Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Dictionaries of Sparse Codes of 3D Movements of Body Joints for Real-Time Human Activity Understanding

Real-time human activity recognition is essential for human-robot interactions for assisted healthy independent living. Most previous work in this area is performed on traditional two-dimensional (2D) videos and both global and local methods have been used. Since 2D videos are sensitive to changes of lighting condition, view angle, and scale, researchers begun to explore applications of 3D information in human activity understanding in recently years. Unfortunately, features that work well on 2D videos usually don't perform well on 3D videos and there is no consensus on what 3D features should be used. Here we propose a model of human activity recognition based on 3D movements of body joints. Our method has three steps, learning dictionaries of sparse codes of 3D movements of joints, sparse coding, and classification. In the first step, space-time volumes of 3D movements of body joints are obtained via dense sampling and independent component analysis is then performed to construct a dictionary of sparse codes for each activity. In the second step, the space-time volumes are projected to the dictionaries and a set of sparse histograms of the projection coefficients are constructed as feature representations of the activities. Finally, the sparse histograms are used as inputs to a support vector machine to recognize human activities. We tested this model on three databases of human activities and found that it outperforms the state-of-the-art algorithms. Thus, this model can be used for real-time human activity recognition in many applications.
Analysis of i-vector Length Normalization in Speaker Recognition Systems

We present a method to boost the performance of probabilistic generative models that work with i-vector representations. The proposed approach deals with the nonGaussian behavior of i-vectors by performing a simple length normalization. This non-linear transformation allows the use of probabilistic models with Gaussian assumptions that yield equivalent performance to that of more complicated systems based on Heavy-Tailed assumptions. Significant performance improvements are demonstrated on the telephone portion of NIST SRE 2010.
The impact of demographic factors on the way lesbian and gay employees manage their sexual orientation at work: An intersectional perspective

Purpose - – The purpose of this paper is to explore the influence demographic factors have on the way lesbians and gay men manage their sexual orientation at work. Design/methodology/approach - – Based on data taken from a cross-sectional survey of 1,308 gay and lesbian employees working in Germany, four regression models are proposed. The means of handling one’s homosexuality at work was measured by the 31 items containing Workplace Sexual Identity Management Measure from Anderson Findings - – Results indicate that being in a relationship is related to increased openness about one’s homosexuality at work. Furthermore, it appears that the older and the more religious lesbian and gay employees are, the more open (and therefore less hidden) about their sexuality they are. Having a migratory background is related to being more guarded about one’s sexual orientation, whereas personal mobility within the country is not related to the way one manages one’s sexual orientation at work. Lesbians tend to be a little more open and less guarded about their homosexuality compared to gay men. Research limitations/implications - – The focus of this research (and the related limitations) offers several starting and connecting points for more intersectional research on workforce diversity and diversity management. Practical implications - – The study’s findings indicate the need for an intersectional approach to organizational diversity management strategies. Exemplified by the dimension “sexual orientation”, it can be shown that the impact each dimension has for an employee’s everyday workplace experiences and behavior in terms of a certain manifestation of one dimension of diversity can only be understood in terms of its interplay with other dimensions of diversity. Originality/value - – It is shown that manifestations of demographic factors that tend to broaden the individual’s coping resources for stigma-relevant stressors lead to more openness about one’s homosexuality in the workplace.
Comparing two approaches for the assessment of coping. Part II. Differences in stability in time

Stress coping inventories can be divided into trait questionnaires independent from situational factors and situation-related inventories. In a previous study, a situation-related version of the Stressverarbeitungsfragebogen (SVF120; Janke & Erdmann, 1997), adapted from the instructions of the Ways of Coping Questionnaire (WCQ, Folkman & Lazarus, 1988), showed reliabilities and factor structures similar to those of the standard version. In the present study, we compared the medium-term stability over a time period of 4 weeks between the situational WCQ version and the situation-independent standard version of the SVF120. The standard version of the SVF120 was filled out by 140 subjects and the WCQ version by 132 subjects. We could confirm that both questionnaires provide similar psychometric properties when a single assessment is considered. We found, however, distinct differences in stability over time. While stability coefficients of the standard version are close to the values of the internal consistency of the scales and factors, almost all stability coefficients of the WCQ version were significantly lower. Absolute stability proved to be considerably lower for the WCQ version. It can be concluded that the WCQ test format is not appropriate for the assessment of person-environment transactions according to the objective of the Lazarus theory, since it is not insensitive to test repetition.
Resiliency in the face of interparental violence : a qualitative investigation

