Miracle-dz/UniFAR-FTUs huggingface dataset:
An evaluation of Nearest Neighbor Images­to­ Classes versus Nearest Neighbor Images­to­Images

In 2008, Boiman et al. announced in their work "In defense of Nearest-Neighbor based image classification" an interesting point that the Nearest-Neighbor-based classifiers have been considerably undervalued. The inferior performance of most NN-based classifiers was not due to the nature of the classifier, but was mainly the consequence of using it in a mistakenly perceived context. The authors claimed that instead of finding NN-fromimages-to-images, finding NN-from-imagesto-classes significantly improves the accuracy, in their experiments up to 17%. A simple fix to the way NN was used has brought it to be among the best classifiers. In the process of implementing the Leaf Recognition project, we had a chance to revalidate the above finding. We implemented both methods and tested on a big dataset of about 5,000 images. Against our expectation that the recommended NN-images-to-classes would greatly outperform the other, it actually insignificantly improves the accuracy from 70.2% to 70.4%. Our results versus their results raises the need for more investigation before confirming the robustness of the above finding.
A visual tracking strategy using Computer Graphics and edge

In this paper we have proposed a visual tracking strategy for 3D object recognition and tracking in robot vision applications. We adopt a Coarse-to-Fine combination strategy for 3D object tracking. In the coarse step, compared with those edge-based only methods, the convergence range of initial camera pose estimation has been enlarged in our system by a template-based matching between a series of template images rendered from Computer Graphics (CG) and a current image. In the fine step an edge-based object tracking method is used to realize a more accurate visual tracking application with the results from the previous coarse step. The implementation of this strategy is introduced in detail. The experiments results show that this combination strategy is effective for tracking real objects in robot vision applications.
Working Paper No. 2011/22 Aid and Growth What Meta-Analysis Reveals

Some recent literature in the meta-analysis category where results from a range of studies are brought together throws doubt on the ability of foreign aid to foster economic growth and development. This paper assesses what meta-analysis has to say about the effectiveness of foreign aid in terms of the growth impact. We re-examine key hypotheses, and find that the effect of aid on growth is positive and statistically significant. This significant effect is genuine, and not an artefact of publication selection. We also show why our results differ from those published elsewhere.
Flexible models with evolving structure

A flexible model in the form of an artificial neural network (NN) with evolving structure (eNN) is represented in the paper in the form of the evolving fuzzy Takagi-Sugeno model. It falls into the same category of models as the recently introduced evolving rule-based (eR) models. The learning algorithm is incremental, unsupervised and is based on the on-line identification of Takagi-Sugeno type quasilinear models. Both eR and eNN differ from the other model schemes by their gradually evolving structure as opposed to the fixed structure models, in which only parameters are subject to optimization or adaptation. Essentially, it represents a Takagi-Sugeno model with gradually evolving set of rules, determined on-line. This approach has potential in both modeling and control using indirect learning mechanisms. Its computational efficiency is based on the non-iterative and recursive procedure, which combines a Kalman filter with proper initializations, and online unsupervised clustering. eNN has been tested with data from a real air-conditioning installation. Applications to real-time adaptive non-linear control, fault detection and diagnostics, performance analysis, time-series forecasting, knowledge extraction and accumulation, etc. are possible directions of their use in the future research.
On the location choice of newly arrived immigrants in Germany

The aging and the shrinkage of the population in Germany will come along with (regional) shortages in the labor force. Since immigration might mitigate the decline of the labor supply I study the initial location choice of newly arrived foreign-born using micro data from the German Socio-Economic Panel (1991-2004) as well as macro data from o¢ cial statistics. As a main result, I nd that migration networks in Western Germany have a signi cant impact on the probability of incoming foreign-born originating in the same source country to move to these ethnic clusters. In contrast, in Eastern Germany only concrete social ties between former and newly arrived foreignborn positively inuence the probability of incoming immigrants to locate in the same region. Keywords: Immigrants, Location Choice, Migration Networks, Discrete Choice Models JEL classi cation: C25, F22, J61 I thank Chad Carroll, Hien Truong and seminar participants at the Dresden University of Technology for helpful comments and discussion. yifo Institute for Economic Research, Branch Dresden, Einsteinstrasse 3, 01069 Dresden, Germany, Email: pohl@ifo.de, phone: +49 351 2647624
IL-based double close loop modelling and control for SDC systems

