Miracle-dz/UniFAR-FTUs huggingface dataset:
Disability as infra-critique: a compositionist approach to the election process in Denmark

ABSTRACT This article investigates how disability can work analytically as a ‘critique from within’. Our case is the accommodation of citizens with disabilities during the voting process in Denmark. Here disability makes explicit how Danish democracy is produced as disability rubs up against implicit, normalized and mundane infrastructures and practices. We investigate disability as critique in this sense of affording a both analytic and practical ‘breakup’. To do so, we promote a ‘compositionist’ post-actor-network theory approach to disability and to polling and investigate what entry-point for critique this offers. We analyze an incident at a polling booth during the 2013 Danish Municipal election. This renders visible some of the complex socio-material processes through which citizens and the Danish state co-enact and co-authorize one another. We highlight how ‘detachments’ are vital to such processes and we examine parts of the historical background for the production of authority in the context of managing disability as exception during polling. In doing so we point out that as the organization of electoral processes evolves, new potentialities for infra-critique also emerge.
Model Uncertainty and its Impact on Derivative Pricing ∗

Financial derivatives written on an underlying can normally be priced and hedged accurately only after a suitable mathematical model for the underlying has been determined. This chapter explains the difficulties in finding a (unique) realistic model — model uncertainty. If the wrong model is chosen for pricing and hedging, unexpected and unwelcome financial consequences may occur. By wrong model we mean either the wrong model type (specification uncertainty) or the wrong model parameter (parameter uncertainty). In both cases, the impact of model uncertainty on pricing and hedging is significant. A variety of measures are introduced to value the model uncertainty of derivatives and a numerical example again confirms that these values are a significant proportion of the derivative price. In this introductory section, we will look at various ways in which the model selection problem may manifest itself and the consequences of this for derivative pricing.
Identification of the Key Genes and Pathways in Esophageal Carcinoma

Objective. Esophageal carcinoma (EC) is a frequently common malignancy of gastrointestinal cancer in the world. This study aims to screen key genes and pathways in EC and elucidate the mechanism of it. Methods. 5 microarray datasets of EC were downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) were screened by bioinformatics analysis. Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction (PPI) network construction were performed to obtain the biological roles of DEGs in EC. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression level of DEGs in EC. Results. A total of 1955 genes were filtered as DEGs in EC. The upregulated genes were significantly enriched in cell cycle and the downregulated genes significantly enriched in Endocytosis. PPI network displayed CDK4 and CCT3 were hub proteins in the network. The expression level of 8 dysregulated DEGs including CDK4, CCT3, THSD4, SIM2, MYBL2, CENPF, CDCA3, and CDKN3 was validated in EC compared to adjacent nontumor tissues and the results were matched with the microarray analysis. Conclusion. The significantly DEGs including CDK4, CCT3, THSD4, and SIM2 may play key roles in tumorigenesis and development of EC involved in cell cycle and Endocytosis.
Hierarchical Takagi-Sugeno Models for Online Security Evaluation Systems

Risk assessment is often done by human experts, because there is no exact and mathematical solution to the problem. Usually the human reasoning and perception process cannot be expressed precisely. This paper propose a light weight risk assessment system based on an Hierarchical Takagi-Sugeno model designed using evolutionary algorithms. Performance comparison is done with neuro-fuzzy and genetic programming methods. Empirical results indicate that the techniques are robust and suitable for developing light weight risk assessment models, which could be integrated with intrusion detection and prevention systems.
Buffer Cache Management for Combined MLC and SLC Flash Memories Using both Volatile and Nonvolatile RAMs

This paper presents a new buffer cache management scheme called DABC-NV for mixed MLC and SLC flash memories as the secondary storage and both byte-accessible NVRAM and conventional volatile RAM as their buffer caches. DABC-NV has four salient features. First, it allocates buffer cache space to MLC and SLC flash memories based on their I/O costs and then dynamically adjusts the allocated size according to the evolution of workloads. Second, it separately exploits read and write histories of block references, and thus it estimates future references of each operation more precisely. Third, it guarantees the complete consistency of write I/Os since all dirty data are cached in nonvolatile buffer caches. Fourth, metadata lists are maintained separately from cached blocks. This allows more efficient management of volatile and nonvolatile buffer caches based on read and write histories, respectively. Trace-driven simulations show that DABC-NV improves the I/O performance of embedded systems significantly. Specifically, it reduces I/O time by 24% on average compared to the CLOCK-NV algorithm.
A Search-Based Dynamic Reranking Model for Dependency Parsing

