contrast, and surrounding clutter.1


Neural Message Passing for Jet Physics


title: A Classification-System Improves the Intra- and Interobserver Reliability of Radiographic Diagnosis of “Pistol-Grip-Deformity”

abstract: Early detection of pistol-grip-deformity may be beneficial in optimising the outcome and the cost effectiveness of surgery. It is important to identify reliable radiographic parameters in assessing femoro-acetabular impingement (FAI) to develop a treatment algorithm. Radiographs of 47 patients ranging from "pistol grip deformity" to a normal head-neck-junction were measured for alpha angle and head ratio, and then classified by two different observers. The Bland-Altman plot was used for inter- and intraobserver agreement of alpha angle and head ratio. Inter- and intraobserver agreement for classification of "pistol grip deformity" was determined using weighted Cohen's kappa coefficient. Observer I achieved a kappa coefficient of 0.97. Observer II achieved a kappa coefficient of 0.92. An interobserver kappa coefficient between 0.87 and 0.92 was achieved by using a classification system. By testing for interobserver agreement, a bias of -0.004 with an upper limit of 0.461 and a lower limit of -0.47 was seen for the ratio and a bias of -3.7 with an upper limit of 17.2 and a lower limit of -24.6 for the alpha angle. Therefore, poor results were seen for intra- and interobserver reliability by using only a single plane for classification of "pistol grip deformity". The strength of agreement could be improved by using a classification system (based on two planes).


The Concept of Lexical Platform


title: Towards Internet of Things (IOTS):Integration of Wireless Sensor Network to Cloud Services for Data Collection and Sharing

abstract: Cloud computing provides great benefits for applications hosted on the Web that also have special computational and storage requirements. This paper proposes an extensible and flexible architecture for integrating Wireless Sensor Networks with the Cloud. We have used REST based Web services as an interoperable application layer that can be directly integrated into other application domains for remote monitoring such as e-health care services, smart homes, or even vehicular area networks (VAN). For proof of concept, we have implemented a REST based Web services on an IP based low power WSN test bed, which enables data access from anywhere. The alert feature has also been implemented to notify users via email or tweets for monitoring data when they exceed values and events of interest.


title: Fake News Detection via NLP is Vulnerable to Adversarial Attacks

abstract: News plays a significant role in shaping people’s beliefs and opinions. Fake news has always been a problem, which wasn’t exposed to the mass public until the past election cycle for the 45th President of the United States. While quite a few detection methods have been proposed to combat fake news since 2015, they focus mainly on linguistic aspects of an article without any fact checking. In this paper, we argue that these models have the potential to misclassify fact-tampering fake news as well as under-written real news. Through experiments on Fakebox, a state-of-the-art fake news detector, we show that fact tampering attacks can be effective. To address these weaknesses, we argue that fact checking should be adopted in conjunction with linguistic characteristics analysis, so as to truly separate fake news from real news. A crowdsourced knowledge graph is proposed as a straw man solution to collecting timely facts about news events.


title: A Large-Scale Community Structure Analysis In Facebook

abstract: Understanding social dynamics that govern human phenomena, such as communications and social relationships is a major problem in current computational social sciences. In particular, given the unprecedented success of online social networks (OSNs), in this paper we are concerned with the analysis of aggregation patterns and social dynamics occurring among users of the largest OSN as the date: Facebook. In detail, we discuss the mesoscopic features of the community structure of this network, considering the perspective of the communities, which has not yet been studied on such a large scale. To this purpose, we acquired a sample of this network containing millions of users and their social relationships; then, we unveiled the communities representing the aggregation units among which users gather and interact; finally, we analyzed the statistical features of such a network of communities, discovering and characterizing some specific organization patterns followed by individuals interacting in online social networks, that emerge considering different sampling techniques and clustering methodologies. This study provides some clues of the tendency of individuals to establish social interactions in online social networks that eventually contribute to building a well-connected social structure, and opens space for further social studies.


