Miracle-dz/UniFAR-FTUs huggingface dataset:
Kalinin is more efficient than laminin in promoting adhesion of primary keratinocytes and some other epithelial cells and has a different requirement for integrin receptors

Kalinin was purified from squamous cell carcinoma (SCC25) spent culture media using an immunoaffinity column prepared from the mAb BM165. The affinity-purified material was separated by SDS-PAGE into three bands of 165-155, 140, and 105 kD identical to those obtained from normal human keratinocyte cultures and previously identified as kalinin. Kalinin promoted adhesion of a large number of normal cells and established cell lines with an activity similar to other adhesion molecules such as the laminin-nidogen complex, fibronectin, or collagen IV. However, kalinin was a much better substrate than laminin-nidogen complex for adhesion of cells of epithelial origin including primary human keratinocytes. Adhesion to kalinin was followed by cell shape changes ranging from rounded to fully spread cells depending on the cell types. The adhesion-promoting activity of kalinin was conformation dependent and was abolished by heat denaturation. mAb BM165 prevented cell adhesion to kalinin but not to other extracellular matrix substrates. However, either complete or partial inhibition was observed with different cells suggesting the existence of at least two cell- binding sites on the kalinin molecule. Experiments inhibiting cell adhesion with function-blocking anti-integrin subunit antibodies indicated that both alpha 3 beta 1 and alpha 6 beta 1 integrins are involved in the cellular interactions with kalinin, while for cell adhesion to classical mouse Engelbreth-Holm-Swarm laminin only alpha 6 beta 1 integrins, and not alpha 3 beta 1, appeared to be functional. Altogether, these results suggest that kalinin may fulfill additional functions than laminin, particularly for epithelial cells.
NONLINEAR QUANTUM MECHANICS IMPLIES POLYNOMIAL-TIME SOLUTION FOR NP-COMPLETE AND P PROBLEMS

If quantum states exhibit small nonlinearities during time evolution, then quantum computers can be used to solve NP-complete problems in polynomial time. We provide algorithms that solve NP-complete and #P oracle problems by exploiting nonlinear quantum logic gates. It is argued that virtually any deterministic nonlinear quantum theory will include such gates, and the method is explicitly demonstrated using the Weinberg model of nonlinear quantum mechanics.
Ideology in Management Studies

Ideology is a core concept in the social sciences and, in management, has long been deployed to highlight the political and embedded nature of management ideas and practices. We provide an overview, classification and evaluation of the meanings and use of ideology in the field of management studies. This is based on a review of 169 peer-reviewed empirical and theoretical articles and 41 related books published between 1956 and 2015. We find seven dominant views of ideology in management studies and connect them to different broader social science conceptualizations of ideology as: (1) domination; (2) legitimation; (3) a way of describing and explaining; (4) social integration; (5) a normative logic; (6) an object of critique; and (7) a fantasy structuring social reality. In doing so, we draw attention to the variety of contributions of ideology to understanding management and organisations and conclude by outlining its continuing relevance and avenues for future research.
Comparison of Mitochondrial-Related Transcriptional Levels of TFAM, NRF1 and MT-CO1 Genes in Single Human Oocytes at Various Stages of the Oocyte Maturation

Background: The aim of the current study was to assess the mRNA levels of two mitochondria-related genes, including nuclear-encoded NRF1 (nuclear respiratory factor 1), mitochondrial transcription factor A (TFAM), and mitochondrial-encoded cytochrome c oxidase subunit 1 (MT-CO1) genes in various stages of the human oocyte maturation. Methods: Oocytes were obtained from nine infertile women with male factor undergoing in vitro fertilization (IVF)/intra-cytoplasmic sperm injection protocol. Mitochondrial-related mRNA levels were performed by single-cell TaqMan real-time PCR. Results: the expression level of the target genes was low at the germinal vesicle stage (P>0.05). Although the mRNA level of NRF1gene remained stable in metaphase I, the mRNA level of TFAM and MT-CO1 increased significantly (P<0.05). In metaphase II, the expression level of all genes increased compared to metaphase I (P<0.05). Conclusion: The overexpression levels of NRF1, TFAM, and MT-CO1 genes are related to the oocyte maturation. Therefore, the current study could be used clinically to improve the success rate of IVF.
Internalizing Symptoms Associated with Emotional Abuse: An Examination of Religious Social Support as a Moderating Variable

3 Emotional Abuse and Mental Health Problems in Later Life . 5 Religious Social Support and Mental Health . 6 Contextual Factors and Religious Social Support . 9 Gender and Religious Social Support . 9 Ethnicity and Religious Social Support . 11 Present Study and Hypotheses . 14 Methods . 15 Participants . 15 Procedures . 15 Measures . 16 Retrospective measures . 16 Current measures . 18 Analytic Plan . 18 Results . 19 Discussion. 22 Limitations and Future Directions . 26 References . 33 INTERNALIZING SYMPTOMS ASSOCIATED WITH EMOTIONAL ABUSE 3
A fuzzy simulated evolution algorithm for integrated manufacturing system design