We propose a novel reranking method to extend a deterministic neural dependency parser. Different to conventional k-best reranking, the proposed model integrates search and learning by utilizing a dynamic action revising process, using the reranking model to guide modiﬁcation for the base outputs and to rerank the candidates. The dynamic reranking model achieves an absolute 1.78% accuracy improvement over the deterministic baseline parser on PTB, which is the highest improvement by neural rerankers in the literature.
Using penalized spline regression to calculate mean trajectories including confidence intervals of human motion data

Research in motion planning for mobile robots increasingly focuses on modeling human-like motions and behaviors. Applied to robots, these models help generating motions that are intuitively comprehensible for a human interaction partner. However, identifying the underlying parameters of such human motion models is challenging. These parameters are commonly estimated by analyzing measured single trajectories or means of trajectory sets. Indeed, raw trajectories as well as the means are often not representative for the data, as measurements are noisy and the amount of generated data is limited. For a reasonable analysis it is necessary to smooth the data and estimate an according confidence interval for the mean. In this paper we apply penalized splines to smooth single trajectories and to estimate means of trajectory sets, which ensures little distortion of the original data. Based on that, a method is presented that yields a confidence interval for the mean of human motion data. In order to cope with unknown distributions and small datasets our method employs bootstrapping. The analysis based on confidence intervals takes the variance of the data into account and allows for reasonable conclusions about underlying human motion parameters.
Brochette: Toward A Continuous Learning Platform for Knowledge Acquisition and Integration

Question-Answering (QA) systems like IBM Watson are particularly challenging to design and need to cover areas including computational linguistics, information retrieval, knowledge representation and reasoning, and machine learning. ‘Exobrain’ is a Korean Research Program which aims at building such a high-performing QA system for the Korean Language. In this paper, we describe ‘Brochette’, a continuous learning platform that iteratively acquires a large volume of unstructured data and extracts sentences related to entries of an ontology, a formal representation of knowledge used for further queries and reasoning purposes. As various Machine Learning modules must be trained and tuned to suit new content and adapt to new domains, Brochette platform also provides a framework for continuously re-training and evaluating machine learning components for syntactic and semantic analysis. Integrating semantic information from the ontology and results of these machine learners, the system is able to discover new knowledge which is then incorporated again in the Knowledge Base, making it continuously evolving.
Trek : Testable Replicated Key-Value Store

This paper describes the implementation of Trek, a testable, replicated key-value store with ZooKeeper-like semantics. The system adapts the revisited version of Viewstamped Replication to provide availability and fault tolerance, and contains a comprehensive testing and monitoring system for simulating both node crash and network failures, and for monitoring system operation.
Joint Detection and Tracking for Multipath Targets: A Variational Bayesian Approach

Different from traditional point target tracking systems assuming that a target generates at most one single measurement per scan, there exists a class of multipath target tracking systems where each measurement may originate from the interested target via one of multiple propagation paths or from clutter, while the correspondence among targets, measurements, and propagation paths is unknown. The performance of multipath target tracking systems can be improved if multiple measurements from the same target are effectively utilized, but suffers from two major challenges. The first is multipath detection that detects appearing and disappearing targets automatically, while one target may produce $s$ tracks for $s$ propagation paths. The second is multipath tracking that calculates the target-to-measurement-to-path assignment matrices to estimate target states, which is computationally intractable due to the combinatorial explosion. Based on variational Bayesian framework, this paper introduces a novel probabilistic joint detection and tracking algorithm (JDT-VB) that incorporates data association, path association, state estimation and automatic track management. The posterior probabilities of these latent variables are derived in a closed-form iterative manner, which is effective for dealing with the coupling issue of multipath data association identification risk and state estimation error. Loopy belief propagation (LBP) is exploited to approximate the multipath data association, which significantly reduces the computational cost. The proposed JDT-VB algorithm can simultaneously deal with the track initiation, maintenance, and termination for multiple multipath target tracking with time-varying number of targets, and its performance is verified by a numerical simulation of over-the-horizon radar.
HIGHER CRITICISM STATISTIC: THEORY AND APPLICATIONS IN NON-GAUSSIAN DETECTION

Higher Criticism is a statistic recently proposed by Donoho and Jin5. It has been shown to be effective in resolving a very subtle testing problem: whether n normal means are all zero versus a small fraction is nonzero. Higher Criticism is also useful for non-Gaussian detection in Cosmic Microwave Background (CMB) data. In this report, we review the theory developed in Donoho and Jin5 and discuss the use of Higher Criticism for two settings: detecting the non-Gaussian component in a superposed image of CMB and cosmic strings (CS), and detecting non-Gaussianity in the WMAP first year data.
Chinese University of Hong Kong at TRECVID 2006: Shot Boundary Detection and Video Search