title: Unsupervised Ontology Induction from Text

abstract: Extracting knowledge from unstructured text is a long-standing goal of NLP. Although learning approaches to many of its subtasks have been developed (e.g., parsing, taxonomy induction, information extraction), all end-to-end solutions to date require heavy supervision and/or manual engineering, limiting their scope and scalability. We present OntoUSP, a system that induces and populates a probabilistic ontology using only dependency-parsed text as input. OntoUSP builds on the USP unsupervised semantic parser by jointly forming ISA and IS-PART hierarchies of lambda-form clusters. The ISA hierarchy allows more general knowledge to be learned, and the use of smoothing for parameter estimation. We evaluate OntoUSP by using it to extract a knowledge base from biomedical abstracts and answer questions. OntoUSP improves on the recall of USP by 47% and greatly outperforms previous state-of-the-art approaches.


title: DetNet: A Backbone network for Object Detection

abstract: Recent CNN based object detectors, no matter one-stage methods like YOLO, SSD, and RetinaNe or two-stage detectors like Faster R-CNN, R-FCN and FPN are usually trying to directly finetune from ImageNet pre-trained models designed for image classification. There has been little work discussing on the backbone feature extractor specifically designed for the object detection. More importantly, there are several differences between the tasks of image classification and object detection. 1. Recent object detectors like FPN and RetinaNet usually involve extra stages against the task of image classification to handle the objects with various scales. 2. Object detection not only needs to recognize the category of the object instances but also spatially locate the position. Large downsampling factor brings large valid receptive field, which is good for image classification but compromises the object location ability. Due to the gap between the image classification and object detection, we propose DetNet in this paper, which is a novel backbone network specifically designed for object detection. Moreover, DetNet includes the extra stages against traditional backbone network for image classification, while maintains high spatial resolution in deeper layers. Without any bells and whistles, state-of-the-art results have been obtained for both object detection and instance segmentation on the MSCOCO benchmark based on our DetNet~(4.8G FLOPs) backbone. The code will be released for the reproduction.


title: Cities as Complex Systems: Scaling, Interactions, Networks, Dynamics and Urban Morphologies

abstract: A circuit for selectively enabling one circuit from among a plurality of circuit alternatives of an integrated circuit includes a selection circuit for selecting one circuit from among the plurality of circuit alternatives. The selection circuit is controlled by a bistable circuit having a preferred state. A disactivatable forcing circuit associated with the bistable circuit is provided for forcing the bistable circuit into a state opposite than the preferred state, so that when the forcing circuit is disactivated, the bistable circuit automatically switches to the preferred state.


title: Severe hyperhomocysteinemia and peripheral neuropathy as side effects of nitrous oxide in two patients with sickle cell disease

abstract: Abstract Nitrous oxide (N2O) is a widely used anesthetic agent. We report two patients with sickle cell disease (SCD) who presented with complications following the use of N2O. Patient 1, a 15-year-old girl, presented severe hyperhomocysteinemia, pancytopenia, vitamin B12 deficiency, and peripheral polyneuropathy after massive use of N2O for pain management. At the 1-year follow-up, hyperhomocysteinemia and B12 deficiency had resolved, but she had persisting mild symptoms of polyneuropathy. Patient 2, a 17-year-old boy, presented only severe hyperhomocysteinemia, only partially corrected by initial B12 supplementation. Careful monitoring of N2O use, especially in patients with SCD, is mandatory to prevent complications.


title: A Broadband Slot Antenna With Unidirectional Circularly Polarized Radiation Patterns

abstract: A novel circular slot antenna is proposed in this letter to generate a broadband circular polarization (CP) wave. This antenna consists of a Γ-shaped radiator and a circular ground with a cut of three ellipses. By adjusting the size and position of these ellipses, a CP wave can be realized. A chip resistor, which is used to receive the currents and decrease reflection, can significantly enhance the CP bandwidth. The study of the effect to antenna performance of different geometric parameters has been conducted. The results show that good performance of impedance bandwidth ( $S$ 11 < −10 dB) is approximately 106.1% (1.01 ∼ 3.29 GHz), and 3-dB axial-ratio bandwidth is approximately 89.7% (1.26 ∼ 3.31 GHz). Moreover, the antenna exhibits a stable average gain of about 6.0 dBic in the operating band, and the circularly polarized peak gain is about 7.6 dBic.


