Miracle-dz/UniFAR-FTUs huggingface dataset:
Parathyroid hormone resets the cartilage circadian clock of the organ-cultured murine femur

Background and purpose — The circadian clock governs endogenous day-night variations. In bone, the metabolism and growth show diurnal rhythms. The circadian clock is based on a transcription-translation feedback loop composed of clock genes including Period2 (Per2), which encodes the protein period circadian protein homolog 2. Because plasma parathyroid hormone (PTH) levels show diurnal variation, we hypothesized that PTH could carry the time information to bone and cartilage. In this study, we analyzed the effect of PTH on the circadian clock of the femur. Patients and methods — Per2::Luciferase (Per2::Luc) knock-in mice were used and their femurs were organ-cultured. The bioluminescence was measured using photomultiplier tube-based real-time bioluminescence monitoring equipment or real-time bioluminescence microscopic imaging devices. PTH or its vehicle was administered and the phase shifts were calculated. Immunohistochemistry was performed to detect PTH type 1 receptor (PTH1R) expression. Results — Real-time bioluminescence monitoring revealed that PTH reset the circadian rhythm of the Per2::Luc activity in the femurs in an administration time-dependent and dose-dependent manner. Microscopic bioluminescence imaging revealed that Per2::Luc activity in the growth plate and the articular cartilage showed that the circadian rhythms and their phase shifts were induced by PTH. PTH1R was expressed in the growth plate cartilage. Interpretation — In clinical practice, teriparatide (PTH (1-34)) treatment is widely used for osteoporosis. We found that PTH administration regulated the femoral circadian clock oscillation, particularly in the cartilage. Regulation of the local circadian clock by PTH may lead to a more effective treatment for not only osteoporosis but also endochondral ossification in bone growth and fracture repair.
Data Node Splitting Policies for Improved Range Query Efficiency in k-dimensional Point Data Indexes

High dimensional vectors (points) are very common in image and video classification, time series data mining, and many modern data mining applications. One of the most popular classification methods on such data is k-Nearest Neighbor (kNN) searching. Unfortunately, all proposed and state-of-the-art multi-attribute indexes fall short in terms of usability as dimensionality increases. This is attributed to the ``dimensionality curse" problem, according to which, range searching above 10 dimensions is as efficient as a sequential scan of the entire database. Thus, kNN searching, as a special case of range searching, has to benefit a lot if we find ways to increase the performance of indexes in high dimensions. In this paper, we deal with space partitioning indexes and we propose six data node splitting techniques. We examine their performance in terms of data node storage utilization and quality of space partitioning. These two conflicting goals are both essential for good range query performance. Our experiments with uniform and skewed data demonstrate that certain splitting techniques can perform satisfactorily.
Two and a Half Cycles of Foresight in the UK

Technikfolgenabschätzung – Theorie und Praxis Nr. 2, 12. Jg. , Juni 2003 Seite 41 vorher nicht bekannte Tatsachen korrigiert, sie ist den Erwartungen der Bevölkerung angepasst. Jene wiederum, durch Einsicht in wissenschaftliche sowohl als politische Zusammenhänge, konkretisieren sich von bloßen Wünschbarkeiten zu vertretbaren Forderungen“ (S. 51). Ein Klassiker bemisst sich auch daran, dass die in ihm formulierten Gedanken, auch nach Jahren und Jahrzehnten Bestand haben. Krauchs „Forschungsprioritäten“ kann man durchaus als „blueprint“ für die heutige, so genannte dritte Foresight-Generation ansehen. Dieses Buch, auch nach über 30 Jahren wieder zu lesen, ist auf jeden Fall lohnend.
Query expansion using explicit semantic analysis

Query expansion is a technique utilized within information retrieval to solve word mismatch between queries and document. In previous method, expansion words are usually selected by counting word co-occurrences in the documents. However, word co-occurrences are not always a good indicator for relevance, whereas some are background words of the whole collection. In order to select good expansion words, explicit semantic analysis (ESA) is adopted in our model to estimate two kinds of relevance weight. One is the relevance weight between query and its relevant word extracted from the top-ranked documents in initial retrieval results. The other is the relevance weight between each query word and its relevant words extracted from the snapshot of Google search result when that query word is used as search keyword. The estimated relevance weights are used to select good expansion words for second retrieval. The experiments on the three test collections show that our expansion words selection model is more effective than the standard Rocchio expansion.
Extension of Service-Oriented Component Models for Dynamic Environment