In this paper, we describe our methodologies and empirical evaluations for the shot boundary detection and automatic video search tasks at TRECVID 2006. For the shot boundary detection task, we consider a simple and efficient solution. Our approach first applies adaptive thresholding on color histogram differences between frames to select candidates for shot boundaries, then runs several tests to dismiss less likely ones. These tests proved to be too finely tuned to the TRECVID 2005 shot boundary detection task data, producing mediocre results on the 2006 data set. For the video search task, we propose a novel multimodal and multilevel ranking scheme for video ranking. Different from traditional ranking schemes, most of them are based on supervised approaches, our approach suggests a semi-supervised ranking method which can exploit both labeled and unlabeled data effectively for the ranking task. At the meantime, our multilevel approach makes the semi-supervised ranking method efficient for large-scale search task in practice. We will evaluate the empirical performance of our approaches and give comments of our solution.
Impact of Work Life Conflict on Job Satisfaction: Special Reference to Ceylon Electricity Board-Sabaragamuwa Provincial Office

Ceylon Electricity Board (CEB) is a body corporate for electricity undertaking in Sri Lanka. It conducts its massive duties to the Sri Lankan public though generation, transmission, distribution & other services. One of the major problems with the CEB work force is, many non- executive staff in CEB Sabaragamuwa were facing problem of work life conflict situation. At present the conflict between work and family has become a crisis for organizations. With the increase in dual family parents, more job demand and long working hours made it almost impossible to create a balance between work and family. This situation automatically reduces job satisfaction of the employees and then affect to the productivity and profitability of the organization. The overall objective of this research was to analyze the factors related to work life conflict and its impact on job satisfaction at CEB-Sabaragamuwa Provincial Office. The study utilized primary data and a sample of 120 chose in accordance census method including non-executive staff employees in CEB-Sabaragamuwa Provincial Office using already developed self administered questionnaire. Pearson correlation test and Regression analysis was carried out to develop the model. The results revealed that the job satisfaction is inversely affected by the two aspects of work life conflict, work to family conflict and family to work conflict. Further, family to work conflict identified as the most critical predictor. Ultimately work life conflict of non-executive staff employees of CEB negatively impacted on their job satisfaction. The relationship between work life conflict and job satisfaction was contrary to each other. It would be worthwhile for CEB to increase the Job Satisfaction of non-executive staff employees through introducing work life balance policies. Keywords: Job Satisfaction, Work Life Conflict, Work to Family Conflict, Family to Work Conflict
Detecting Unipolar and Bipolar Depressive Disorders from Elicited Speech Responses Using Latent Affective Structure Model

Mood disorders, including unipolar depression (UD) and bipolar disorder (BD) [1] , are reported to be one of the most common mental illnesses in recent years. In diagnostic evaluation on the outpatients with mood disorder, a large portion of BD patients are initially misdiagnosed as having UD [2] . As most previous research focused on long-term monitoring of mood disorders, short-term detection which could be used in early detection and intervention is thus desirable. This work proposes an approach to short-term detection of mood disorder based on the patterns in emotion of elicited speech responses. To the best of our knowledge, there is no database for short-term detection on the discrimination between BD and UD currently. This work collected two databases containing an emotional database (MHMC-EM) collected by the Multimedia Human Machine Communication (MHMC) lab and a mood disorder database (CHI-MEI) collected by the CHI-MEI Medical Center, Taiwan. As the collected CHI-MEI mood disorder database is quite small and emotion annotation is difficult, the MHMC-EM emotional database is selected as a reference database for data adaptation. For the CHI-MEI mood disorder data collection, six eliciting emotional videos are selected and used to elicit the participants’ emotions. After watching each of the six eliciting emotional video clips, the participants answer the questions raised by the clinician. The speech responses are then used to construct the CHI-MEI mood disorder database. Hierarchical spectral clustering is used to adapt the collected MHMC-EM emotional database to fit the CHI-MEI mood disorder database for dealing with the data bias problem. The adapted MHMC-EM emotional data are then fed to a denoising autoencoder for bottleneck feature extraction. The bottleneck features are used to construct a long short term memory (LSTM)-based emotion detector for generation of emotion profiles from each speech response. The emotion profiles are then clustered into emotion codewords using the K-means algorithm. Finally, a class-specific latent affective structure model (LASM) is proposed to model the structural relationships among the emotion codewords with respect to six emotional videos for mood disorder detection. Leave-one-group-out cross validation scheme was employed for the evaluation of the proposed class-specific LASM-based approaches. Experimental results show that the proposed class-specific LASM-based method achieved an accuracy of 73.33 percent for mood disorder detection, outperforming the classifiers based on SVM and LSTM.
TranSegId: A System for Concurrent Speech Transcription, Speaker Segmentation and Speaker Identification