title: The Concept of gender identity disorder in childhood and adolescence after 39 years

abstract: Abstract The term “gender role” appeared in print first in 1955. The term “gender identity” was used in a press release, November 21, 1966, to announce the new clinic for transsexuals at The Johns Hopkins Hospital. It was disseminated in the media worldwide, and soon entered the vernacular. The definitions of gender and gender identity vary on a doctrinal basis. In popularized and scientifically debased usage, sex is what you are biologically; gender is what you become socially; gender identity is your own sense or conviction of maleness or femaleness; and gender role is the cultural stereotype of what is masculine and feminine. Causality with respect to gender identity disorder is subdivisible into genetic, prenatal hormonal, postnatal social, and postpubertal hormonal determinants, but there is, as yet, no comprehensive and detailed theory of causality. Gender coding in the brain is bipolar. In gender identity disorder, there is discordancy between the natal sex of one's external genitalia and the brain...


title: YourGloves, hothands and hotmits: devices to hold hands at a distance

abstract: There is a growing body of work in HCI on the design of communication technologies to help support lovers in long distance relationships. We build upon this work by presenting an exploratory study of hand-holding prototypes. Our work distinguishes itself by basing distance communication metaphors on elements of familiar, simple co-located behaviours. We argue that the combined evocative power of unique co-created physical representations of the absent other can be used by separated lovers to generate powerful and positive experiences, in turn sustaining romantic connections at a distance.


Using Goal Modeling and OntoUML for Reengineering the Good Relations Ontology


title: Purity: Building Fast, Highly-Available Enterprise Flash Storage from Commodity Components

abstract: Although flash storage has largely replaced hard disks in consumer class devices, enterprise workloads pose unique challenges that have slowed adoption of flash in ``performance tier'' storage appliances. In this paper, we describe Purity, the foundation of Pure Storage's Flash Arrays, the first all-flash enterprise storage system to support compression, deduplication, and high-availability. Purity borrows techniques from modern database and key-value storage architectures, and introduces novel storage primitives that have wide applicability to data management systems. For instance, all writes in Purity are monotonic, and deletions are handled using an atomic predicate-based tuple elision primitive. Purity's redundancy mechanisms are optimized for SSD failure modes and performance characteristics, allowing for fast recovery from component failures and lower space overhead than the best hard disk systems. We built deduplication and data compression schemes atop these primitives. Flash changes storage capacity/performance tradeoffs: unlike disk-based systems, flash deployments are rarely performance bound. A single Purity appliance can provide over 7GiB/s of throughput on 32KiB random I/Os, even through multiple device failures, and while providing asynchronous off-site replication. Typical installations have 99.9% latencies under 1ms, and production arrays average 5.4x data reduction and 99.999% availability. Purity takes advantage of storage performance increasing more rapidly than computational performance to build a simpler (with respect to engineering, installation, and management) scale-up storage appliance that supports hundreds of terabytes of highly-available, high-performance storage. The resulting performance and capacity supports many customer deployments of multiple applications, including scale-out and parallel systems, such as MongoDB and Oracle RAC, on a single Purity appliance.


title: Efficient Prediction Structures for Multiview Video Coding

abstract: An experimental analysis of multiview video coding (MVC) for various temporal and inter-view prediction structures is presented. The compression method is based on the multiple reference picture technique in the H.264/AVC video coding standard. The idea is to exploit the statistical dependencies from both temporal and inter-view reference pictures for motion-compensated prediction. The effectiveness of this approach is demonstrated by an experimental analysis of temporal versus inter-view prediction in terms of the Lagrange cost function. The results show that prediction with temporal reference pictures is highly efficient, but for 20% of a picture's blocks on average prediction with reference pictures from adjacent views is more efficient. Hierarchical B pictures are used as basic structure for temporal prediction. Their advantages are combined with inter-view prediction for different temporal hierarchy levels, starting from simulcast coding with no inter-view prediction up to full level inter-view prediction. When using inter-view prediction at key picture temporal levels, average gains of 1.4-dB peak signal-to-noise ratio (PSNR) are reported, while additionally using inter-view prediction at nonkey picture temporal levels, average gains of 1.6-dB PSNR are reported. For some cases, gains of more than 3 dB, corresponding to bit-rate savings of up to 50%, are obtained.


