Miracle-dz/UniFAR-FTUs huggingface dataset:
Shape-Based Human Detection and Segmentation via Hierarchical Part-Template Matching

We propose a shape-based, hierarchical part-template matching approach to simultaneous human detection and segmentation combining local part-based and global shape-template-based schemes. The approach relies on the key idea of matching a part-template tree to images hierarchically to detect humans and estimate their poses. For learning a generic human detector, a pose-adaptive feature computation scheme is developed based on a tree matching approach. Instead of traditional concatenation-style image location-based feature encoding, we extract features adaptively in the context of human poses and train a kernel-SVM classifier to separate human/nonhuman patterns. Specifically, the features are collected in the local context of poses by tracing around the estimated shape boundaries. We also introduce an approach to multiple occluded human detection and segmentation based on an iterative occlusion compensation scheme. The output of our learned generic human detector can be used as an initial set of human hypotheses for the iterative optimization. We evaluate our approaches on three public pedestrian data sets (INRIA, MIT-CBCL, and USC-B) and two crowded sequences from Caviar Benchmark and Munich Airport data sets.
Effects of a Multidisciplinary Educational Rehabilitative Intervention in Breast Cancer Survivors: The Role of Body Image on Quality of Life Outcomes

In breast cancer survivors, own body image may change due to physical and psychological reasons, worsening women's living. The aim of the study was to investigate whether body image may affect the functional and quality of life outcomes after a multidisciplinary and educational rehabilitative intervention in sixty women with primary nonmetastatic breast cancer who have undergone conservative surgery. To assess the quality of life was administered The European Organization for Research and Treatment of Cancer Study Group on Quality of Life core questionnaire, while to investigate the psychological features and self-image were administered the following scales: the Body Image Scale, the Hamilton Rating Scale for Depression, and the State-Trait Anxiety Inventory. To assess the recovery of the function of the shoulder were administered: the Disabilities of the Arm, Shoulder, and Hand Questionnaire and the Constant-Murley Score. Data were collected at the baseline, at the end of the intervention, and at 3-month follow-up. We found a general improvement in the outcomes related to quality of life, and physical and psychological features after treatment (P < 0.001). During follow-up period, a higher further improvement in women without alterations in body image in respect of those with an altered self-perception of their own body was found (P = 0.01). In conclusion, the body image may influence the efficacy of a rehabilitative intervention, especially in the short term of follow-up.
Accelerating Pipelined Integer and Floating-Point Accumulations in Configurable Hardware with Delayed Addition Techniques

The speed of arithmetic calculations in configurable hardware is limited by carry propagation, even with the dedicated hardware found in recent FPGAs. This paper proposes and evaluates an approach called delayed addition that reduces the carry-propagation bottleneck and improves the performance of arithmetic calculations. Our approach employs the idea used in Wallace trees to store the results in an intermediate form and delay addition until the end of a repeated calculation such as accumulation or dot-product; this effectively removes carry propagation overhead from the calculation's critical path. We present both integer and floating-point designs that use our technique. Our pipelined integer multiply-accumulate (MAC) design is based on a fairly traditional multiplier design, but with delayed addition as well. This design achieves a 72 MHz clock rate on an XC4036xla-9 FPGA and 170 MHz clock rate on an XV300epq240-8 FPGA. Next, we present a 32-bit floating-point accumulator based on delayed addition. Here, delayed addition requires a novel alignment technique that decouples the incoming operands from the accumulated result. A conservative version of this design achieves a 40 MHz clock rate on an XC4036xla-9 FPGA and 97 MHz clock rate on an XV100epq240-8 FPGA. We also present a 32-bit floating-point accumulator design with compiler-managed overflow avoidance that achieves a 80 MHz clock rate on an XC4036xla-9 FPGA and 150 MHz clock rate on an XCV100epq240-8 FPGA.
Real-time Data Assimilation for Chaotic Storm Surge Model Using NARX Neural Network

Siek, M. and Solomatine, D.P., 2011. Real-time data assimilation for chaotic storm surge model using NARX neural network. Journal of Coastal Research, SI 64 (Proceedings of the 11th International Coastal Symposium), 1189 – 1194. Szczecin, Poland, ISSN 0749-0208 This paper introduces a real-time data assimilation technique where Nonlinear AutoRegressive with eXogenous inputs (NARX) neural network is used to re-analyze and improve chaotic model forecasts. The chaotic model is built using adaptive local models based on the dynamical neighbors in the reconstructed phase space of the observables. The NARX data assimilation can perform in nearly real-time process since it does not require a lot of computation in comparison to variational or sequential data assimilation methods. This proposed method was implemented and tested for assimilating chaotic storm surge models for the North Sea. The results show that the chaotic storm surge model with data assimilation using NARX neural network has typically more accurate forecasts than the chaotic storm surge model without data assimilation, and than other European operational numerical storm surge models. ADDITIONAL INDEX WORDS: Storm surge dynamics, Phase space reconstruction, Adaptive local model, Real-time data assimilation, NARX neural network, European operational storm surge models
Self-organized learning model for behavior emergence and its application to mobile robot