A system to analyze live audio feeds using speech and speaker recognition is described. Multi-modal analysis of this nature calls for concurrent operations to transcribe speech, to partition it into acoustically homogeneous, single-speaker segments, and to identify the speaker in these segments. The architecture of such a system built at IBM is described along with the techniques used in the individual modules involved. The output of this system can be used in searching through audio and video clips, tracking speakers in multiple broadcast feeds, and in other cataloging tasks.
The Molecular Basis of Neural Memory. Part 7 Neural Intelligence (NI) versus Artificial Intelligence (AI)

The link of memory to intelligence is incontestable, though the development of electronic artifacts with memory has confounded cognitive and computer scientists’ conception of memory and its relevance to “intelligence”. We propose two categories of “Intelligence”: (1) Logical (objective) — mathematics, numbers, pattern recognition, games, programmable in binary format. (2) Emotive (subjective) — sensations, feelings, perceptions, goals desires, sociability, sex, food, love. The 1 st has been reduced to computational algorithms of which we are well versed, witness global technology and the internet. The 2 nd relates to the mysterious process whereby (psychic) emotive states are achieved by neural beings sensing, comprehending, remembering and dealing with their surroundings. Many theories and philosophies have been forwarded to rationalize this process, but as neuroscientists, we remain dissatisfied. Our own musings on universal neural memory, suggest a tripartite mechanism involving neurons interacting with their surroundings, notably the neural extracellular matrix (nECM) with dopants [trace metals and neurotransmitters (NTs)]. In particular, the NTs are the molecular encoders of emotive states. We have developed a chemographic representation of such a molecular code. To quote Longuet-Higgins, “ Perhaps it is time for the term ‘artificial intelligence’ to be replaced by something more modest and less provisional ”. We suggest “artifact intelligence” (ARTI) or “machine intelligence” (MI), neither of which imply emulation of emotive neural processes, but simply refer to the ‘demotive’ (lacking emotive quality) capability of electronic artifacts that employ a recall function, to calculate algorithms.
Investigation of Path Loss of Mobile Radio Services at L-Band Frequency over Akure, South Western, Nigeria

As the migration of wireless communication network from one generation to another continue, radio wave propagation model or path loss model plays a very significant role in achieving an optimum performance as well as suitability over different locations of interest. In this research, investigation of radio communication channel behavior based on practical measurement in the L-band frequency in Akure- one of the dense urban regions of southwestern part of Nigeria has been carried out. A total number of ten base stations of MTN GSM service located across several areas in Akure were monitored. Path loss and break point distances was deduced from the Received Signal Strength (RSS) and compared with some existing path loss models. The mean and Standard deviation (SD) of each path loss model and measured path loss were estimated and compared on environmental basis; urban and suburban. Based on the regression line (log-normal model) of the measured exponent, Erceg and Cost-231 Hata path loss models show great consistency when compared with Ericson's model with Cost-231 Hata model showing the closest fit to the measured values. The results show that either the log-normal or Cost 231 Hata models may be used to estimate the path loss in mobile micro cell coverage in this region. Path loss exponents, break point distances for each location as well as fitted parameters for the suitable models were also derived. Information from this work will be useful for channel design of micro-wave band
Angiotensin II receptor blocker shows antiproliferative activity in prostate cancer cells: a possibility of tyrosine kinase inhibitor of growth factor.

Angiotensin II (A-II) receptor (AT(1) receptor) blockers (ARB) are a class of antihypertensive agent. It is known that they suppress signal transduction pathways mediated by growth factors [e.g., epidermal growth factor (EGF)] through the AT(1) receptor in vascular endothelial cells. In the present study, we demonstrated that A-II activates the cell proliferation of prostate cancer as well as EGF. In addition, we showed that A-II induces the phosphorylations of mitogen-activated protein kinase (MAPK) and signal transducer and activator of transcription 3 (STAT3) in prostate cancer cells. In contrast, ARB was shown to inhibit the proliferation of prostate cancer cells stimulated with EGF or A-II, the mechanism of which is through the suppression of MAPK or STAT3 phosphorylation by ARB. Oral administration of ARB to nude mice inhibited the growth of prostate cancer xenografts in both androgen-dependent and androgen-independent cells in a dose-dependent manner. Microvessel density was reduced in xenografts treated with ARB, which means ARB inhibits the vascularization of xenografts. Expression of AT(1) receptor mRNA was examined by reverse transcription-PCR using 10 pairs of human prostate cancer and normal prostate tissues. AT(1) receptor expression in human prostate cancer tissue was higher (9 of 10 cases) than that in normal prostate tissue. These results suggest the possibility that ARB is a novel therapeutic class of agents for prostate cancer, especially hormone-independent tumors.