Miracle-dz/UniFAR-FTUs huggingface dataset:
A hybrid probabilistic neural model for person tracking based on a ceiling-mounted camera

Person tracking is an important topic in ambient living systems as well as in computer vision. In particular, detecting a person from a ceiling-mounted camera is a challenge since the person's appearance is very different from the top or from the side view, and the shape of the person changes significantly when moving around the room. This article presents a novel approach for a real-time person tracking system based on particle filters with input from different visual streams. A new architecture is developed that integrates different vision streams by means of a Sigma-Pi-like network. Moreover, a short-term memory mechanism is modeled to enhance the robustness of the tracking system. Based on this architecture, the system can start localizing a person with several cues and learn the features of other cues online. The experimental results show that robust real-time person tracking can be achieved.
eb 2 00 7 Is room-temperature superconductivity with phonons possible ?

By recognizing the vital importance of two-hole Cooper pair s (CPs) in addition to the usual two-electron ones in a strongly-interacting many-el ectron system, the concept of CPs was re-examined with striking conclusions: namely, they are ga pped and linearly-dispersive resonances with a finite lifetime—but provided the ideal-gas Fermi sea i s replaced by a BCS-correlated unperturbed ground-state “sea. ” Based on this, Bose-Einstein co ndensation (BEC) theory has been generalized to include not boson-boson interactions (also ne glected in BCS theory) but rather bosonfermion (BF) interaction vertices reminiscent of the Fröhl ic electron-phonon interaction in metals. Instead of phonons, the bosons in the generalized BEC ( GBEC) theory are nowboth particle and hole CPs. Unlike BCS theory, the GBEC model is not a mean-field theory restricted to weak-coupling as it can be diagonalized exactly. It reprodu ces the BCS condensation energy exactly for any coupling, and each kind of CP is responsible for only half the condensation energy. The GBEC theory reduces to all the old known statistical theo ries as special cases—including the so-called “BCS-Bose crossover” picture which in turn gener alizes BCS theory bynot assuming that the interelectronic chemical potential equals the Fer mi nergy. Indeed, a BCS condensate is preciselythe weak-coupling limit of a GBE condensate with equal numbe rs of both types of CPs. With feasible Cooper/BCS model interelectonic interactio n parameter values, and even without BF interactions, the GBEC theory yields transition temperatu res [including room-temperature superconductivity (RTSC)] substantially higher than the BCS cei ling of around 45K, without relying on non-phonon dynamics involving excitons, plasmons, magnon s or otherwise purely-electronic mechanisms. The results are expected to shed light on the experim ental search for RTSC.
Feature-Preserving Volume Filtering

In this paper a feature-preserving volume filtering method is presented. The basic idea is to minimize a three-component global error function penalizing the density and gradient errors and the curvature of the unknown filtered function. The optimization problem leads to a large linear equation system defined by a sparse coefficient matrix. We will show that such an equation system can be efficiently solved in frequency domain using fast Fourier transformation (FFT). For the sake of clarity, first we illustrate our method on a 2D example which is a dedithering problem. Afterwards the 3D extension is discussed in detail since we propose our method mainly for volume filtering. We will show that the 3D version can be efficiently used for elimination of the typical staircase artifacts of direct volume rendering without losing fine details. Unlike local filtering techniques, our novel approach ensures a⋅global smoothing effect. Previous global 3D methods are restricted to binary volumes or segmented iso-surfaces and they are based on area minimization of one single reconstructed surface. In contrast, our method is a general volume-filtering technique, implicitly smoothing all the iso-surfaces at the same time. Although the strength of the presented algorithm is demonstrated on a specific 2D and a specific 3D application, it is considered as a general mathematical tool for processing images and volumes.
The Good, the Bad, and the Ugly: Facial Stereotyping and Juridic Judgments

The existence of stereotypes and their possible influence upon social information processing has received a great deal ofempirical investigation from social psychologists in recent years. Stereotypes have been shown to affect all stages of the human information processing sequence. That is, they influence our selection of information from our experiential field, how we encode and internally represent this information, and, perhaps most importantly, how easily we can retrieve this information from memory (eg. Hamilton, 1979). Given the apparent magnitude and range of these stereotypical effects, it isnot surprising that legal psychologists have explicitly considered the possible existence of criminal stereotypes and the effects they may have in the judicial process. It is argued, that in the same way that we hold stereotypical notions about how we expect certain categories of people to look, we also possess stereotypical conceptions of the likely appearance of different types of criminals (Bull & Rumsey,1988; Shepherd, 1989). That is, in addition to possessing a mental image or picture of the typical politician, or athlete, or even psychologist, we also possess well formed internal representations of muggers, rapists, and murderers, etc. Importantly, it is suggested that these criminal stereotypes may affect judgments at all stages of the legal process. For example, the selection of an individual from an identity parade may be influenced by the degree to which he or she resembles the commonly held criminal stereotype for the incident in question. Furthermore, people's memory for incident details may be affected by the availability of a stereotypical explanation for the crime (Macrae & Shepherd, in press). Also, the appearance of the defendant may affect the jury's final verdict. The study reported in the present paper directly addresses the relationship between appearance and juridic judgments. Specifically, if it is the case that the defendant's face fits the stereotype for a particular crime, will he or she subsequently be more likely to be found guilty? Before discussing the present study in more detail several questions are posed. These consider, (1) whether the general public do actually hold criminal stereotypes, and (2) whether previous findings support the assertion that criminal stereotypes affect juridic judgments. The present study is then outlined in some detail, and thepaperends with a consideration ofthe concIusions and implications that can bedrawn from the experimental findings reported (Macrae, in press; Macrae & Shepherd, in press).
Research on smart space oriented context-aware system