In recent years, service-oriented computing has been successfully adopted by the industry and used in numerous projects. This raises however new challenges, especially with respect to service selection and ranking in dynamic environments. Selection and ranking are mostly based on first-order predicates and comparison functions, expressed in the consumer side. Such approaches suffer from several shortcomings. The expression language is too complex for most non-technical persons and yet not powerful enough to capture external information, which limits the use of contextual information. Also, current solutions lack flexibility to handle dynamic environments. This paper proposes to integrate algorithms based on the Formal Concept Analysis theory to extend service-oriented component models. The resulting framework externalizes service selection and ranking. Results are integrated in the Apache Felix iPOJO component model.
Robust estimation of directions-of-arrival in diffuse noise based on matrix-space sparsity

We consider the estimation of the Directions-Of-Arrival (DOA) of target signals in diffuse noise. The state-of-the-art MUltiple SIgnal Classification (MUSIC) algorithm necessitates accurate identification of the signal subspace. In diffuse noise, however, it is difficult to identify it directly from the observed spatial covariance matrix. In our approach, we estimate the target spatial covariance matrix, so that we can identify the orthogonal complement of the signal subspace as its null space. We present a unified framework for modeling noise covariance in a matrix space, which generalizes four state-of-the-art diffuse noise models. We propose two alternative algorithms for estimating the target spatial covariance matrix, namely Low-rank Matrix Completion (LMC) and Trace Norm Minimization (TNM). These rely on denoising of the observed spatial covariance matrix via orthogonal projection onto the orthogonal complement of the noise matrix subspace. The missing component lying in the noise matrix subspace is then completed by exploiting the low-rankness of the target spatial covariance matrix. Large-scale experiments with real-world noise show that TNM with a certain noise model outperforms conventional MUSIC based on Generalized EigenValue Decomposition (GEVD) by 5% in terms of the precision averaged over the dataset.
Data-driven approach for ontology learning

In this paper an approach to construct ontologies based on a text corpus is described. Using Latent Dirichlet Allocation the topics that describe the documents contained in the corpus are identified. Each topic is formed by a set of terms whose semantic relatednesses are determined applying the distributional hypothesis, which considers as similar terms those that share the similar linguistic context. This context is described by the verbs they share. The concept described by each topic's terms is modeled through a taxonomy that describes the relation between them.
Sanskrit Linguistics Web Services

We propose to demonstrate a collection of tools for Sanskrit Computational Linguistics developed by cooperating teams in the general setting of Web services. These services oer a systematic architecture integrating multilingual lexicons, morphological generation and analysis, segmentation and parsing, and interlink with the Sanskrit Library digital repository. They may be used as distributed Internet services, or installed as local tools on individual users workstations. 1 Community building Sanskrit is the primary culture-bearing language of India, with a continuous production of literature in all fields of human endeavour over the course of four millennia. It benefited from a strong linguistics tradition, established from early times, and notably from the grammar
Spoken Language Translation for Polish

Spoken language translation (SLT) is becoming more important in the increasingly globalized world, both from a social and economic point of view. It is one of the major challenges for automatic speech recognition (ASR) and machine translation (MT), driving intense research activities in these areas. While past research in SLT, due to technology limitations, dealt mostly with speech recorded under controlled conditions, today's major challenge is the translation of spoken language as it can be found in real life. Considered application scenarios range from portable translators for tourists, lectures and presentations translation, to broadcast news and shows with live captioning. We would like to present PJIIT's experiences in the SLT gained from the Eu-Bridge 7th framework project and the U-Star consortium activities for the Polish/English language pair. Presented research concentrates on ASR adaptation for Polish (state-of-the-art acoustic models: DBN-BLSTM training, Kaldi: LDA+MLLT+SAT+MMI), language modeling for ASR & MT (text normalization, RNN-based LMs, n-gram model domain interpolation) and statistical translation techniques (hierarchical models, factored translation models, automatic casing and punctuation, comparable and bilingual corpora preparation). While results for the well-defined domains (phrases for travelers, parliament speeches, medical documentation, movie subtitling) are very encouraging, less defined domains (presentation, lectures) still form a challenge. Our progress in the IWSLT TED task (MT only) will be presented, as well as current progress in the Polish ASR.
AEON --An approach to the automatic evaluation of ontologies

OntoClean is an approach towards the formal evaluation of taxonomic relations in ontologies. The application of OntoClean consists of two main steps. First, concepts are tagged according to meta-properties known as rigidity, unity, dependency and identity. Second, the tagged concepts are checked according to predefined constraints to discover taxonomic errors. Although OntoClean is well documented in numerous publications, it is still used rather infrequently due to the high costs of application. Especially, the manual tagging of concepts with the correct meta-properties requires substantial efforts of highly experienced ontology engineers. In order to facilitate the use of OntoClean and to enable the evaluation of real-world ontologies, we provide AEON, a tool which automatically tags concepts with appropriate OntoClean meta-properties and performs the constraint checking. We use the Web as an embodiment of world knowledge, where we search for patterns that indicate how to properly tag concepts. We thoroughly evaluated our approach against a manually created gold standard. The evaluation shows the competitiveness of our approach while at the same time significantly lowering the costs. All of our results, i.e. the tool AEON as well as the experiment data, are publicly available.
Wheelchair Movement Control VIA Human Eye Blinks

