Miracle-dz/UniFAR-FTUs huggingface dataset:
Lexical semantics and automatic hypertext construction

We describe recent work in the area of lexical semantics as it applies to the task of automatic hypertext construction. A discussion of the basic principles of lexical cohesion is used to inform the discussion of a method for extracting sets of lexically related words from a text -a process called lexical chaining. We discuss how various researchers have used lexical chains to perform automatic hypertext construction and related tasks.
Effect of Mechanical Deformation on the Structure of Regenerated Bombyx mori Silk Fibroin Films as Revealed Using Raman and Infrared Spectroscopy

To better understand the effect of mechanical stress during the spinning of silk, the protein orientation and conformation of Bombyx mori regenerated silk fibroin (RSF) films have been studied as a function of deformation in a static mode or in real time by tensile-Raman experiments and polarization modulation infrared linear dichroism (PM-IRLD), respectively. The data show that either for step-by-step or continuous stretching, elongation induces the progressive formation of β-sheets that align along the drawing axis, in particular above a draw ratio of 2. The formation of β-sheets begins before their alignment during a continuous drawing. Unordered chains were, however, never found to be oriented, which explains the very low level of orientation of the amorphous phase of the natural fiber. Stress-perturbed unordered chains readily convert into β-sheets, the strain-induced transformation following a two-state process. The final level of orientation and β-sheet content are lower than those found in the native fiber, indicating that various parameters have to be optimized in order to implement a spinning process as efficient as the natural one. Finally, during the stress relaxation period in a step-by-step drawing, there is essentially no change of the content and orientation of the β-sheets, suggesting that only unordered structures tend to reorganize.
A new approach for nonlinear distortion correction in endoscopic images based on least squares estimation

Images captured with a typical endoscope show spatial distortion, which necessitates distortion correction for subsequent analysis. Here, a new methodology based on least squares estimation is proposed to correct the nonlinear distortion in the endoscopic images. A mathematical model based on polynomial mapping is used to map the images from distorted image space onto the corrected image space. The model parameters include the polynomial coefficients, distortion center, and corrected center. The proposed method utilizes a line search approach of global convergence for the iterative procedure to obtain the optimum expansion coefficients. A new technique to find the distortion center of the image based on curvature criterion is presented. A dual-step approach comprising token matching and integrated neighborhood search is also proposed for accurate extraction of the centers of the dots contained in a rectangular grid, used for the model parameter estimation. The model parameters were verified with different grid patterns. The distortion correction model is applied to several gastrointestinal images and the results are presented. The proposed technique provides high-speed response and forms a key step toward online camera calibration, which is required for accurate quantitative analysis of the images.
The Prevalence and Associated Risk Factors of Impaired Glucose Regulation in Chinese Adults: A Population-Based Cross-Sectional Study

The goal of this study was to determine the prevalence and associated risk factors of impaired glucose regulation (IGR) in the population of Tongzhou, China, and to provide scientific basis for preventive interventions. In the study, the overall age-standardized prevalence of IGR (16.0%) in Tongzhou residents was higher than that in the national population (15.0%). There was no significant geographic difference in prevalence of IGR between urban and rural males. Older age, elevated blood pressure, high serum lipids, overweight, and central obesity were significantly associated with increased risk of IGR.
0368-3248-01-Algorithms in Data Mining Fall 2013 Lecture 4 : Frequency Moment Estimation in Streams

Estimating f0 Here we describe an algorithm for estimating f0 which merges (and hopefully simplifies) ideas from [1] and [2]. First, assume a hash function h : a→ [0, 1] uniformly. Let us define a random variable X = minih(ai). Intuitively, X should be roughly 1/m and therefore 1/X should be a fair estimate of m. This is almost true. In what comes next we make this into an exact statement. Let us first compute the expectation of X. The distribution function fX of the random variable X is fX(x) = m(1 − x)m−1. This is because, we have m different choices for the minimal element and for every value it takes, x, all the rest m− 1 values need to be higher than it (w.p (1− x)m−1). Therefore:
Wellness, Professional Quality of Life, and Career‐Sustaining Behaviors: What Keeps Us Well?

A sample of 506 professional counselors who were members of the American Counseling Association completed measures of professional quality of life, career-sustaining behaviors (CSBs), and wellness. Significant differences were found both within the sample based on caseload characteristics and between the participants and available norm groups. Counselors with high wellness scores engaged in more CSBs and reported higher positive professional quality of life factors (compassion satisfaction). Implications for counselors, counselor training, and research are discussed.
Soil Classification Using GATree

This paper details the application of a genetic programming framework for classification of decision tree of Soil data to classify soil texture. The database contains measurements of soil profile data. We have applied GATree for generating classification decision tree. GATree is a decision tree builder that is based on Genetic Algorithms (GAs). The idea behind it is rather simple but powerful. Instead of using statistic metrics that are biased towards specific trees we use a more flexible, global metric of tree quality that try to optimize accuracy and size. GATree offers some unique features not to be found in any other tree inducers while at the same time it can produce better results for many difficult problems. Experimental results are presented which illustrate the performance of generating best decision tree for classifying soil texture for soil data set.
[Study of the brachial plexus by magnetic resonance].