Article history: Received September202012 Received in revised format December 28 2012 Accepted January2 2013 Available online 3January 2013 Integrated cell formation and layout (CFLP) is an extended application of the group technology philosophy in which machine cells and cell layout are addressed simultaneously. The aim of this technological innovation is to improve both productivity and flexibility in modern manufacturing industry. However, due to its combinatorial complexity, the cell formation and layout problem is best solved by heuristic and metaheuristic approaches. As CFLP is prevalent in manufacturing industry, developing robust and efficient solution methods for the problem is imperative. This study seeks to develop a fuzzy simulated evolution algorithm (FSEA) that integrates fuzzy-set theoretic concepts and the philosophy of constructive perturbation and evolution. Deriving from the classical simulated evolution algorithm, the search efficiency of the major phases of the algorithm is enhanced, including initialization, evaluation, selection and reconstruction. Illustrative computational experiments based on existing problem instances from the literature demonstrate the utility and the strength of the FSEA algorithm developed in this study. It is anticipated in this study that the application of the algorithm can be extended to other complex combinatorial problems in industry. © 2013 Growing Science Ltd. All rights reserved
Non-uniform linear antenna array design for millimeter wave MIMO channels

In this paper, we study the achievable multiplexing gain of millimeter wave (MMW) line-of-sight (LoS) multiple-input multiple-output (MIMO) channels with non-uniform linear antenna arrays (NULAs) at both the transmitter and receiver. We first present an analytical characterization for the asymptotic channel eigenvalues with arbitrarily deployed NULAs. Then, aiming at achieving an effective multiplexing gain of two at practical signal-to-noise ratios, we mathematically formulate the array optimization problem. We show that the optimal NULA deployment at both link ends should follow a bi-ULA arrangement, consisting of two separate uniform linear antenna arrays (ULAs) with the minimum allowable antenna spacing within each ULA and the maximum feasible separation between the two ULAs. Numerical results demonstrate that significant capacity gain can be achieved by our optimized NULAs over the conventional ULAs.
Force feedback without sensor: A preliminary study on haptic modeling

Haptic feedback is an active trend in robotic surgery. However, it is not always feasible to place physical force sensors on the robotic tool-tips due to size or other constraints. Haptic modeling of the organ/tissue could be an alternative approach to provide estimated force information from available data. In this paper, we investigate haptic modeling to provide real-time force estimation in endoscopic surgery. Viscoelastic behavior of the gastrointestinal organ/tissue needs to be taken into consideration. Hence, we proposed a modified haptic model based on commonly-used lumped models, in order to restore the tool-tissue interaction force by using the robot motion. Performance of this approach was evaluated by ex-vivo experiments. The proposed model is capable of approximating the interaction force. Discussions were made on possible limitations and problems in the implementation of such approach.
Translating related words to videos and back through latent topics

Documents containing video and text are becoming more and more widespread and yet content analysis of those documents depends primarily on the text. Although automated discovery of semantically related words from text improves free text query understanding, translating videos into text summaries facilitates better video search particularly in the absence of accompanying text. In this paper, we propose a multimedia topic modeling framework suitable for providing a basis for automatically discovering and translating semantically related words obtained from textual metadata of multimedia documents to semantically related videos or frames from videos. The framework jointly models video and text and is flexible enough to handle different types of document features in their constituent domains such as discrete and real valued features from videos representing actions, objects, colors and scenes as well as discrete features from text. Our proposed models show much better fit to the multimedia data in terms of held-out data log likelihoods. For a given query video, our models translate low level vision features into bag of keyword summaries which can be further translated using simple natural language generation techniques into human readable paragraphs. We quantitatively compare the results of video to bag of words translation against a state-of-the-art baseline object recognition model from computer vision. We show that text translations from multimodal topic models vastly outperform the baseline on a multimedia dataset downloaded from the Internet.
Gene Family Evolution Reflects Adaptation to Soil Environmental Stressors in the Genome of the Collembolan Orchesella cincta