The emergence of context-aware computing (CAC) environment makes context based human-computer interaction (HCI) possible. The main problems faced CAC is the lack of a unified, general support for CAC system framework. This paper proposed a smart space oriented context-aware system called S2CAS in order to provide an idea to handle contextual information processing and services.
Soft string-to-dependency hierarchical machine translation

In this paper, we dissect the influence of several target-side dependency-based extensions to hierarchical machine translation, including a dependency language model (LM). We pursue a non-restrictive approach that does not prohibit the production of hypotheses with malformed dependency structures. Since many questions remained open from previous and related work, we offer in-depth analysis of the influence of the language model order, the impact of dependencybased restrictions on the search space, and the information to be gained from dependency tree building during decoding. The application of a non-restrictive approach together with an integrated dependency LM scoring is a novel contribution which yields significant improvements for two large-scale translation tasks for the language pairs Chinese–English and German–French.
Partial Fourier reconstruction using subspace projection

Scan time reduction in MRI can be achieved by partial k-space reconstruction. Truncation of the k-space results in generation of artifacts in the reconstructed image. A subspace projection algorithm is developed for artifact-free reconstruction of sparse MRI. The algorithm is applied to a frequency weighted k-space, which fits into a signal-space model for sparse MR images. The application is illustrated using Magnetic Resonance Angiogram (MRA).
Time-Optimal Top-k Document Retrieval

Let $\mathcal D$ be a collection of $D$ documents, which are strings over an alphabet of size $\sigma$, of total length $n$. We describe a data structure that uses linear space and reports $k$ most relevant documents that contain a query pattern $P$, which is a string of length $p$ packed in $p/\log_\sigma n$ words, in time $O(p/\log_\sigma n+k)$. This is optimal in the RAM model in the general case where $\log D = \Theta(\log n)$, and involves a novel RAM-optimal suffix tree search. Our construction supports an ample set of important relevance measures, such as the number of times $P$ appears in a document (called term frequency), a fixed document importance, and the minimal distance between two occurrences of $P$ in a document. When $\log D = o(\log n)$, we show how to reduce the space of the data structure from $O(n\log n)$ to $O(n(\log\sigma+\log D+\log\log n))$ bits, and to $O(n(\log\sigma+\log D))$ bits in the case of the popular term frequency measure of relevance, at the price of an additive term $O(\log^\varepsilon_\sigma n)$ in the query time, for any constant $\varepsilon>0$. We also consider the dynamic scenario, where documents can be inserted and deleted from the collection. We obtain linear space and query time $O(p(\log\log n)^2/\log_\sigma n+\log n + k\log\log k)$, whereas insertions and deletions require $O(\log^{1+\varepsilon} n)$ time per symbol, for any constant $\varepsilon>0$. Finally, we consider an extended static scenario where an extra parameter $\mathtt{par}(P,d)$ is defined, and the query must retrieve only documents $d$ such that $\mathtt{par}(P,d)\in [\tau_1,\tau_2]$, where this range is specified at query time. We solve these queries using linear space and $O(p/\log_\sigma n + \log^{1+\varepsilon} n + k\log^\varepsilon n)$ time, for any constant $\varepsilon>0$. Our technique is to translate these top-$k$ problems into multidimensional geometric search problems. As a bonus, we describe some improvements to those problems.
An improved dynamic data replica selection and placement in cloud

Cloud computing platform is getting more and more attentions as a new trend of data management. Data replication has been widely used to speed up data access in cloud. Replica selection and placement are the major issues in replication. In this paper we propose an approach for dynamic data replication in cloud. A replica management system allows users to create, and manage replicas and update the replicas if the original datas are modified. The proposed work concentrates on designing an algorithm for suitable optimal replica selection and placement to increase availability of data in the cloud. The method consists of two main phases file application and replication operation. The first phase contains the replica location and creation by using catalog and index. In second phase is used to find whether there is enough space in the destination to store the requested file or not. Replication aims to increase availability of resources, minimum access cost, shared bandwidth consumption and delay time by replicating data. The proposed systems developed under the Eucalyptus cloud environment. The results of proposed replica selection algorithm achieve better accessibility compared with other methods.
A Study of Methods for Initialization and Permutation Alignment for Time-Frequency Domain Blind Source Separation