A double closed loop stochastic distribution modelling and control structure based on iterative learning (IL) is presented for non-Gaussian dynamical stochastic systems in this paper. Each of the outer loop and the inner loop iteration are called as BATCH and batch, respectively. The output probability density functions (PDFs) of the system are approximated by radial basis function neural network (RBFNN). Iterative learning method is applied to adjust the parameters (i.e. the centers and widths of RBFs ) of the RBFNN, and then a standard state-space model is constructed within each BATCH by the use of subspace method. Application the state-space model, an IL-based controller, which tunes the control input signals in terms of the shaping tracking error from last batch, is given in the inner loop of the system. A simulation case study is included to show the effectiveness of the proposed algorithm and encouraging results have been obtained.
The Connections Between Self-Monitoring and Theory of Mind

MILLER, JANE E. The Connections Between Self-Monitoring and Theory of Mind. Department of Psychology, March 2015. ADVISOR: DEBONO, KENNETH Previous research on the personality characteristic of self-monitoring has shown that high self-monitors constantly adjust their behavior to fit their environment while low self-monitors follow internal cues to keep their attitudes and behaviors consistent with one another. Theory of Mind is a person’s ability to connect unobservable mental and emotional states to one’s self and others, while understanding that other people may have different thoughts, attitudes and beliefs than one’s self that guides their behaviors. The current research examined a possible connection between self-monitoring and theory of mind. Participants completed a self-monitoring scale and two tasks evaluating their theory of mind. Participants who were high self-monitors had more correct answers on theory of mind tasks than participants who were low self-monitors. Low self-monitors showed more theory of mind deficits than high self-monitors, suggesting that high self-monitors have more intact theory of minds. The research therefore provides evidence of a previously unknown connection between selfmonitoring and theory of mind, that may be an explanation for individual differences in self-monitoring. Self-Monitoring and Theory of Mind 2
A new Rational Conformal Field Theory extension of the fully degenerate W_{1+\infty}^{(m)}

We found new identities among the Dedekind eta-function, the characters of the W_{m} algebra and those of the level 1 affine Lie algebra su(m)_{1}. They allow to characterize the Z_{m}-orbifold of the m-component free bosons u(1)_{K_{m,p}} (our theory TM) as an extension of the fully degenerate representations of W_{1+infty}^{(m)}. In particular, TM is proven to be a Gamma _{theta}-RCFT extension of the chiral fully degenerate W_{1+infty}^{(m)}.
XSEM - A Conceptual Model for XML

We propose a new conceptual model for XML data called XSEM as a combination of several approaches in the area of the conceptual modeling for XML. The model divides the conceptual modeling process of XML data to two levels. On the first level, a designer designs an overall non-hierarchical conceptual schema of a domain. On the second level, he or she derives different hierarchical representations of parts of the overall conceptual schema using transformation operators. These hierarchical representations describe how the data is organized in an XML form.
A Novel Dynamic Current Boosting Technique for Enhancement of Settling Time and Elimination of Slewing of CMOS Amplifiers

A very simple technique to achieve low settling time is presented. It is based on the combination of class AB differential input stages, local common-mode feedback (LCMFB), and clamping circuit which provides additional current boosting, keeping the gain-bandwidth product (GBW) nearly constant. The slew enhancement is provided by an auxiliary circuit which is activated only during transients. The design is based on the “TSMC 180nm CMOS technology”.
The effect of cylinder rotation and blockage ratio on the onset of periodic flows

The steady two-dimensional laminar flow past a stationary cylinder is well known to lose stability to a periodic flow at a supercritical Hopf bifurcation point as the flow rate is increased. It is less well known that the critical flow rate at which the instability occurs can be increased by rotating the cylinder about is own axis, and that at a fixed flow rate the vortex street can be eliminated entirely by sufficiently large rotation. We confine the flow between two parallel walls and report the effect of cylinder rotation on the Reynolds number and frequency at the Hopf bifurcation point. Two new, and somewhat surprising, results are that for stationary cylinders at large blockage ratios, the steady symmetric flow can restabilize above a critical Reynolds number, and that steady asymmetric flows exist.
msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding

Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework fails to account for the substantial variation in the DNase I cleavage profiles across different binding sites. Neither does it account for variation in the profiles at the same binding site across multiple replicate DNase I experiments, which are increasingly available. In this work, we introduce new methods, based on multi-scale models for inhomogeneous Poisson processes, to account for such variation in DNase I cleavage patterns both within and across binding sites. These models account for the spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-seq peaks for those factors. Finally, we explore the effects of DNase I sequence bias on inference of factor binding using a simple extension to our framework that allows for a more flexible background model. The proposed model can also be easily applied to paired-end ATAC-seq and DNase-seq data. msCentipede, a Python implementation of our algorithm, is available at http://rajanil.github. io/msCentipede.
Nitrogen fixation associated with Suillus tomentosus tuberculate ectomycorrhizae on Pinus contorta var. latifolia.