In this paper, we propose a self-organized learning model that can generate behaviors for successfully performing various tasks, unlike conventional systems that could perform only a limited task in a limited environment. The model memorizes various relationships between changes in sensory information and a motor command through learning. After the learning, the model can perform various tasks by generating the various behaviors automatically. We investigated the performance of the model by applying it to a mobile robot simulation. In the simulation, we applied two different tasks to the model, which performed the same learning. The results indicate that suitable behaviors for all the tasks emerged spontaneously.
Corrected-Moment Illuminant Estimation

Image colors are biased by the color of the prevailing illumination. As such the color at pixel cannot always be used directly in solving vision tasks from recognition, to tracking to general scene understanding. Illuminant estimation algorithms attempt to infer the color of the light incident in a scene and then a color cast removal step discounts the color bias due to illumination. However, despite sustained research since almost the inception of computer vision, progress has been modest. The best algorithms - now often built on top of expensive feature extraction and machine learning - are only about twice as good as the simplest approaches. This paper, in effect, will show how simple moment based algorithms - such as Gray-World - can, with the addition of a simple correction step, deliver much improved illuminant estimation performance. The corrected Gray-World algorithm maps the mean image color using a fixed (per camera) 3x3 matrix transform. More generally, our moment approach employs 1st, 2nd and higher order moments - of colors or features such as color derivatives - and these again are linearly corrected to give an illuminant estimate. The question of how to correct the moments is an important one yet we will show a simple alternating least-squares training procedure suffices. Remarkably, across the major datasets - evaluated using a 3-fold cross validation procedure - our simple corrected moment approach always delivers the best results (and the performance increment is often large compared with the prior art). Significantly, outlier performance was found to be much improved.
Evolutionary relationships within the lamioid tribe Synandreae (Lamiaceae) based on multiple low-copy nuclear loci

The subfamily Lamioideae (Lamiaceae) comprises ten tribes, of which only Stachydeae and Synandreae include New World members. Previous studies have investigated the phylogenetic relationships among the members of Synandreae based on plastid and nuclear ribosomal DNA loci. In an effort to re-examine the phylogenetic relationships within Synandreae, the current study incorporates data from four low-copy nuclear loci, PHOT1, PHOT2, COR, and PPR. Our results confirm previous studies based on chloroplast and nuclear ribosomal markers in supporting the monophyly of tribe Synandreae, as well as sister relationships between Brazoria and Warnockia, and between that pair of genera and a monophyletic Physostegia. However, we observe incongruence in the relationships of Macbridea and Synandra. The placement of Synandreae within Lamioideae is poorly resolved and incongruent among different analyses, and the sister group of Synandreae remains enigmatic. Comparison of the colonization and migration patterns corroborates a single colonization of the New World by Synandreae during the Late Miocene/Tortonian age. This is in contrast to the only other lamioid tribe that includes New World members, Stachydeae, which colonized the New World at least twice—during the mid-Miocene and Pliocene. Edaphic conditions and intolerance of soil acidity may be factors that restricted the distribution of most genera of Synandreae to southeastern and south–central North America, whereas polyploidy could have increased the colonizing capability of the more wide-ranging genus, Physostegia.
Assembly output codes for learning neural networks

Neural network-based classifiers usually encode the class labels of input data via a completely disjoint code, i.e. a binary vector with only one bit associated with each category. We use coding theory to propose assembly codes where each element is associated with several classes, making for better target vectors. These codes emulate the combination of several classifiers, which is a well-known method to improve decision accuracy. Our experiments on data-sets such as MNIST with a multi-layer neural network show that assembly output codes, which are characterized by a higher minimum Hamming distance, result in better classification performance. These codes are also well suited to the use of clustered clique-based networks in category representation.
Orientation and 3D modelling from markerless terrestrial images: combining accuracy with automation

In this paper an automated methodology is presented (i) to orient a set of close‐range images captured with a calibrated camera, and (ii) to extract dense and accurate point clouds starting from the estimated orientation parameters. The whole procedure combines different algorithms and techniques in order to obtain accurate 3D reconstructions in an automatic way. The exterior orientation parameters are estimated using a photogrammetric bundle adjustment with the image correspondences detected using area‐ and feature‐based matching algorithms. Surface measurements are then performed using advanced multi‐image matching techniques based on multiple image primitives. To demonstrate the reliability, precision and robustness of the procedure, several tests on different kinds of free‐form objects are illustrated and discussed in the paper. Three‐dimensional comparisons with range‐based data are also carried out.
An Integrated Healthcare System for Personalized Chronic Disease Care in Home–Hospital Environments