OBJECTIVE Magnetic resonance (MR) is considered to be the best method available at present for diagnosis of lesions of the brachial plexus. In this study we analyze our experience in patients with pathology of the brachial plexus. MATERIAL AND METHODS Between August 1991 and March 1997 we did magnetic resonance studies on 42 patients with pathology of the brachial plexus. Our protocols included T1, T2 axial spin-echo sequences with 5 mm cuts and coronal echo of a T1-3D gradient with 2 mm cuts, together with a T1 coronal spin-echo, with cuts 2 mm in width. RESULTS The causes found were: traumatic 16 patients (38%), pathology of bone 2 (4.7%), 19 tumours (45.2%) with breast cancer being commonest, and no obvious aetiology in 5 (11.9%). CONCLUSIONS MR is the technique of choice for study of pathology of the brachial plexus of traumatic or tumour origin. In some patients, 11.9% in our series, no aetiology could be detected using MR.
Behavioural specifications '

For historic reasons, programming (and its theory, as well as methodology) evolved from the computing paradigm. Many computer applications in common use do not fit this paradigm well. Neither an operating system, nor a word processor compute anything, even if their operation involves many computations. Here we follow another paradigm, viz. that of system behaviour, in which finite actions are undertaken and accepted in specific circumstances. In this approach there is no notion of action sequencing, therefore we avoid all problems of synchronization and global time. Instead, we recognize that action executions are not instantaneous and—therefore—the circumstances under which a particular action was deemed desirable may have changed during an execution to ones under which its outcome is no more needed. (Establishing a telephone connection in a modern network is an action which certainly takes some time during which the caller may decide to hang up; should this be the case, the laboriously established connection may safely be dissolved.) The chosen paradigm accommodates an arbitrary degree of parallelism in the sense that an unspecified number of actions may be executed concurrently and—as far as the execution processes are concerned—independently. All actions that can be initiated in a given state, are initiated, regardless of any possible future conflict; conflicts are resolved in the accepting state. (When a client asks the bank to cash a cheque, the teller starts counting the money and checks the balance only when handing the cash out.) Observable system behaviour obtains from successful executions of actions. For each system the collection of actions is determined by its specification which associates each action with two guards. A preguard characterizes the set of system states in which an action may be undertaken, a postguard characterizes the set of states in which the outcome of the executed action is acceptable i.e. may be reflected in the system state. Each action is assumed finite if undertaken in a state satisfying the corresponding preguard.
CLL : a concurrent language built from logical principles

We present CLL, a concurrent programming language that symmetrically integrates functional and concurrent logic programming. First, a core functional language is obtained from a proof-term assignment to a variant of intuitionistic linear logic, called FOMLL, via the Curry-Howard isomorphism. Next, we introduce a Chemical Abstract Machine (CHAM) whose molecules are typed terms of this functional language. Rewrite rules for this CHAM are derived by augmenting proof-search rules for FOMLL with proof-terms. We show that this CHAM is a powerful concurrent language and that the linear connectives ®, 3, ©, —o and Sz correspond to process-calculi connectives for parallel composition, name restriction, internal choice, input prefixing and external choice respectively. We also demonstrate that communication and synchronization between CHAM terms can be performed through proof-search on the types of terms. Finally, we embed this CHAM as a construct in our functional language to allow interleaving functional and concurrent computation in CLL.
Hybrid index structures for location-based web search

There is more and more commercial and research interest in location-based web search, i.e. finding web content whose topic is related to a particular place or region. In this type of search, location information should be indexed as well as text information. However, the index of conventional text search engine is set-oriented, while location information is two-dimensional and in Euclidean space. This brings new research problems on how to efficiently represent the location attributes of web pages and how to combine two types of indexes. In this paper, we propose to use a hybrid index structure, which integrates inverted files and R*-trees, to handle both textual and location aware queries. Three different combining schemes are studied: (1) inverted file and R*-tree double index, (2) first inverted file then R*-tree, (3) first R*-tree then inverted file. To validate the performance of proposed index structures, we design and implement a complete location-based web search engine which mainly consists of four parts: (1) an extractor which detects geographical scopes of web pages and represents geographical scopes as multiple MBRs based on geographical coordinates; (2) an indexer which builds hybrid index structures to integrate text and location information; (3) a ranker which ranks results by geographical relevance as well as non-geographical relevance; (4) an interface which is friendly for users to input location-based search queries and to obtain geographical and textual relevant results. Experiments on large real-world web dataset show that both the second and the third structures are superior in query time and the second is slightly better than the third. Additionally, indexes based on R*-trees are proven to be more efficient than indexes based on grid structures.
Doubly Iterative Equalization of Continuous-Phase Modulation