Childhood exposure to family violence is a risk factor for dysfunctional intimate relationships in adulthood (e.g., Fagan & Browne, 1994). Family violence research has commonly utilized Social Learning Theory to explain cross-generational patterns of family violence. However, not all individuals who experience a violent home environment become abusers or victims in their parental or marital roles (e.g., Duffy & Momirov, 1997), which illustrates resiliency positive adaptation or development in spite of serious threats or significant adversity (Masten, 2001). Considering strengths and resources, rather than risks and vulnerabilities, is relatively new in the field of family violence. Consequently, a basic interpretive qualitative research design (Merriam, 2002) was utilized to investigate the experiences of three heterosexual women who self-identified as (a) having observed a pattern of interparental violence in childhood and (b) currently being in non-violent intimate adult relationships. The study’s purpose was to describe and understand this phenomenon, with special attention given to identifying possible resiliency and protective factors. Interviews were conducted on multiple occasions and transcripts were analyzed in terms of Kearney’s (2001) shared meaning and descriptive categories. The shared meaning involved the role of emotional work required, and the descriptive categories included five themes (i.e., Diverse Experiences of Family Violence, Family Violence is Always with You, Complex Daughter-Mother Relationships, Understanding and Making Sense of Healthy Relationships, and Sources of Strength and Positive Influences). Findings are described alongside implications for counselling practice and future research.
A Time-Frequency Domain Underdetermined Blind Source Separation Algorithm for MIMO Radar Signals

This paper considers the underdetermined blind separation of multiple input multiple output (MIMO) radar signals that are insufficiently sparse in both time and frequency domains under noisy conditions, while traditional algorithms are usually applied in the ideal sparse environment. An effective separation method based on single source point (SSP) identification and time-frequency smoothed l 0 norm (TF-SL0) is proposed. Firstly, a preprocessing step of the moving average filter and a novel argument-based time-frequency SSPs detection are employed to improve the signal-to-noise ratio and signal sparsity of the observed signals, respectively. Then, the mixing matrix is obtained by using clustering algorithms. Secondly, to obtain the optimal solution of underdetermined sparse component analysis, the smoothed l 0 norm (SL0) is introduced to preliminarily achieve signal separation in the time-frequency domain. Finally, time-frequency ridge estimation is proposed to jointly enhance the reconstruction accuracy of the MIMO radar signals, and the time domain waveforms are recovered by the model of the signals. Simulations illustrate the validity of the method and show that the proposed method outperforms the traditional methods in source separation, especially in the non-cooperative electromagnetic case where the prior information is unknown.
Warped image restoration with applications to digital libraries

Recently, high resolution digital cameras have made the digitization process more flexible and convenient than traditional scanning technology. Therefore, document image analysis techniques need to be extended to handle images captured using ordinary hand-held cameras under an uncontrolled environment. In this paper, we propose an image restoration technique that corrects various warping distortions such as rolling curl, binding curl and fold distortions in camera images of geometrically distorted documents. Our goal is to restore these arbitrarily warped document images to a planar shape so as to facilitate OCR. Our method uses spline interpolation technique based on a ruled surface model constructed from text lines in the 2D document image. Comparisons on OCR performance using Microsoft imaging software with two existing methods show an encouraging improvement in terms of recall and precision. The OCR precision on the corrected images using our method is improved up to 21.8% comparing to original images with a resolution of 3 mega pixels.
Online meta-learning by parallel algorithm competition

