Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Speed Performance Operation of Induction Motors Drives Using Low-Resolution Speed Sensor

In this paper, indirect field oriented control (IFOC) of induction motor at very low speed range (1-5 rpm) using a low-count encoder is presented. To test the lowest possible pulse encoder that gives both satisfactory performance for IFOC algorithm and precise enough speed information, a low-count encoder implementation algorithm is developed. This algorithm converts high-resolution encoder data to various number of pulse low-count encoders. Using this algorithm, different pulse encoders are tested and the lowest possible pulse encoder is selected for IFOC implementation due to the cost limitation. Furthermore, solutions to computational problems of microprocessing unit associated with low speed measurement techniques are given. These contributions are experimentally tested and successfully applied to digital signal processor (DSP)- based (TMS320F28xx) ac drives used in traction applications.
A Digitally Corrected 5-mW 2-MS/s SC  $\Delta\Sigma$ ADC in 0.25- $\mu$m CMOS With 94-dB SFDR

A digital correction scheme that allows a switched-capacitor (SC) ΔΣ ADC to operate with significantly reduced power consumption is proposed. As power dissipation is reduced in the integrators, nonlinear settling errors cause increasing harmonic distortion. The correction technique uses a polynomial approximation to correct the nonlinearity and reduce distortion in the post-filtered digital output. With correction, experimental results yield a peak SNDR of 75 dB, a THD of -90 dB and a SFDR of 94 dB. The total analog power dissipation of the corrected modulator is 5 mW at 2.4 V, saving 38% over a similarly performing uncorrected modulator output. The active area is 0.39 mm2 in 0.25-μ m CMOS.
Chemical Constituents from the Stems of Excoecaria acertiflia

Six new compounds, including two diterpenoids excocarinols F and G (1 and 2, resp.), two carotane (daucane) sesquiterpenoids excoecafolinols A and B (3 and 4, resp.), one lignanoid compound, excoecanol A (5), and one simple phenol, excoecanol B (6), together with 17 known compounds, were isolated from the BuOH extract of Excoecaria acerifolia Didr. stems. Their structures were elucidated through the analysis of the spectroscopic data. The AChE‐inhibitory activities of 17 compounds were evaluated and revealed that four of them possessed moderate inhibitory activities against AChE.
CtIP Silencing as a Novel Mechanism of Tamoxifen Resistance in Breast Cancer

Acquired resistance to the antiestrogen tamoxifen constitutes a major clinical challenge in breast cancer therapy. However, the mechanisms involved are still poorly understood. Using serial analysis of gene expression, we identified CtIP, a BRCA1- and CtBP-interacting protein, as one of the most significantly down-regulated transcripts in estrogen receptor α–positive (ER+) MCF-7 tamoxifen-resistant breast cancer cells. We further confirmed the association of CtIP down-regulation with tamoxifen resistance in an additional ER+ breast cancer line (T47D), strengthening the relevance of the phenomenon observed. In additional studies, we found CtIP protein expression in a majority of ER+ breast cancer cell lines that we tested, but no or very little CtIP expression in ER-negative lines. Furthermore, CtIP protein expression status correlates with clinical response to neoadjuvant endocrine therapy, and patients with progressive disease express significantly lower CtIP protein in their primary breast carcinomas than those who respond. Meta-analysis of seven publicly available gene expression microarray data sets showed that CtIP expression is significantly associated with ER, disease-free survival, and breast cancer metastasis status. Importantly, we found that silencing endogenous CtIP in tamoxifen-sensitive breast cancer cells confers tamoxifen resistance. On the other hand, reexpression of CtIP in tamoxifen-resistant breast cancer cells restores sensitivity to the inhibitory growth effects of tamoxifen. Together, our findings indicate that CtIP silencing might be a novel mechanism for the development of tamoxifen resistance in breast cancer, suggesting that CtIP is likely associated with ER function, and that CtIP gene and protein expression may be useful biomarkers for breast cancer prognosis and clinical management. (Mol Cancer Res 2007;5(12):1285–95)
Autoregulation of MARCH1 Expression by Dimerization and Autoubiquitination

Some members of the membrane-associated RING-CH family of E3 ubiquitin ligases (MARCHs) are membrane-bound and target major players of the immune response. MARCH1 ubiquitinates and downregulates MHC class II expression in APCs. It is induced by IL-10 and despite a strong increase in mRNA expression in human primary monocytes, the protein remains hardly detectable. To gain insights into the posttranslational regulation of MARCH1, we investigated whether its expression is itself regulated by ubiquitination. Our results demonstrate that MARCH1 is ubiquitinated in transfected human cell lines. Polyubiquitin chain-specific Abs revealed the presence of K48-linked polyubiquitin chains. A mutant devoid of lysine residues in the N- and C-terminal regions was less ubiquitinated and had a prolonged half-life. Reduced ubiquitination was also observed for an inactive mutated form of the molecule (M1WI), suggesting that MARCH1 is capable of autoubiquitination. Immunoprecipitation and energy transfer experiments demonstrated that MARCH1 homodimerizes and also forms heterodimers with others family members. Coexpression of MARCH1 decreased the protein levels of the inactive M1WI, suggesting a transubiquitination process. Taken together, our results suggest that MARCH1 may regulate its own expression through dimerization and autoubiquitination.
Movement synchronization fails during non-adaptive human-robot interaction