In this paper, a doubly iterative receiver is proposed for joint turbo equalization, demodulation, and decoding of coded binary continuous-phase modulation (CPM) in multipath fading channels. The proposed receiver consists of three soft-input soft-output (SISO) blocks: a front-end soft-information-aided minimum mean square error (MMSE) equalizer followed by a CPM demodulator and a back-end channel decoder. The MMSE equalizer, combined with an a priori soft-interference canceler (SIC) and an a posteriori probability mapper, forms a SISO processor suitable for iterative processing that considers discrete-time CPM symbols which belong to a finite alphabet. The SISO CPM demodulator and the SISO channel decoder are both implemented by the a posteriori probability algorithm. The proposed doubly iterative receiver has a central demodulator coupled with both the front-end equalizer and the back-end channel decoder. A few back-end demodulation/decoding iterations are performed for each equalization iteration so as to improve the a priori information for the equalizer. As presented in the extrinsic information transfer (EXIT) chart analysis and simulation results for different multipath fading channels, this provides not only faster convergence to low bit error rates, but also lower computational complexity.
Detailed-routability-driven analytical placement for mixed-size designs with technology and region constraints

A placer without considering modern technology and region constraints could generate solutions with irresolvable detailed-routing violations or even illegal solutions. This paper presents a high-quality placement algorithm to satisfy technology and region constraints and optimize detailed-routing routability with three major techniques: (1) a clustering algorithm followed by two-round quadratic placement to obtain an initial placement satisfying region constraints, (2) an analytical placement algorithm with new wirelength and density models to consider region constraints, and (3) a legalization algorithm that preserves the solution quality of global placement while satisfying technology/region constraints. Compared with the winning teams of the ISPD 2015 Blockage-Aware Detailed Routing-Driven Placement Contest, our placer achieves the best overall score and detailed-routing results.
Automated brain state identification using graph embedding

The functional activation pattern within the human brain is known to change at varying time-scales. This existence of and dynamics between inherently different brain functional states are found to be related to human learning, behavior, and development, and, are therefore of high importance. Yet, tools to automatically identify such cognitive states are limited. In this study, we consider high-dimensional functional connectome data constructed from BOLD fMRI over short time-intervals as a graph, each time-point as a node, and the similarity between two time-points as the edge between those two nodes. We apply graph embedding techniques to automatically extract clusters of time-points, which represent canonical brain states. Application of graph embedding technique to BOLD fMRI time-series of a population comprised of autistic and neurotypical subjects demonstrates that two-layer embedding by preserving the higherorder similarity between different time-points is crucial toward successful identification of low-dimensional brain functional states. Finally, the present study reveals inherent existence of two brain meta-states within human brain.
Parallel convolutional-linear neural network for motor imagery classification

Deep learning, recently, has been successfully applied to image classification, object recognition and speech recognition. However, the benefits of deep learning and accompanying architectures have been largely unknown for BCI applications. In motor imagery-based BCI, an energy-based feature, typically after spatial filtering, is commonly used for classification. Although this feature corresponds to the estimate of event-related synchronization/desynchronization in the brain, it neglects energy dynamics which may contain valuable discriminative information. Because traditional classiication methods, such as SVM, cannot handle this dynamical property, we proposed an architecture that inputs a dynamic energy representation of EEG data and utilizes convolutional neural networks for classification. By combining this network with a static energy network, we saw a significant increase in performance. We evaluated the proposed method and compared with SVM on a multi-class motor imagery dataset (BCI competition dataset IV-2a). Our method outperforms SVM with static energy features significantly (p <; 0.01).
An Adaptive Energy Saving and Reliable Routing Protocol for Limited Power Sensor Networks

In wireless sensor networks, the value of transmission range has a direct impact on energy utilization. A sensor node makes use of the maximum transmission power and transmission range to send packets in a network which results in wastage of energy, since maximum transmission range is mostly longer than the distance between node and its extreme neighbor due to node distribution. In this paper, we present an adaptive energy saving and reliable routing protocol (AESRR) for wireless sensor networks. In AESRR, sensor node reduces its maximum transmission range in order to reach the extreme neighbor for saving energy before sending the first packet. The route discovery process of AESRR is on-demand. In the route discovery process, a combined link weight is determined based on the parameters transmission success ratio and node’s residual energy. The best route is selected based on this link weight value. The sensor node must readjust the transmission range when remaining energy reaches bellow a threshold link weight value. Our proposed protocol saves energy, and prolongs the lifetime of node while enhancing the reliability. By simulation results, we demonstrate that AESRR has better delivery ratio and less energy consumption and delay.
Decision Tree of Occupational Lung Cancer Using Classification and Regression Analysis