The efficiency of reinforcement learning algorithms depends critically on a few meta-parameters that modulate the learning updates and the trade-off between exploration and exploitation. The adaptation of the meta-parameters is an open question, which arguably has become a more important issue recently with the success of deep reinforcement learning. The long learning times in domains such as Atari 2600 video games makes it not feasible to perform comprehensive searches of appropriate meta-parameter values. In this study, we propose the Online Meta-learning by Parallel Algorithm Competition (OMPAC) method, which is a novel Lamarckian evolutionary approach to online meta-parameter adaptation. The population consists of several instances of a reinforcement learning algorithm which are run in parallel with small differences in initial meta-parameter values. After a fixed number of learning episodes, the instances are selected based on their performance on the task at hand, i.e., the fitness. Before continuing the learning, Gaussian noise is added to the meta-parameters with a predefined probability. We validate the OMPAC method by improving the state-of-the-art results in stochastic SZ-Tetris and in 10x10 Tetris by 31% and 84%, respectively, and by improving the learning speed and performance for deep Sarsa(λ) agents in the Atari 2600 domain.
Adaptive IP Mutation: A Proactive Approach for Defending against Worm Propagation

IP address mutation is a proactive defense method that is used to reduce the risk of network attacks, especially to deal with the worm propagation attacks. However, previous work did not give much consideration to the negative effects that IP address mutation could bring to network performance. To be specific, there is a trade-off between network performance and security, which implies that when a security mechanism is reinforced, network performance would be impaired and vice versa. Therefore, in order to achieve the optimal balance between performance and security, an optimal solution should be provided. In this paper, we propose an adaptive IP mutation defense method which is based on temporal-dimension, to dynamically control the mutation interval according to real-time measurable metrics, assurance and avoidance. This method leverages a genetic algorithm to achieve the optimization of performance-security trade-off. We then evaluate our method in a simulated computer cluster environment, including 1024 hosts, and demonstrate that our method can successfully find the optimal solution according to the experimental results. For example, it can reduce the worm propagation significantly, while maintaining the network performance in a predefined level.
NV-Hypervisor: Hypervisor-Based Persistence for Virtual Machines

Power outages and subsequent recovery are major causes of service downtimes. This issue is amplified by the ongoing trend of steadily growing in-memory state of Internet-based services which increases the risk of data loss and extends recovery time. Protective measures against power outages, such as uninterruptible power supply are expensive, maintenance-intensive and often fragile. With the advent of non-volatile random-access memory (NVRAM) provided by commodity servers, there is a scalable, less costly and robust alternative to recover from power outages and other failures. However, as of today, off-the-shelf software is not ready for benefiting from NVRAM. We present NV-Hyper visor a lightweight hyper visor extension that transparently provides persistence for virtual machines. NV-Hyper visor paves the way for utilizing NVRAM in virtualized environments (i.e., infrastructure-as-a-service clouds) and protects stateful services such as key-value stores and databases from data loss and time-consuming recovery.
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning

We introduce a variational Bayesian inference algorithm which can be widely applied to sparse linear models. The algorithm is based on the spike and slab prior which, from a Bayesian perspective, is the golden standard for sparse inference. We apply the method to a general multi-task and multiple kernel learning model in which a common set of Gaussian process functions is linearly combined with task-specific sparse weights, thus inducing relation between tasks. This model unifies several sparse linear models, such as generalized linear models, sparse factor analysis and matrix factorization with missing values, so that the variational algorithm can be applied to all these cases. We demonstrate our approach in multi-output Gaussian process regression, multi-class classification, image processing applications and collaborative filtering.
Varying Rate of RNA Chain Elongation during rrn Transcription in Escherichia coli