Interpersonal movement synchronization is a phenomenon that does not only increase the predictability of movements; it also increases rapport among people. In this line, synchronization might enhance human-robot interaction. An experiment is presented which explores to what extend a human synchronizes own movements to a non-adaptive robot during a repetitive tapping task. It is shown that the human does not take over the complete effort of movement adaptation to reach synchronization, which indicates the need for adaptive robots.
Sign systems in special education: some experiences of their use with deaf children

The recent and striking growth in the use of sign-systems with communication-handicapped children other than the deaf has not been monitored by systematic research. Sign-systems have been in use with deaf children, however, for a considerable number of years and much research has been undertaken aimed at evaluating their effectiveness as a means of developing communication. This research is discussed, and the conclusion is that using signs with deaf children is not unproblematic. The findings may well be applicable to the use of signs with any communication- handicapped child. Thus enthusiasm for alternative communication approaches should not be mistaken for efficacy.
Collaborative localization of vehicle formations based on ranges and bearings

We examine the problem of jointly determining the positions of multiple underwater vehicles based on a set of pairwise range and bearing measurements taken over time. This extends prior work on the so-called (static) collaborative localization paradigm where a hybrid approach was proposed for seamless instantaneous fusion (i.e., no time dependence) of range and bearing measurements. To incorporate time we add to the original convexified least-squares cost function a regularizing term that penalizes deviations between predicted and computed vehicle positions at a given instant. The method operates progressively over time, with past estimates used for prediction at the current instant assuming a very simple quasilinear motion model. The method is amenable to parallelization, with simple gradient-like updates. Numerical results demonstrate promising accuracy gains (reduction on the order of 10 % in terms of root-mean-square positioning error) in simulations inspired by an underwater geoacoustic surveying application.
A Typology of Adolescent Drinking-Drivers

ABSTRACT Based on a 1993 survey of Ontario students in grades 7, 9, 11, and 13, data from students who reported driving within one hour of drinking two or more drinks are cluster analyzed. A 3-cluster typology was derived: “Marginals” consist of the lowest proportion of males, lowest delinquency, alcohol use, accident involvement, drinking-driving frequency and consequence experience; “Heavy Drinkers” consist of the greatest proportion of males, heaviest alcohol use, greatest driving exposure, highest drinking-driving frequency, few drinking-driving convictions and no drinking-driving accidents; and “Delinquents” consist of the highest delinquency, accident involvement, drinking-driving accident and conviction, and least driving exposure.
Why the Dynamics of Competition Matter for Category Profitability

Category management (CM) has become a widespread trade practice in recent years. A category manager's decision problem is complex and multifaceted owing to demand dependencies across products and across time. Extant research on CM has typically focused on one or the other of these dependencies, but seldom both. The authors address this research gap by presenting a competition framework that reconciles cross-sectional breadth (large numbers of stockkeeping units in any given period) with longitudinal depth (demand effects across time). The endeavor is to offer retailers a general, realistic, and practical CM approach by comprehensively accounting for competitive effects. The authors demonstrate their approach using real-world data in the beer category for a midsize grocery chain in the northeastern United States. After determining the optimal weekly prices for the entire assortment over 23 weeks, the authors report a profit yield that is 3.30% more than in the benchmark logit model and substantially more than in the retailer's current everyday low price policy.
A Model of Social Influence and Network Formation

This paper presents an agent-based model of self-organizing social networks. The affiliation between agents is mediated by agent attributes, or culture, that are subject to influence from other agents. The paper examines conditions in which distinct cultural and social subgroups form and characterize the model’s behavior with respect to both social structure and culture. A strong correspondence was found between clustering, or how tightly knit an agent’s affiliates are, and cultural homogeneity. The dynamics between the two are examined at individual, group, and global levels. In particular, it is observed that clustering is correlated with a high degree of assortative mixing amongst linked agents. Furthermore, the model gives rise to global cultural trends in which particular cultural values are pervasive among the entire population. Finally, the stability of cultural values at the aggregate and agent level are found to be closely related to the amount of clustering in the network. 1
Data Mining Approach to Implement a Recommendation System for Electronic Tour Guides