Objectives Determining the work-relatedness of lung cancer developed through occupational exposures is very difficult. Aims of the present study are to develop a decision tree of occupational lung cancer. Methods 153 cases of lung cancer surveyed by the Occupational Safety and Health Research Institute (OSHRI) from 1992-2007 were included. The target variable was whether the case was approved as work-related lung cancer, and independent variables were age, sex, pack-years of smoking, histological type, type of industry, latency, working period and exposure material in the workplace. The Classification and Regression Test (CART) model was used in searching for predictors of occupational lung cancer. Results In the CART model, the best predictor was exposure to known lung carcinogens. The second best predictor was 8.6 years or higher latency and the third best predictor was smoking history of less than 11.25 pack-years. The CART model must be used sparingly in deciding the work-relatedness of lung cancer because it is not absolute. Conclusion We found that exposure to lung carcinogens, latency and smoking history were predictive factors of approval for occupational lung cancer. Further studies for work-relatedness of occupational disease are needed.
Named entity extraction from Japanese broadcast news

This paper describes a method for named entity extraction from Japanese broadcast news. Our proposed named entity tagger gives entity categories for every character in order to deal with unknown words and entities correctly. This character-based tag-ger has models designed by maximum entropy modeling. We discuss the efﬁciency of the proposed tagger by comparison with a conventional word-based tagger. The results indicate that the capability of the taggers depends on the entity categories. Therefore, the features derived from both character and word contexts are required to obtain high performance of named entity extraction.
Neuropilin-1 Mediates Divergent R-Smad Signaling and the Myofibroblast Phenotype*

The transforming growth factor-beta (TGF-β) superfamily is one of the most diversified cell signaling pathways and regulates many physiological and pathological processes. Recently, neuropilin-1 (NRP-1) was reported to bind and activate the latent form of TGF-β1 (LAP-TGF-β1). We investigated the role of NRP-1 on Smad signaling in stromal fibroblasts upon TGF-β stimulation. Elimination of NRP-1 in stromal fibroblast cell lines increases Smad1/5 phosphorylation and downstream responses as evidenced by up-regulation of inhibitor of differentiation (Id-1). Conversely, NRP-1 loss decreases Smad2/3 phosphorylation and its responses as shown by down-regulation of α-smooth muscle actin (α-SMA) and also cells exhibit more quiescent phenotypes and growth arrest. Moreover, we also observed that NRP-1 expression is increased during the culture activation of hepatic stellate cells (HSCs), a liver resident fibroblast. Taken together, our data suggest that NRP-1 functions as a key determinant of the diverse responses downstream of TGF-β1 that are mediated by distinct Smad proteins and promotes myofibroblast phenotype.
Magneto-driven Shock Waves in Core-Collapse Supernovae

We perform a series of two-dimensional magnetohydrodynamic simulations of the rotational core collapse of a magnetized massive star. We employ a realistic equation of state and take into account the neutrino cooling by the so-called leakage scheme. In this study we systematically investigate how the strong magnetic field and the rapid rotation affect the propagation of the shock waves. Our results show that in the case of the strong initial poloidal magnetic field, the toroidal magnetic field amplified by the differential rotation becomes strong enough to generate a tightly collimated shock wave along the rotational axis. On the other hand, in the case of the weak initial magnetic field, although the differential rotation amplifies the toroidal magnetic field over the long rotational period, the launched shock wave is weak and the shape of it becomes wider. The former case is expected to be accompanied by the formation of the so-called magnetar. Our models with rapid rotation and strong magnetic field can create a nozzle formed by the collimated shock wave. This might be the analogous situation to the collapsar that is plausible as the central engine of the gamma-ray bursts.
Design and experimental study of an ultrasonic sensor system for lateral collision avoidance at low speeds

In this study, we-design and implement an ultrasonic sensor system for lateral collision avoidance of vehicles at low speeds. The developed sensor system is useful for detecting vehicles, motorcycles, bicycles and pedestrians that pass by the lateral side of a vehicle. The system can be adopted to enhance the rear-view mirrors of present vehicles, which have blind spots on the lateral sides. Ultrasonic sensors, which have been widely used on cars for rear object detection during parking, are developed for lateral object detection at low speeds. Detailed experimental studies are presented in this paper. Experimental results show that the proposed system can detect a vehicle at speeds up to 40 km/hr with a maximum range of 6 meters. Moreover, the influence of wind on the measurement is also investigated. The developed sensor system gives satisfactory results for a wind speed up to 35 km/hr.