Many disorders can disrupt the neuromuscular channels used by the brain to communicate with and control its external environment. Patients with severe neural disorders lose most of voluntary muscle control. Some patients can con- trol their eye movements and may be able to communicate. In the absence of methods for repairing the damage done by these disorders, the only option for restoring function to those with motor impairments is to provide the brain with a new, muscular/non-muscular and non invasive communication and control channel, a direct Brain Machine Interface (BMI) for conveying messages and commands to the external world. An attempt has been provided to collect the brain activities and extract specific estimations to control the wheelchair movement. The data has been collected though fourteen electrodes fixed on the scalp by modern strategy (without non-drying conductive past). This method has used a wireless communica- tion head set based on the hydration sensors and Bluetooth technology. Four movements are detected (turn right - turn left - forward - stop) based on the eye blinks (right wing, left wing, single/double blinks). The wavelet has used to extract best signals and NN system to take the learning sessions and give the required action.
PID Coefficient Design for the Automatic Voltage Regulator Using an Adaptive Inertia Particle Swarm Optimization

Abstract-In this paper, we present a new particle swarm optimization. The original PSO has a weight term which is decreasing, increasing, or constant during iterations. In this paper, inertia terms are a vector instead of a scalar. Comparing a velocity and an updating term, the weight can be increased or decreased. That is, if the absolute value of velocity is larger or lesss than that of the update term, then the weight is increased or decreased, respectively. With the so-called adaptive inertia PSO algorithm we design PID coefficients for the automatic voltage regulators. We show that the proposed algorithm has a better performance than the original PSO under some conditions for the PID design of automatic voltage regulators.
Estimating risk of cancer associated with radiation exposure from 64-slice computed tomography coronary angiography.

CONTEXT Computed tomography coronary angiography (CTCA) has become a common diagnostic test, yet there are little data on its associated cancer risk. The recent Biological Effects of Ionizing Radiation (BEIR) VII Phase 2 report provides a framework for estimating lifetime attributable risk (LAR) of cancer incidence associated with radiation exposure from a CTCA study, using the most current data available on health effects of radiation. OBJECTIVES To determine the LAR of cancer incidence associated with radiation exposure from a 64-slice CTCA study and to evaluate the influence of age, sex, and scan protocol on cancer risk. DESIGN, SETTING, AND PATIENTS Organ doses from 64-slice CTCA to standardized phantom (computational model) male and female patients were estimated using Monte Carlo simulation methods, using standard spiral CT protocols. Age- and sex-specific LARs of individual cancers were estimated using the approach of BEIR VII and summed to obtain whole-body LARs. MAIN OUTCOME MEASURES Whole-body and organ LARs of cancer incidence. RESULTS Organ doses ranged from 42 to 91 mSv for the lungs and 50 to 80 mSv for the female breast. Lifetime cancer risk estimates for standard cardiac scans varied from 1 in 143 for a 20-year-old woman to 1 in 3261 for an 80-year-old man. Use of simulated electrocardiographically controlled tube current modulation (ECTCM) decreased these risk estimates to 1 in 219 and 1 in 5017, respectively. Estimated cancer risks using ECTCM for a 60-year-old woman and a 60-year-old man were 1 in 715 and 1 in 1911, respectively. A combined scan of the heart and aorta had higher LARs, up to 1 in 114 for a 20-year-old woman. The highest organ LARs were for lung cancer and, in younger women, breast cancer. CONCLUSIONS These estimates derived from our simulation models suggest that use of 64-slice CTCA is associated with a nonnegligible LAR of cancer. This risk varies markedly and is considerably greater for women, younger patients, and for combined cardiac and aortic scans.
Productivity Spillover of Foreign Direct Investment: A Computable General Equilibrium Model of China

A computable general equilibrium (CGE) model is constructed to capture the endogenous productivity spillover from foreign-invested firms to domestic firms in China. This paper summarizes the main results we have found so far. There are three contributions we have made to the knowledge. First and foremost, the endogenous productivity increase in manufacturing sectors caused by FDI spillover has been successfully introduced into a CGE model. The productivity spillover effects in both perfectly competitive and imperfectly competitive markets are also compared. This technique can be readily applied to a global dynamic CGE model embodying trade-FDI-productivity spillover linkages. Second, we have estimated and compared the importance of four spillover channels in econometric analyses and the CGE model. Third, we have made a tentative assessment of Chinese FDI policies characterised by swapping market access for technology with the CGE prototype model. There are various directions worth future research, e.g. to estimate productivity spillover parameters by industries with firm-level data, to extend the research to include spillovers in services sectors, and to construct a global dynamic model capturing capital accumulation and trade-FDI-productivity interactions.
An Efficient Data-Driven Clustering Technique to Detect Attacks in SCADA Systems