Facing the increasing demands and challenges in the area of chronic disease care, various studies on the healthcare system which can, whenever and wherever, extract and process patient data have been conducted. Chronic diseases are the long-term diseases and require the processes of the real-time monitoring, multidimensional quantitative analysis, and the classification of patients' diagnostic information. A healthcare system for chronic diseases is characterized as an at-hospital and at-home service according to a targeted environment. Both services basically aim to provide patients with accurate diagnoses of disease by monitoring a variety of physical states with a number of monitoring methods, but there are differences between home and hospital environments, and the different characteristics should be considered in order to provide more accurate diagnoses for patients, especially, patients having chronic diseases. In this paper, we propose a patient status classification method for effectively identifying and classifying chronic diseases and show the validity of the proposed method. Furthermore, we present a new healthcare system architecture that integrates the at-home and at-hospital environment and discuss the applicability of the architecture using practical target services.
Physical and chemical properties of the regional mixed layer of Mexico's Megapolis Part II: evaluation of measured and modeled trace gases and particle size distributions

This study extends the work of Baumgardner et al. (2009) in which measurements of trace gases and par- ticles, at a remote, high altitude mountain site, 60 km from Mexico City were analyzed with respect to the origin of the air masses. In the current evaluation, the temperature, water vapor mixing ratio (WMR), ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2) and acyl peroxy nitrate (APN) are simulated with the WRF-Chem chemical transport model and compared with the measurements at the mountain site. Comparisons between the model and measurements are also evaluated for particle size distributions (PSDs) of the mass concentrations of sulfate, nitrate, ammonium and organic mass (OM). The model predictions of the diurnal trends in temperature, WMR and trace gases were generally well cor- related; 13 of the 18 correlations were significant at a con- fidence level of <0.01. Less satisfactory were the average hourly differences between model and measurements that showed predicted values within expected, natural variation for only 10 of the 18 comparisons. The model performed best when comparing with the measurements during periods when the air originated from the east. In that case all six of the parameters being compared had average differences be- tween the model and measurements less than the expected standard deviation. For the cases when the air masses are from the southwest or west northwest, only two of the com- parisons from each case showed differences less than the ex- pected standard deviation. The differences appear to be a re- sult of an overly rapid growth of the boundary layer predicted by the model and too much dilution. There also is more O3 being produced, most likely by photochemical production, downwind of the emission sources than is predicted by the model. The measured and modeled PSD compare very well with respect to their general shape and the diameter of the peak concentrations. The spectra are log normally distributed with most of the mass in the accumulation mode centered at 200± 20 nm and little observed or predicted changes with respect to the time when the RML is above the Altzomoni research station. Only the total mass changes with time and air mass origin. The invariability of average diameter of the accumulation mode suggests that there is very little growth of the particles by condensation or coagulation after six hours of aging downwind of the major sources of anthropogenic emissions in Mexico's Megapolis. This could greatly sim- plify parameterization in climate models although it is not known at this time if this invariance can be extended to other megacity regions.
Characterization of the Hansenula polymorpha CPY gene encoding carboxypeptidase Y

We have isolated the Hansenula polymorpha CPY gene encoding carboxypeptidase Y (Hp‐CPY). The deduced amino acid sequence revealed that Hp‐CPY consists of 541 amino acids and has a calculated Mr of 60,793. The protein is highly similar to Saccharomyces cerevisiae CPY (61·8% identity). At the N‐terminus of Hp‐CPY signals for the entry into the secretory pathway and subsequent sorting to the vacuole were identified. Immunocytochemically, using monospecific antibodies raised against Hp‐CPY, the protein was localized to the vacuole. On Western blots, a diffuse protein band was observed in extracts of H. polymorpha cells, suggesting that the protein is glycosylated. This was confirmed by endoglycosidase H treatment, which resulted in a strong reduction of the apparent Mr of the protein. We have investigated the effect of CPY deletion on the degradation of peroxisomes, an autophagous process that occurs when the organelles become redundant for growth. In Δcpy cells peroxisomal proteins were degraded in the vacuole as efficiently as in wild‐type H. polymorpha cells, indicating that CPY is not a major proteinase in this pathway. Copyright © 1999 John Wiley & Sons, Ltd.
Violent victimisation, professional self-esteem, and punitivity of German police officers