Collembola (springtails) are detritivorous hexapods that inhabit the soil and its litter layer. The ecology of the springtail Orchesella cincta is extensively studied in the context of adaptation to anthropogenically disturbed areas. Here, we present a draft genome of an O. cincta reference strain with an estimated size of 286.8 Mbp, containing 20,249 genes. In total, 446 gene families are expanded and 1,169 gene families evolved specific to this lineage. Besides these gene families involved in general biological processes, we observe gene clusters participating in xenobiotic biotransformation. Furthermore, we identified 253 cases of horizontal gene transfer (HGT). Although the largest percentage of them originated from bacteria (37.5%), we observe an unusually high percentage (30.4%) of such genes of fungal origin. The majority of foreign genes are involved in carbohydrate metabolism and cellulose degradation. Moreover, some foreign genes (e.g., bacillopeptidases) expanded after HGT. We hypothesize that horizontally transferred genes could be advantageous for food processing in a soil environment that is full of decaying organic material. Finally, we identified several lineage-specific genes, expanded gene families, and horizontally transferred genes, associated with altered gene expression as a consequence of genetic adaptation to metal stress. This suggests that these genome features may be preadaptations allowing natural selection to act on. In conclusion, this genome study provides a solid foundation for further analysis of evolutionary mechanisms of adaptation to environmental stressors.
Experimental Design and Testing of a Multimodal Cognitive Overload Classifier

We report the results of an experiment designed to construct and test a robust multimodal system for automatic classification of cognitive overload. With the assistance of The Scripps Research Institute, twenty-two experienced gamers performed a first-person shooter combat simulation while multiple biosignals and performance were recorded. Signals included EEG, EOG, EMG, ECG, accuracy, and reaction time measures. A kernel partial least squares or KPLS classifier was trained to distinguish subtle differences in EEG spectra within subjects as they pertained to passive viewing, low-difficulty, and high-difficulty simulations. The KPLS classifier was supported and enhanced by algorithms for preprocessing and normalization of EEG and other biosignals. Results indicated that for some subjects, robust classifiers discriminated passive viewing from active performance with accuracies in the range of 99% to 100% and that such models were stable over two test days, using 35-70 min. of training data from Day 1 and testing on data from Day 2. In addition, for some subjects, classifiers discriminated high-difficulty simulations from low-difficulty and passive simulations with stable accuracies of 80% or better across two test days, using 35-70 min. of training data from Day 1 and testing on data from Day 2. We also tested the effect of alcohol intoxication on simulation performance and classifier accuracy. Performance was slightly altered by drinking alcohol to a blood alcohol level of 0.06%, producing more aggressive behavior than with a placebo across subjects. The KPLS classifiers showed remarkable resilience to alcohol effects, considerably less than the effects of day of testing. Not all subjects had such impressive results. To address the lack of generality across subjects, we are currently performing a modified study design that includes provisions for three calibration tasks. These calibration tasks are intended to serve as brief, simple tasks that a user could easily perform at any time as needed to recalibrate algorithms that relate physiology to cognitive state. The tasks include a) eyes-open and eyes-closed for general EEG calibration, b) a mental arithmetic task (divide by twos or sevens) for cognitive load classification, and c) passive viewing of the combat simulation task, for calibration of engagement/disengagement of mental resources. We aim to use the data from the calibration tasks to adapt classification algorithms for variations over time and for inclusion of multiple sensor and data modalities, such as electrocardiographic, electrooculographic, and electromyographic sensor data.
Evolving teamwork and role-allocation with real robots

We report on recent work in which we employed artificial evolution to design neural network controllers for small, homogeneous teams of mobile autonomous robots. The robots are evolved to perform a formation movement task from random starting positions, equipped only with infrared sensors. The dual constraints of homogeneity and minimal sensors make this a non-trivial task. We describe the behaviour of a successful evolved team in which robots adopt and maintain functionally distinct roles in order to achieve the task. We believe this to be the first example of the use of artificial evolution to design coordinated, cooperative behaviour for real robots.
Research Statement—detailed Version

We live amidst complex systems such as the Internet as well as advances like speech recognition, Mars missions and genome mapping, made possible by the unprecedented communication, computation , and storage resources available today. Modern research attempts to further these advances, and simultaneously harness available knowledge and data better. Two aspects of this effort stand out. First—while a lot of statistical work has been answering questions on sequences of symbols much longer than the set of possible symbols, the alphabet size—a number of current research problems require solutions for the opposite scenario, the large alphabet setting. For instance, take speech recognition—language models for speech recognition estimate distributions over English words using text examples much smaller than the vocabulary. Or modern medicine— thousands of genes are clustered by their expression levels for applications in diagnosis and drug response prediction. Tweaking old results and using them for large alphabets yields unsatisfactory results in many cases, and we are therefore forced to rework these problems altogether. Second, problems posed by different systems are interconnected. But progress among interconnected problems do not always match up, typically due to lack of communication between the relevant fields. For example, consider text compression on the one hand, along with language modeling for speech recognition which requires estimation of word probabilities given a text sample. It is folklore that compression and estimation are closely linked, but some very commonly used probability estimators in the machine learning community had not even been considered from a compression perspective till recently [1]. The theoretical focus of this statement will be the intersection of statistical learning and information theory. In particular, we concentrate on probability estimation, inference, classification and clustering in large alphabet settings. Practical applications include text classification and compression, image segmentation, analysis of gene expression data, and risk analysis for network insurance policies. Many principles behind the approaches we take for the above problems are not just new and theoretically interesting, but have proved to be general in nature. As a demonstration, text classifiers based on these principles are comparable to the state of the art results (and faster) on several real life benchmarks using nothing but simple minded implementations of our algorithms, no tuning, no tweaking. We start with some background on these problems, starting with compression and probability estimation. 1 Background 1.1 Universal data compression and large alphabets While Huffman or Shannon codes achieve compression of data by …
EEG controlled remote robotic system from motor imagery classification