ABSTRACT The value of the rRNA chain elongation rate in bacteria is an important physiological parameter, as it affects not only the rRNA promoter activity but also the free-RNA polymerase concentration and thereby the transcription of all genes. On average, rRNA chains elongate at a rate of 80 to 90 nucleotides (nt) per s, and the transcription of an entire rrn operon takes about 60 s (at 37°C). Here we have analyzed a reported distribution obtained from electron micrographs of RNA polymerase molecules along rrn operons in E. coli growing at 2.5 doublings per hour (S. Quan, N. Zhang, S. French, and C. L. Squires, J. Bacteriol. 187:1632-1638, 2005). The distribution exhibits two peaks of higher polymerase density centered within the 16S and 23S rRNA genes. An evaluation of this distribution indicates that RNA polymerase transcribes the 5′ leader region at speeds up to or greater than 250 nt/s. Once past the leader, transcription slows down to about 65 nt/s within the 16S gene, speeds up in the spacer region between the 16S and 23S genes, slows again to about 65 nt/s in the 23S region, and finally speeds up to a rate greater than 400 nt/s near the end of the operon. We suggest that the slowing of transcript elongation in the 16S and 23S sections is the result of transcriptional pauses, possibly caused by temporary interactions of the RNA polymerase with secondary structures in the nascent rRNA.
An Integrated Approach to Heterogeneous Data for Information Extraction

The paper proposes an integrated framework for web personal information extraction, such as biographical information and occupation, and those kinds of information are necessary to further construct a social network (a kind of semantic web) for a person. As web data is heterogeneous in nature, most of IE systems, regardless of named entity recognition (NER) or relation detection and recognition (RDR) systems, fail to get reliably robust results. We propose a flexible framework, which can effectively complement stateof-the-art statistical IE systems with rule-based IE systems for web data, and achieves substantial improvement over other existing systems. In particular, in our current experiment, both the rule-based IE system, which is designed according to some web specific expression patterns, and the statistical IE systems, which are developed for some homogeneous corpora, are sensitive only to specific information types. Hence we argue that our system performance can be incrementally improved when new and effective IE systems are added into our framework.
Regulation of transforming growth factor (cid:98) - and activin-induced transcription by mammalian Mad proteins

Members of the transforming growth factor (cid:98) (TGF- (cid:98) ) superfamily are involved in diverse physiological activities including development, tissue repair, hormone reg- ulation, bone formation, cell growth, and differentiation. At the cellular level, these functions are initiated by the interac- tion of ligands with specific transmembrane receptors with intrinsic serine (cid:121) threonine kinase activity. The signaling pathway that links receptor activation to the transcriptional regulation of the target genes is largely unknown. Recent work in Drosophila and Xenopus signaling suggested that Mad (Mothers against dpp ) functions downstream of the receptors of the TGF- (cid:98) family. Mammalian Mad1 has been reported to respond to bone morphogenetic protein (BMP), but not to TGF- (cid:98) or activin. We report here the cloning and functional studies of a novel mammalian Mad molecule, Mad3, as well as a rat Mad1 homologue. Overexpression of Mad3 in a variety of cells stimulated basal transcriptional activity of the TGF- (cid:98) (cid:121) activin-responsive reporter construct, p3TP-Lux. Further-more, expression of Mad3 could potentiate the TGF- (cid:98) - and activin-induced transcriptional stimulation of p3TP-Lux. By contrast, overexpression of Mad1 inhibited the basal as well as the TGF- (cid:98) (cid:121) activin induced p3TP-Lux activity. These findings, We describe here the cloning and transcriptional regulation of two rat Mad proteins, Mad1 and Mad3. Sequence analysis reveals that the rat Mad1 is a rat homologue of human and Xenopus Mad1. The sequence of the rat Mad3 is similar to but distinct from the reported mammalian and Xenopus Mad2s. Overexpression of Mad3 leads to ligand-independent stimulation of p3TP-Lux transcription, whereas overexpression of the rat Mad1 blocks it. Immunofluorescence staining with a myc Mad3 reveals that Mad3 is predominantly localized in the nucleus independent by ligand stimulation. Fur-thermore, Mad3 can potentiate the transcriptional response of TGF- (cid:98) and activin. These findings suggest that Mad3 differs in transcriptional regulation and that may play a role in the signal transduction of TGF- (cid:98) (cid:121) activin receptors.
Therapeutic Discovery MUC 13 Mucin Augments Pancreatic Tumorigenesis