title: An integrated system for real-time model predictive control of humanoid robots

abstract: Generating diverse behaviors with a humanoid robot requires a mix of human supervision and automatic control. Ideally, the user's input is restricted to high-level instruction and guidance, and the controller is intelligent enough to accomplish the tasks autonomously. Here we describe an integrated system that achieves this goal. The automatic controller is based on real-time model-predictive control (MPC) applied to the full dynamics of the robot. This is possible due to the speed of our new physics engine (MuJoCo), the efficiency of our trajectory optimization algorithm, and the contact smoothing methods we have developed for the purpose of control optimization. In our system, the operator specifies subtasks by selecting from a menu of predefined cost functions, and optionally adjusting the mixing weights of the different cost terms in runtime. The resulting composite cost is sent to the MPC machinery which constructs a new locally-optimal time-varying linear feedback control law once every 30 msec, while planning 500 msec into the future. This control law is evaluated at 1 kHz to generate control signals for the robot, until the next control law becomes available. Performance is illustrated on a subset of the tasks from the DARPA Virtual Robotics Challenge.


title: Stabilizing function of the diaphragm: dynamic MRI and synchronized spirometric assessment

abstract: The aim was to describe diaphragmatic behavior during postural limb activities and examine the ventilatory and stabilizing functions of the diaphragm. Thirty healthy subjects were examined in the supine position using a dynamic MRI system assessed simultaneously with specialized spirometric readings. The diaphragmatic excursions (DEs) were measured at three diaphragmatic points in the sagittal plane; the diaphragm positions (DPs) as related to a reference horizontal baseline were determined. Measurements were taken during tidal breathing (TB) and isometric flexion of upper or lower extremities against external resistance together with TB. Mean DE in both upper and lower postural limb activities was greater compared with the TB condition (P < 0.05), with the effect greater for lower limb activities. Inspiratory DPs in the upper and lower extremity activities were lower compared with TB alone (P < 0.01). Expiratory DP was lower only for lower extremity activities (P < 0.01). DP was most affected at the apex of the crescent and crural (posterior) portion of the diaphragm. DEs correlated strongly with tidal volume (Vt) in all conditions. Changes in DEs relative to the initial value were minimal for upper and lower extremities but were related to lower values of Vt (P < 0.03). Significant involvement of the diaphragm in the limb postural activities was found. Resulting DEs and DPs differed from the TB conditions, especially in lower extremity activities. The differences between the percent changes of DEs vs. Vt found for lower extremity activities were confirmed by both ventilatory and postural diaphragm recruitment in response to postural demands.


title: Spatio-temporal Gaussian Mixture Model for Background Modeling

abstract: Background subtraction is widely employed in the detection of moving objects when background does not show much dynamic behavior. Many background models have been proposed by researchers. Most of them analyses only temporal behavior of pixels and ignores spatial relations of neighborhood that may be a key to better separation of foreground from background when background has dynamic activities. To remedy, some researchers proposed spatio-temporal approaches usually in the block-based framework. Two recent reviews[1, 2] showed that temporal kernel density estimation(KDE) method and temporal Gaussian mixture model(GMM) perform about equally best among possible temporal background models. Spatio-temporal version of KDE was proposed. However, for GMM, explicit extension to spatio-temporal domain is not easily seen in the literature. In this paper, we propose an extension of GMM from temporal domain to spatio-temporal domain. We applied the methods to well known test sequences and found that the proposed outperforms the temporal GMM.


title: Ground reaction forces during downhill and uphill running.

abstract: Abstract We investigated the normal and parallel ground reaction forces during downhill and uphill running. Our rationale was that these force data would aid in the understanding of hill running injuries and energetics. Based on a simple spring-mass model, we hypothesized that the normal force peaks, both impact and active, would increase during downhill running and decrease during uphill running. We anticipated that the parallel braking force peaks would increase during downhill running and the parallel propulsive force peaks would increase during uphill running. But, we could not predict the magnitude of these changes. Five male and five female subjects ran at 3 m/s on a force treadmill mounted on the level and on 3°, 6°, and 9° wedges. During downhill running, normal impact force peaks and parallel braking force peaks were larger compared to the level. At −9°, the normal impact force peaks increased by 54%, and the parallel braking force peaks increased by 73%. During uphill running, normal impact force peaks were smaller and parallel propulsive force peaks were larger compared to the level. At +9°, normal impact force peaks were absent, and parallel propulsive peaks increased by 75%. Neither downhill nor uphill running affected normal active force peaks. Combined with previous biomechanics studies, our normal impact force data suggest that downhill running substantially increases the probability of overuse running injury. Our parallel force data provide insight into past energetic studies, which show that the metabolic cost increases during downhill running at steep angles.


Churn Prediction in Telecommunications Using MiningMart