The problem of the blind signal separation (BSS) consists of estimating the latent component signals in a linear mixture, referred to as the sources, starting from several observed signals, without relying on any specific knowledge of the sources. In particular, when the sources are audible, this problem is known as to the cocktail-party problem, making reference to the ability of the human ear to isolate the conversation of our interest among several conversations immersed in a noisy environment with many people talking at the same time.
Spatial navigational strategies correlate with gray matter in the hippocampus of healthy older adults tested in a virtual maze

Healthy young adults use different strategies when navigating in a virtual maze. Spatial strategies involve using environmental landmarks while response strategies involve executing a series of movements from specific stimuli. Neuroimaging studies previously confirmed that people who use spatial strategies show increased activity and gray matter in the hippocampus, while those who use response strategies show increased activity and gray matter in caudate nucleus (Iaria et al., 2003; Bohbot et al., 2007). A growing number of studies report that cognitive decline that occurs with normal aging is correlated with a decrease in volume of the hippocampus. Here, we used voxel-based morphometry (VBM) to examine whether spatial strategies in aging are correlated with greater gray matter in the hippocampus, as found in our previous study with healthy young participants. Forty-five healthy older adults were tested on a virtual navigation task that allows spatial and response strategies. All participants learn the task to criterion after which a special “probe” trial that assesses spatial and response strategies is given. Results show that spontaneous spatial memory strategies, and not performance on the navigation task, positively correlate with gray matter in the hippocampus. Since numerous studies have shown that a decrease in the volume of the hippocampus correlates with cognitive deficits during normal aging and increases the risks of ensuing dementia, the current results suggest that older people who use their spatial memory strategies in their everyday lives may have increased gray matter in the hippocampus and enhance their probability of healthy and successful aging.
Factors Affecting the Entrepreneurial Intentions of Women Entrepreneur Candidates: A Structural Equation Model

The aim of this study was to reveal factors influencing the entrepreneurial intentions of women entrepreneur candidates using the theory of planned behaviour (TPB). The authors employ the TPB, in which intentions and aspiration towards being an entrepreneur are regarded as resulting from attitudes, perceived behavioural control, and subjective norms. A questionnaire was conducted on 252 women entrepreneur candidates who attend the vocational course, OMEK, which belongs to Eskişehir (Turkey) Odunpazarı Municipality. In the research model that has been proposed within the scope of TPB, the Structural Equation Model (SEM) was used in order to reveal the relationship between the factors influencing entrepreneurial intentions and evaluating the conformity of the model. According to the results of the SEM analysis, it was determined that the latent variables, which are attitudes, subjective norms and perceived behaviour control, have a positive effect on entrepreneurial intentions. In the study, the perceived behavioral control variable was calculated to be 0.38, 0.06 and 0.44, respectively, direct, indirect and total effect on an aspiration towards being an entrepreneur. In summary, it is determined that the perceived behavior control factor is the most important predictor of entrepreneurial intention and an aspiration towards being an entrepreneur factor.
Noisy training for deep neural networks

Deep neural networks (DNN) have gained remarkable success in speech recognition, partially attributed to its flexibility in learning complex patterns of speech signals. This flexibility, however, may lead to serious over-fitting and hence miserable performance degradation in adverse environments such as those with high ambient noises. We propose a noisy training approach to tackle this problem: by injecting noises into the training speech intentionally and randomly, more generalizable DNN models can be learned. This `noise injection' technique has been well-known to the neural computation community, however there is little knowledge if it would work for the DNN model which involves a highly complex objective function. The experiments presented in this paper confirm that the original assumptions of the noise injection approach largely holds when learning deep structures, and the noisy training may provide substantial performance improvement for DNN-based speech recognition.
Stabilin-1, a homeostatic scavenger receptor with multiple functions