BACKGROUND AND AIMS Tuberculate ectomycorrhizae are a unique form of ectomycorrhiza where densely packed clusters of mycorrhizal root tips are enveloped by a thick hyphal sheath to form a tubercle. The functional significance of such a unique structure has not previously been established. The purpose of the present study was to investigate and measure the potential nitrogenase activity associated with Suillus tomentosus/Pinus contorta tuberculate ectomycorrhizae in two stand ages, young and old, and across a range of nitrogen-poor soil conditions. METHODS Short roots were compared with other mycorrhizae and non-mycorrhizal secondary roots using tuberculate ectomycorrhizae. Assessment of nitrogenase activity was determined and quantitative measurements were taken on tuberculate ectomycorrhizae in situ in a variety of different circumstances, by using an adaptation of the acetylene reduction assay. KEY RESULTS Significant nitrogenase activity was measured associated with S. tomentosus/P. contorta tuberculate ectomycorrhizae whereas no nitrogenase activity was measured with non-tuberculate mycorrhizae or secondary roots without mycorrhizae. Average nitrogenase activity ranged from undetectable to 5696.7 nmol C2H4 g(-1) tubercle 24 h(-1). Maximum nitrogenase activity was 25,098.8 nmol C2H4 g(-1) tubercle 24 h(-1). Nitrogenase activity was significantly higher in young stands than in old stands of P. contorta. Season or some covariate also seemed to affect nitrogenase activity and there was suggestion of a site effect. CONCLUSIONS Suillus tomentosus/P. contorta tuberculate ectomycorrhizae are sites of significant nitrogenase activity. The nitrogenase activity measured could be an important contribution to the nitrogen budget of P. contorta stands. Season and stand age affect levels of nitrogenase activity.
Understanding the benefits of game jams: Exploring the potential for engaging young learners in STEM

There is a wide range of implementations of game jams throughout the world. Game jams have been organized in a number of different formats, themes, and timeframes [43]. What they all have in common is the opportunity for participants to make a game within a specified constraint such as time, location, technology, or theme. Additionally, game jams as social experience support active and collaborative learning formats. In this paper, we discuss the potential of game jams for young learners, describe successful jam events in this context, and provide a list of tools useful for organizing game jams for this target group.
Force control for tissue tensioning in precise robotic laser surgery

Lasers are being used in various surgical procedures to remove tissue or bones, to coagulate vessels or other structures. Due to difficulties in handling only a limited number of surgeons manage to display sufficient levels of precision in Minimally Invasive Surgery (MIS) procedures. Prior works on robotic laser surgery demonstrated shorter learning curves and higher ablation precision, but unfortunately ignored the fact that most clinically relevant tasks are bi-manual by nature. Surgeons are also reluctant to use current commercial surgical robotic systems for complex laser tasks, indicating that the lack of haptic feedback prevents them from efficient and safe tissue handling in preparation of laser treatment. This paper expands earlier robotic laser work towards bi-manual operation. The paper introduces a system for precisely tensioning tissue that is being targeted by the laser. An artificial test-setup that captures some essential features of bimanual laser surgery is described. Experiments have been conducted to investigate the effect of haptic feedback on ablation performance. A comparison is made of achievable levels of ablation precision when there is no haptic feedback, when there is haptic feedback and when an automatic tension control algorithm is deployed. The conducted experimental results confirm the great potential of haptic feedback and automatic tensioning systems for complex bi-manual lasering tasks.
Performance evaluation for data reduction techniques of hyperspectral images

Hyperspectral image contains information as a set of contiguous spectral wavelength bands. These images are three dimensional images having up to two hundred bands or more. Although hyperspectral image contain hundreds of bands, a few number of bands can explain the vast majority of the information. Hence, hyperspectral image is transformed into lower dimension by preserving the main features of the original data using dimension reduction techniques, which eliminate data redundancy. In order to reduce the complexity and the time taken for processing of the hyperspectral images, Principal Component Analysis (PCA) is used to reduce the dimensionality while retaining all important features of the image. In this paper, PCA and Noise Adjusted Principal Component Analysis (NAPCA) have been simulated in the area of hyperspectral image processing. Experiments were carried out on three different types of aerial hyperspectral images having different spectral and spatial resolutions. An elaborate performance evaluation for these data reduction techniques has been carried out by taking image quality parameters such as Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE). Image quality parameters are calculated using original hyperspectral image and reconstructed images of PCA and NAPCA.
Clinical and Immunological Markers of Dengue Progression in a Study Cohort from a Hyperendemic Area in Malaysia