In this paper we consider the problem of discovering patterns generated by association rules between items in a database of locations that tourists have visited within the Manchester city centre area. The focus of the paper is on the interactive agenda offered by a tourist information system. This assist the user in the organisation of a visit or a tour by providing personalised recommendations dependent upon the calculations made by the association rule algorithm using the entries within a database of tourist attractions. The paper shows the development of a stand alone recommendation system, which uses association rule algorithm to propose and recommend any related locations that a particular tourist can visit based on the option they initially choose. This opens the door to take this implementation further to an extent where developing this system on a manufacturing scale is viable and be installed in major cities around the UK so that tourists can truly appreciate their time in cities like Manchester. Before such mass production can take place other data mining techniques such as clustering or temporal data mining are suitable extensions to such an application. This can offer further interactive options to tourists who use the application as a means of visiting major attractions and fulfilling their holidays.
Pooling Facial Segments to Face: The Shallow and Deep Ends

Generic face detection algorithms do not perform very well in the mobile domain due to significant presence of occluded and partially visible faces. One promising technique to handle the challenge of partial faces is to design face detectors based on facial segments. In this paper two such face detectors namely, SegFace and DeepSegFace, are proposed that detect the presence of a face given arbitrary combinations of certain face segments. Both methods use proposals from facial segments as input that are found using weak boosted classifiers. SegFace is a shallow and fast algorithm using traditional features, tailored for situations where real time constraints must be satisfied. On the other hand, DeepSegFace is a more powerful algorithm based on a deep convolutional neutral network (DCNN) architecture. DeepSegFace offers certain advantages over other DCNN-based face detectors as it requires relatively small amount of data to train by utilizing a novel data augmentation scheme and is very robust to occlusion by design. Extensive experiments show the superiority of the proposed methods, specially DeepSegFace, over other state-of-the-art face detectors in terms of precision-recall and ROC curve on two mobile face datasets.
Toward Spontaneous Interaction with the Perceptive Workbench 1

ers mostly by using wire-based devices. Typically, the wires limit the distance of movement and inhibit freedom of orientation. In addition, most interactions are indirect. The user moves a device as an analog for the action created in the display space. We envision an untethered interface that accepts gestures directly and can accept any objects we choose as interactors. In conventional 3D interaction, the devices that track position and orientation are still usually tethered to the machine by wires. Devices, such as pinch gloves, that permit the user to experience a more natural interface often don’t perform as well. Users generally prefer simple handheld devices with buttons. Pinch gloves carry assumptions about the position of the user’s hand and fingers with respect to the tracker. Of course, users’ hands differ in size and shape, so the assumed tracker position must be recalibrated for each user. This is hardly ever done. Also, the glove interface causes subtle changes to recognized hand gestures. The result is that fine manipulations can be imprecise, and the user comes away with the feeling that the interaction is slightly off in an indeterminate way. If we can recognize gestures directly, we take into account the difference in hand sizes and shapes. An additional problem is that any device held in the hand can become awkward while gesturing. We’ve found this even with a simple pointing device, such as a stick with a few buttons (see Figure 1). Also, unless fairly skilled, a user often has to pause to identify and select buttons on the stick. With accurately tracked hands, most of this awkwardness disappears. We’re adept at pointing in almost any direction and can quickly pinch fingers, for example, without looking at them. Finally, physical objects are often natural interactors (such as phicons). However, with current systems these objects must be inserted in advance or specially prepared. We’d like the system to accept objects that we choose spontaneously for interaction. In this article we discuss methods for producing more seamless interaction between the physical and virtual environments through the Perceptive Workbench. We applied the system to an augmented reality game and a terrain navigating system. The Perceptive Workbench can reconstruct 3D virtual representations of previously unseen real-world objects placed on its surface. In addition, the Perceptive Workbench identifies and tracks such objects as they’re manipulated on the desk’s surface and allows the user to interact with the augmented environment through 2D and 3D gestures. These gestures can be made on the plane of the desk’s surface or in the 3D space above the desk. Taking its cue from the user’s actions, the Perceptive Workbench switches between these modes automatically. Computer vision controls all interaction, freeing the user from the wires of traditional sensing techniques.
Towards a Thirdspace Approach to Tourism and Related Research