The multifunctional scavenger receptor stabilin‐1 (STAB1, FEEL‐1, CLEVER‐1, KIAA0246) was originally identified as the MS‐1 antigen, expressed by sinusoidal endothelial cells in human spleen. Extensive histological studies revealed that stabilin‐1 is also expressed by tissue macrophages and sinusoidal endothelial cells in the healthy organism; its expression on both macrophages and different subtypes of endothelial cells is induced during chronic inflammation and tumorigenesis. In vitro induction of stabilin‐1 in macrophages requires the presence of glucocorticoids. Stabilin‐1 is involved in two intracellular trafficking pathways: receptor mediated endocytosis and recycling; and shuttling between the endosomal compartment and trans‐Golgi network (TGN). The latter intracellular pathway of stabilin‐1 trafficking is mediated by GGAs, clathrin adaptors that interact with the DDSLL motif in the cytoplasmic tail of stabilin‐1. When expressed by alternatively activated macrophages, stabilin‐1 mediates the uptake and targeting for degradation of acLDL and SPARC, a regulator of tissue remodeling. Likewise, stabilin‐1 in macrophages is involved in intracellular sorting and lysosomal delivery of the novel stabilin‐1‐interacting chitinase‐like protein (SI‐CLP). Indirect evidence suggests that stabilin‐1 is involved in adhesion and transmigration in various cell types (including tumor cells, leukocytes, and lymphocytes); however, its rapid recycling and scant level of surface expression argue against its universal role in cell adhesion. In summary, stabilin‐1 is a homeostatic receptor which links signals from the extracellular environment to intracellular vesicular processes, creating a potential impact on the macrophage secretion profile.
FPGA implementation of trellis decoders for linear block codes

Abstract. Forward error correction based on trellises has been widely adopted for convolutional codes. Because of their efficiency, they have also gained a lot of interest from a theoretic and algorithm point of view for the decoding of block codes. In this paper we present for the first time hardware architectures and implementations for trellis decoding of block codes. A key feature is the use of a sophisticated permutation network, the Banyan network, to implement the time varying structure of the trellis. We have implemented the Viterbi and the max-log-MAP algorithm in different folded versions on a Xilinx Virtex 6 FPGA.
Estimation of the Mixture Transition Distribution Model

This paper introduces a new iterative algorithm for the estimation of the Mixture Transition Distribution model (cid:2)MTD(cid:3)(cid:4) It does not require the use of any speci(cid:5)c external optimization procedure and can therefore be programmed in any computing language(cid:4) Comparisons with previously published results show that this new algorithm performs at least as good or better than other methods(cid:4) The choice of initial values is also discussed(cid:4) The MTD model was designed for the modeling of high(cid:6)order Markov chains and already proved to be a useful tool for the analysis of di(cid:7)erent types of time(cid:6)series such as wind speeds and wind directions(cid:4) In this paper(cid:8) we also propose to use this it for the modeling of one(cid:6) dimensional spatial data(cid:4) An application using a DNA sequence shows that this approach can lead to better results than the classical Potts model(cid:4)
Inhaled diesel emissions generated with cerium oxide nanoparticle fuel additive induce adverse pulmonary and systemic effects.

Diesel exhaust (DE) exposure induces adverse cardiopulmonary effects. Cerium oxide nanoparticles added to diesel fuel (DECe) increases fuel burning efficiency but leads to altered emission characteristics and potentially altered health effects. Here, we evaluated whether DECe results in greater adverse pulmonary effects compared with DE. Male Sprague Dawley rats were exposed to filtered air, DE, or DECe for 5 h/day for 2 days. N-acetyl glucosaminidase activity was increased in bronchial alveolar lavage fluid (BALF) of rats exposed to DECe but not DE. There were also marginal but insignificant increases in several other lung injury biomarkers in both exposure groups (DECe > DE for all). To further characterize DECe toxicity, rats in a second study were exposed to filtered air or DECe for 5 h/day for 2 days or 4 weeks. Tissue analysis indicated a concentration- and time-dependent accumulation of lung and liver cerium followed by a delayed clearance. The gas-phase and high concentration of DECe increased lung inflammation at the 2-day time point, indicating that gas-phase components, in addition to particles, contribute to pulmonary toxicity. This effect was reduced at 4 weeks except for a sustained increase in BALF γ-glutamyl transferase activity. Histopathology and transmission electron microscopy revealed increased alveolar septa thickness due to edema and increased numbers of pigmented macrophages after DECe exposure. Collectively, these findings indicate that DECe induces more adverse pulmonary effects on a mass basis than DE. In addition, lung accumulation of cerium, systemic translocation to the liver, and delayed clearance are added concerns to existing health effects of DECe.
Intercultural experience and creativity

Creativity, commonly defined as a production of novel and useful ideas, has long been considered as a type of individual difference that is highly associated with personality traits and intellectual intelligence, which cannot be changed easily. Recently, it has been demonstrated that creativity can occur as a result of cognitive, emotional, and motivational processes that are driven by intercultural experiences, which can be acquired. In line with the creative cognition approach, individuals with direct intercultural experiences exhibit higher individual creativity. Specifically, those who adopt biculturalism as their acculturation strategy and develop a high level of integration between their bicultural identities can reap the creative benefit of their intercultural experiences. Individuals with indirect intercultural experiences also exhibit higher creativity, and the underlying psychological mechanisms are the use of a comparison mindset and perceived cultural distance between the cultures. Advantageous features of intercultural experiences for enhancing individual creativity are discussed.