Background With its elusive pathogenesis, dengue imposes serious healthcare, economic and social burden on endemic countries. This study describes the clinical and immunological parameters of a dengue cohort in a Malaysian city, the first according to the WHO 2009 dengue classification. Methodology and Findings This longitudinal descriptive study was conducted in two Malaysian hospitals where patients aged 14 and above with clinical symptoms suggestive of dengue were recruited with informed consent. Among the 504 participants, 9.3% were classified as non-dengue, 12.7% without warning signs, 77.0% with warning signs and 1.0% with severe dengue based on clinical diagnosis. Of these, 37% were misdiagnosed as non-dengue, highlighting the importance of both clinical diagnosis and laboratory findings. Thrombocytopenia, prolonged clotting time, liver enzymes, ALT and AST served as good markers for dengue progression but could not distinguish between patients with and without warning signs. HLA-A*24 and -B*57 were positively associated with Chinese and Indians patients with warning signs, respectively, whereas A*03 may be protective in the Malays. HLA-A*33 was also positively associated in patients with warning signs when compared to those without. Dengue NS1, NS2A, NS4A and NS4B were found to be important T cell epitopes; however with no apparent difference between with and without warning signs patients. Distinction between the 2 groups of patients was also not observed in any of the cytokines analyzed; nevertheless, 12 were significantly differentially expressed at the different phases of illness. Conclusion The new dengue classification system has allowed more specific detection of dengue patients, however, none of the clinical parameters allowed distinction of patients with and without warning signs. While the HLA-A*33 may be predictive marker for development of warning signs; larger studies will be needed to support this findings.
Dictionary Learning Based Hashing for Cross-Modal Retrieval

Recent years have witnessed the growing popularity of cross-modal hashing for fast multi-modal data retrieval. Most existing cross-modal hashing methods project heterogeneous data directly into a common space with linear projection matrices. However, such scheme will lead to large error as there will probably be some heterogeneous data with semantic similarity hard to be close in latent space when linear projection is used. In this paper, we propose a dictionary learning cross-modal hashing (DLCMH) to perform cross-modal similarity search. Instead of projecting data directly, DLCMH learns dictionaries and generates sparse representation for each instance, which is more suitable to be projected to latent space. Then, it assumes that all modalities of one instance have identical hash codes, and gets final binary codes by minimizing quantization error. Experimental results on two real-world datasets show that DLCMH outperforms or is comparable to several state-of-the-art hashing models.
Transference and countertransference: Opportunities and risks as two technical constructs migrate beyond their psychoanalytic homeland

The terms 'transference' and 'countertransference' have developed a wide use in psychotherapy and no longer hold an exclusive association with therapies based on psychoanalysis. These terms have become so commonplace there is a real risk that therapists will gloss over the complex, and even problematic, nature of the underlying constructs. Without an appreciation of this complexity, clinical use of the terms may result in more harm than good. Robert King and Tom O'Brien return to the psychoanalytic meaning of the terms in order to minimise this danger. Regardless of the therapy framework, emotional responses of clients and therapists to each other can inform the work of therapy, and enhance the impact of therapeutic communication. However, transference and countertransference, when identified correctly, will only constitute a small part of the feelings that exist between therapist and client. Interpretation of transference and countertransference will be subject to errors and distortions, and these need to be identified and managed for effective use of these two technical constructs.
Directed test suite augmentation: an empirical investigation

Test suite augmentation techniques are used in regression testing to identify code elements in a modified program that are not adequately tested and to generate test cases to cover those elements. A defining feature of test suite augmentation techniques is the potential for reusing existing regression test suites. Our preliminary work suggests that several factors influence the efficiency and effectiveness of augmentation techniques that perform such reuse. These include the order in which target code elements are considered while generating test cases, the manner in which existing regression test cases and newly generated test cases are used, and the algorithm used to generate test cases. In this work, we present the results of two empirical studies examining these factors, considering two test case generation algorithms (concolic and genetic). The results of our studies show that the primary factor affecting augmentation using these approaches is the test case generation algorithm utilized; this affects both cost and effectiveness. The manner in which existing and newly generated test cases are utilized also has a substantial effect on efficiency and in some cases a substantial effect on effectiveness. The order in which target code elements are considered turns out to have relatively few effects when using concolic test case generation but in some cases influences the efficiency of genetic test case generation. The results of our first study, on four relatively small programs using a large number of test suites, are supported by our second study of a much larger program available in multiple versions. Together, the studies reveal a potential opportunity for creating a more cost‐effective hybrid augmentation approach leveraging both concolic and genetic test case generation techniques, while appropriately utilizing our understanding of the factors that affect them. Copyright © 2014 John Wiley & Sons, Ltd.