This paper aims at laying a foundation towards the development of a robust platform for efficient control of the motion of autonomous mobile robots. Electroencephalographic (EEG) signals liberated during motor imagery of a human controller have been used to design the control mechanism. The proposed scheme can find widespread applications in the defense sector as secrecy of generated commands can be maintained efficiently. With decoding of the brain signals for various limb movements being a major area of research for EEG based BCI, the paper employs the usage of finger-elbow-shoulder movement classification in addition to the left-right arm movement classification. The proposed system integrates the advantages of high classification accuracy of EEG measurements (more than 80% for both the folds, i.e., Left-Right classification and Finger-Elbow-Shoulder classification) along with a suitable coding technique of liberated control signals for error detection thereby paving the way for an efficient EEG signal encoded robot control system design.
Activity Based Matching in Distributed Camera Networks

In this paper, we consider the problem of finding correspondences between distributed cameras that have partially overlapping field of views. When multiple cameras with adaptable orientations and zooms are deployed, as in many wide area surveillance applications, identifying correspondence between different activities becomes a fundamental issue. We propose a correspondence method based upon activity features that, unlike photometric features, have certain geometry independence properties. The proposed method is robust to pose, illumination and geometric effects, unsupervised (does not require any calibration objects). In addition, these features are amenable to low communication bandwidth and distributed network applications. We present quantitative and qualitative results with synthetic and real life examples, and compare the proposed method with scale invariant feature transform (SIFT) based method. We show that our method significantly outperforms the SIFT method when cameras have significantly different orientations. We then describe extensions of our method in a number of directions including topology reconstruction, camera calibration, and distributed anomaly detection.
An Adaptively Accelerated Bayesian Deblurring Method with Entropy Prior

The development of an efficient adaptively accelerated iterative deblurring algorithm based on Bayesian statistical concept has been reported. Entropy of an image has been used as a "prior" distribution and instead of additive form, used in conventional acceleration methods an exponent form of relaxation constant has been used for acceleration. Thus the proposed method is called hereafter as adaptively accelerated maximum a posteriori with entropy prior (AAMAPE). Based on empirical observations in different experiments, the exponent is computed adaptively using first-order derivatives of the deblurred image from previous two iterations. This exponent improves speed of the AAMAPE method in early stages and ensures stability at later stages of iteration. In AAMAPE method, we also consider the constraint of the nonnegativity and flux conservation. The paper discusses the fundamental idea of the Bayesian image deblurring with the use of entropy as prior, and the analytical analysis of superresolution and the noise amplification characteristics of the proposed method. The experimental results show that the proposed AAMAPE method gives lower RMSE and higher SNR in 44% lesser iterations as compared to nonaccelerated maximum a posteriori with entropy prior (MAPE) method. Moreover, AAMAPE followed by wavelet wiener filtering gives better result than the state-of-the-art methods.
Android Provenance: Diagnosing Device Disorders

Mobile devices are a ubiquitous part of our daily lives. Smartphones are being used in many areas where data privacy and integrity are a concern. One threat to integrity and privacy is the existence of bugs in operating system code. Little has been done to provide tools for system-wide runtime profiling and accountability. We propose operating system auditing and data provenance tracking as mechanisms for generating useful traces of system activity and information flow on mobile devices. The goal of these traces is to enable debugging and profiling of complicated system issues such as increased power drain. We contribute a prototype system for Android-based mobile devices and provide realistic examples of how our system can be used for debugging.
Convergence Results on Proximal Method of Multipliers in Nonconvex Programming

We describe a primal-dual application of the proximal point algorithm to nonconvex minimization problems. Motivated by the work of Spingarn and more recently by the work of Kaplan and Tichatschke about the proximal point methodology in nonconvex optimization. This paper discusses some local results in two directions. The rst one concerns the application of the proximal method of multipliers to a general nonconvex problem under second order optimality conditions. Secondly we show that without the second order statements, local convergence is obtained for a particular class of nonconvex programs.