ABSTRACT To date, punitive attitudes of police officers have received only little attention in criminological research. The present study addresses this lack of research by investigating punitivity in German police officers. Focus is placed on violent victimisation, which has been commonly considered to be a main cause of punitive attitudes. Furthermore, this study examined whether police officers’ professional self-esteem is related to punitivity and serves as a protective factor that buffers the negative effect of violent victimisation on punitivity. Ordinary least square regression analyses based on a sample of 15,733 German police officers support a relationship between victimisation experiences and punitivity. Police officers who had been violently attacked within the last year reported higher levels of punitivity than non-victims. Moreover, respondents with repeated victimisation experiences were the most punitive. While higher levels of professional self-esteem were indeed associated with less punitivity, the suggested protective influence of police officers’ professional self-esteem could not be confirmed. By contrast, higher levels of self-esteem instead increased the negative effects of victimisation experiences on punitivity.
Measuring constraint violations in information retrieval

Recently, an inductive approach to modelling term-weighting function correctness has provided a number of axioms (constraints), to which all good term-weighting functions should adhere. These constraints have been shown to be theoretically and empirically sound in a number of works. It has been shown that when a term-weighting function breaks one or more of the constraints, it typically indicates sub-optimality of that function. This elegant inductive approach may more accurately model the human process of determining the relevance a document. It is intuitive that a person's notion of relevance changes as terms that are either on or off-topic are encountered in a given document. Ultimately, it would be desirable to be able to mathematically determine the performance of term-weighting functions without the need for test collections. Many modern term-weighting functions do not satisfy the constraints in an unconditional manner. However, the degree to which these functions violate the constraints has not been investigated. A comparison between weighting functions from this perspective may shed light on the poor performance of certain functions in certain settings. Moreover, if a correlation exists between performance and the number of violations, measuring the degree of violation could help more accurately predict how a certain scheme will perform on a given collection.
Bridging attention across space and time: Do positive emotion generated shifts in spatial attention lead to changes in attention across time?

iii ACKNOWLEDGEMENTS . iv LIST OF TABLES . vi LIST OF FIGURES . vii LIST OF ABBREVIATIONS . viii BRIDGING ATTENTION ACROSS SPACE AND TIME: DO POSITIVE EMOTION GENERATED SHIFTS IN SPATIAL ATTENTION LEAD TO CHANGES IN ATTENTION ACROSS TIME? 1 Experiment 1 . 5 Experiment 2 . 13 General Discussion . 18 REFERENCES . 20
Investigations of Reduction of Mutual Coupling Between Two Planar Monopoles Using Two λ/4 Slots

This article presents a simple structure for reducing mutual coupling between two diversity planar monopole antennas for WLAN 5.2/5.8GHz applications. The structure has two ‚=4 (‚- wavelength in the substrate) slots cut into the ground plane between the two monopoles. In 0.5‚o (‚o-wavelength in the air) of the antenna spacing, mutual coupling was i33:3, i21:1dB at 5.2, 5.8GHz, respectively. The lowest mutual coupling of i33:3dB was achieved at 5.2GHz, which are 20.8dB improvements over the reference.
Synthetic approach of symbolic creatures

We propose, based on the Peircean semiotics and inspired by an ethological case, a methodology to simulate the emergence of symbolic predator-warning communication among artificial creatures in a virtual world of predatory events. In order to build a digital ecosystem, and infer the minimum organizational constraints for the design of our creatures, we examined the well-studied case of semiosis in East African vervet monkeys (Cercopithecus aethiops).
Layered video multicast with retransmissions (LVMR): evaluation of hierarchical rate control

Layered video multicast with retransmissions (LVMR) is a system for distributing video using layered coding over the Internet. The two key contributions of the system are: (1) improving the quality of reception within each layer by retransmitting lost packets given an upper bound on recovery time and applying an adaptive playback point scheme to help achieve more successful retransmission, and (2) adapting to network congestion and heterogeneity using hierarchical rate control mechanism. This paper concentrates on the rate control aspects of LVMR. In contrast to the existing sender-based and receiver-based rate control in which the entire information about network congestion is either available at the sender (in sender-based approach) or replicated at the receivers (in receiver-based approach), the hierarchical rate control mechanism distributes the information between the sender, receivers, and some agents in the network in such a way that each entity maintains only the information relevant to itself. In addition to that, the hierarchical approach enables intelligent decisions to be made in terms of conducting concurrent experiments and choosing one of several possible experiments at any instant of time based on minimal state information at the agents in the network. Protocol details are presented in the paper together with experimental and simulation results to back our claims.
Experiments with Mood Classification in

We present preliminary work on classifying blog text according to the mood reported by its author during the writing. Our data consists of a large collection of blog posts – online diary entries – which include an indication of the writer’s mood. We obtain modest, but consistent improvements over a baseline; our results show that further increasing the amount of available training data will lead to an additional increase in accuracy. Additionally, we show that the classification accuracy, although low, is not substantially worse than human performance on the same task. Our main finding is that mood classification is a challenging task using current text analysis methods.