This paper explores new ways of conceptualizing and researching the Postmodern city, focusing in particular on Soja's (1996) development of a Thirdspace approach to the theorization of human and cultural geography and planning. Despite its shortcomings, his work arguably has important implications for multi-disciplinary fields of research such as tourism and regeneration. Thirdspace is what might be described as a ‘ludic’ postmodern construct (Kincheloe and McLaren 2000), which enables a researcher to (re)conceptualize the ways in which a city (or other environment) can be perceived, interpreted and represented. It affords the researcher a mechanism with which s (he) can make better sense of urban spatial developments, practices and representations. However, like many postmodern concepts, Thirdspace could be seen as overly theoretical and abstract. For example, Allmendinger (2001) questions its practical relevance to contemporary urban planning. Although Soja (1996) contests that Thirdspace thinking can lead to potentially emancipatory praxis, his own attempts at demonstrating its application tend to be somewhat eclectic and inconclusive. This paper, therefore, focuses on questioning the extent to which a Thirdspace approach can be applied to practical research contexts with tangible outcomes. Emphasis is placed on both policy-making and cultural planning within the framework of ‘Third Way’ politics in a UK-based case study of Maritime Greenwich.
A VLSI spike-driven dynamic synapse which learns only when necessary

We describe an analog VLSI circuit implementing spike-driven synaptic plasticity, embedded in a network of integrate-and-fire neurons. This biologically inspired synapse is highly effective in learning to classify complex stimuli in semi-supervised fashion. The circuits presented are designed in sub-threshold CMOS consuming extremely low power. The pulse-based neural network communicates with the outside world using the address event representation in an asynchronous fashion. We present measurements from a test chip, characterizing all the modules of the circuit and show how they match well with theoretical expectations. We finally demonstrate that the learning mechanism of the synapse is fully functional by stimulating it with Poisson distributed spike trains
Can Psychoanalysis Become Empirically Supported?

The article discusses the application of the ideas of evidence-based medicine or empirically supported treatment to the field of psychoanalysis. The author argues that empirical support is gradual rather than categorical, and that the randomized clinical trial (RCT), which is commonly heralded as the appropriate method of support, confounds patient suitability factors with treatment 'as such'. As a consequence of the exclusive reliance on the RCT, empirical validation of psychological treatments has generally favoured cognitive-behavioural treatments, although there is ample evidence - albeit non-RCT - for the beneficial effects of short-term and long-term psychoanalytic treatment. The author argues for a suitability matching approach to empirical validation, where each treatment is tested, as powerfully as feasible, on samples based on selfselection (patient's choice, clinical assignment, suitability judgments etc.). The paper concludes by referring to two recent studies, indicating that there is reason to evaluate alleged empirical support with caution.
Regulatory Mode and Preferred Leadership Styles: How Fit Increases Job Satisfaction

Four studies conducted in diverse organizational contexts examined preferences and fit between two regulatory modes, referred to as “locomotion” and “assessment” (Higgins, Kruglanski, & Pierro, 2003; Kruglanski, et al., 2000), and leadership styles practiced by supervisors over their subordinates. The locomotion mode constitutes the aspect of self-regulation that is concerned with movement from state to state, and the assessment mode constitutes the aspect of self-regulation that is concerned with making comparisons. The present studies consistently show that individuals high in locomotion prefer a “forceful” leadership style, represented by “coercive”, “legitimate”, and “directive” kinds of strategic influence, whereas individuals high in assessment prefer an “advisory” leadership style, represented by “expert”, “referent”, and “participative” kinds of strategic influence. Consistent with regulatory fit theory (Higgins, 2000), the job satisfaction of subordinates was found to be higher when the style of strategic influence practiced by their supervisor fit their regulatory mode orientation (high locomotion/“forceful” style; high assessment/“advisory” style).
Using rank propagation and Probabilistic counting for Link-Based Spam Detection

This paper describes a link-based technique for automating the detection of Web spam, that is, pages using deceptive techniques for obtaining an undeservedly high score in search engines. The problem of Web spam is widespread and difficult to solve, mostly due to the large size of the Web that makes many algorithms infeasible in practice. We propose spam detection techniques that only consider the link structure of Web, regardless of page contents. In particular, we compute statistics of the links in the vicinity of every Web page applying rank propagation and probabilistic counting over the Web graph. These statistical features are used to build a classifier that is tested over a large collection of Web link spam. After ten-fold cross-validation, our best classifier can detect about 80% of the spam hosts with a rate of false positives of 2%. This is competitive with state-of-the-art spam classifiers that use content attributes.
When Strong Evidence Has Less Impact Than Weak Evidence: Bias, Adjustment, and Instructions to Ignore

Results from three experiments show that individuals who are asked to ignore a strong testimony “overadjust” their judgments relative to those asked to ignore testimony with a milder persuasive impact. Participants made jury decisions after reading several testimonies, one of which provided either strong or weak evidence for the defendant's guilt. Relative to the weak testimony, the strong testimony led to higher judgments of guilt when participants were instructed to use it, but lower judgments of guilt when participants were instructed to ignore it. Strength of the critical testimony was manipulated through the confidence of the witness (Experiment 1), linguistic cues to the witness's age (Experiment 2), and the abnormality of the defendant's actions (Experiment 3). Results are interpreted as supporting previous theories about awareness of cognitive processes and how individuals adjust beliefs.