The high death rate of pancreatic cancer is attributed to the lack of reliable methods for early detection and underlying molecular mechanisms of its aggressive pathogenesis. Although MUC13, a newly identified transmembranemucin, is known to be aberrantly expressed in ovarian and gastro-intestinal cancers, its role in pancreatic cancer is unknown. Herein, we investigated the expression profile and functions of MUC13 in pancreatic cancer progression. The expression profile of MUC13 in pancreatic cancer was investigated using a recently generatedmonoclonal antibody (clone PPZ0020) and pancreatic tissuemicroarrays. The expression of MUC13 was significantly (P < 0.005) higher in cancer samples compared with normal/nonneoplastic pancreatic tissues. For functional analyses, full-length MUC13 was expressed in MUC13 null pancreatic cancer cell lines, MiaPaca and Panc1. MUC13 overexpression caused a significant (P < 0.05) increase in cell motility, invasion, proliferation, and anchorage-dependent or -independent clonogenicity while decreasing cell–cell and cell-substratum adhesion. Exogenous MUC13 expression significantly (P < 0.05) enhanced pancreatic tumor growth and reduced animal survival in a xenograft mouse model. These tumorigenic characteristics correlated with the upregulation/phosphorylation of HER2, p21-activated kinase 1 (PAK1), extracellular signal-regulated kinase (ERK), Akt, and metastasin (S100A4), and the suppression of p53. Conversely, suppression of MUC13 in HPAFII pancreatic cancer cells by short hairpin RNA resulted in suppression of tumorigenic characteristics, repression of HER2, PAK1, ERK, and S100A4, and upregulation of p53. MUC13 suppression also significantly (P < 0.05) reduced tumor growth and increased animal survival. These results imply a role of MUC13 in pancreatic cancer and suggest its potential use as a diagnostic and therapeutic target. Mol Cancer Ther; 11(1); 24–33. 2011 AACR.
Toward Patient-Tailored Perfusion Thresholds for Prediction of Stroke Outcome

BACKGROUND AND PURPOSE: Multiple patient-specific clinical and radiologic parameters impact traditional perfusion thresholds used to classify/determine tissue outcome. We sought to determine whether modified baseline perfusion thresholds calculated by integrating baseline perfusion and clinical factors better predict tissue fate and clinical outcome. MATERIALS AND METHODS: CTP within 4.5 hours of acute anterior circulation stroke onset and 5- to 7-day MR imaging were performed for 203 patients with stroke, divided into derivation (n = 114) and validation (n = 89) data bases. Affected regions were operationally classified as infarct and noninfarct according to baseline CTP and follow-up FLAIR imaging. Perfusion thresholds were derived for each of the infarct and noninfarct regions, without and with transformation by baseline clinical and radiologic variables by using a general linear mixed model. Performance of transformed and nontransformed perfusion thresholds for tissue fate and 90-day clinical outcome prediction was then tested in the derivation data base. Reproducibility of models was verified by using bootstrapping and validated in an independent cohort. RESULTS: Perfusion threshold transformation by clinical and radiologic baseline parameters significantly improved tissue fate prediction for both gray matter and white matter (P < .001). Transformed thresholds improved the 90-day outcome prediction for CBF and time-to-maximum (P < .001). Transformed relative CBF and absolute time-to-maximum values demonstrated maximal GM and WM accuracies in the derivation and validation cohorts (relative CBF GM: 91%, 86%; WM: 86%, 83%; absolute time-to-maximum 88%, 79%, and 80%, 76% respectively). CONCLUSIONS: Transformation of baseline perfusion parameters by patient-specific clinical and radiologic parameters significantly improves the accuracy of tissue fate and clinical outcome prediction.