Supervisory control and data acquisition (SCADA) systems have become a salient part in controlling critical infrastructures, such as power plants, energy grids, and water distribution systems. In the past decades, these systems were isolated and use proprietary software, operating systems, and protocols. In recent years, SCADA systems have been interfaced with enterprise systems, which therefore exposed them to the vulnerabilities of the Internet and the security threats. Traditional security solutions (e.g., firewalls, antivirus software, and intrusion detection systems) cannot fully protect SCADA systems, because they have different requirements. This paper presents an innovative intrusion detection approach to detect SCADA tailored attacks. This is based on a data-driven clustering technique of process parameters, which automatically identifies the normal and critical states of a given system. Later, it extracts proximity-based detection rules from the identified states for monitoring purposes. The effectiveness of the proposed approach is tested by conducting experiments on eight data sets that consist of process parameters' values. The empirical results demonstrated an average accuracy of 98% in automatically identifying the critical states, while facilitating the monitoring of the SCADA system.
A Review of QoS in Wireless Sensor Networks

— The adoption of wireless sensor networks by applications that require complex operations, ranging from health care to industrial monitoring, has brought forward a new challenge of fulfilling the quality of service (QoS) requirements of these applications. However, providing QoS support is a challenging issue due to highly resource constrained nature of sensor nodes, unreliable wireless links and harsh operation environments. In this paper, we focus on the QoS support at the MAC layer which forms the basis of communication stack and has the ability to tune key QoS-specific parameters, such as duty cycle of the sensor devices. We explore QoS challenges and perspectives for wireless sensor networks, survey the QoS mechanisms and classify the state of the art QoS-aware MAC protocols together with discussing their advantages and disadvantages.
A Relational Compound Collaborative Filtering Recommendation System

This paper presents a relational compound collaborative filtering (RCCF) recommendation system architecture, which integrated the behaviors associated mechanism and recommendation system to calculate the area associated values and the corresponding region of the recommended items rating, resulting in top-N list of the recommended item values. This system can avoid interested items in the MU¡¦s opposite direction and give higher weights value to the points of interest (POIs) in the predicted moving region in order to provide correct and real-time information and promote user satisfaction. We will use simulations to further verify the recommended accuracy and efficiency of the proposed RCCF recommendation system, but also look forward to apply on related mobile services.
SwingStates: adding state machines to Java and the Swing toolkit

This article describes SwingStates, a Java toolkit designed to facilitate the development of graphical user interfaces and bring advanced interaction techniques to the Java platform. SwingStates is based on the use of finite‐state machines specified directly in Java to describe the behavior of interactive systems. State machines can be used to redefine the behavior of existing Swing widgets or, in combination with a new canvas widget that features a rich graphical model, to create brand new widgets. SwingStates also supports arbitrary input devices to implement novel interaction techniques based, for example, on bi‐manual or pressure‐sensitive input. We have used SwingStates in several Master's‐level classes over the past two years and have developed a benchmark approach to evaluate the toolkit in this context. The results demonstrate that SwingStates can be used by non‐expert developers with little training to successfully implement advanced interaction techniques. Copyright © 2007 John Wiley & Sons, Ltd.
Dividend Behavior and Pure Accrual Management of Japanese Banks

This paper provides some evidence of dividend-motivated earnings management using pure accruals defined as accruals and deferrals (A&D) by Japanese banks under the governmental guideline. This guideline had until August 1992 explicitly limited dividend payments by banks to 40 percent or less of their current net income. A bank with the potential to violate the guideline would increasingly manage its A&D to stay within the upper limits. Bias caused by measurement error and research method seems to remain in our tests. Though, under the comparative study, our empirical results consistently support the direct relationship between earnings management and dividend payment policies of Japanese banks: the magnitude of income increasing A&D management becomes significantly smaller when dividend-driven motivation disappears.
STUDIES ON SENSITIVITY OF FACE RECOGNITION PERFORMANCE TO EYE LOCATION ACCURACY

by Haoshu Wang Face recognition systems generally require location of face landmarks (often eyes) as an essential preprocessing step. Eye location estimates can be assessed in absolute terms (e.g., proximity to known eye location) and also in application-specific terms (e.g., performance of a system that employs the location). This thesis assesses an automatic commercial eye-finding system in both absolute and application-specific terms, using two face recognition systems and a database of thousands of images. Experimental results highlight the sensitivity of both systems to accurate eye location estimation as well as quality face lighting. Time-lapse effect is also investigated in this study, and the results suggest that the face recognition system performance degrades with elapsed time.