Miracle-dz/UniFAR-FTUs huggingface dataset:
An approach to crosslayer QoS management in MISO systems

We study the crosslayer problem of joint optimization of radio parameters in a multiuser MISO system with partial channel state information at the transmitter. The central optimization problem within is formulated as a power minimization subject to a set of QoS constraints. Based upon analytical models for all regarded sublayers and the formulation of the resulting channel outage probability of the MISO system, the mode of operation and the MISO transmit processing are optimized jointly in an fast converging iterative scheme. With these results service requests from different users can be met with a significantly lower amount of resources, resulting in a higher system capacity
Tour Route Planning Based on Ant Colony System

In this paper, a random meeting algorithm is introduced into ant colony system. This algorithm can improve the quality of the ant’s tour and can be used to make tour route planning. The improved calculating method of distance can balance the load in scenic spots and has the capability of dynamic route planning. The experimental results show that the proposed algorithm is effective and practical.
Annotation of anaphoric relations in biomedical full-text articles using a domain-relevant scheme

Biomedical literature has been the focus of relevant information extraction projects, however there is no corpus of full scientific articles annotated with anaphoric links for training and evaluation of anaphora resolution systems—which are an important part of information extraction efforts—for this domain. We have created a corpus of biomedical articles that are annotated with anaphoric links between noun phrases referring to the biomedical entities of our interest, using an annotation scheme that aims to capture the domain relations that link the entities.
Self-Esteem, Moral Capital, and Wrongdoing

We present an infinite-horizon model of moral standards where self-esteem and unconscious drives play key roles. In the model, an individual receives random temptations (such as bribe offers) and must decide which to resist. Individual actions depend both on conscious intent and a type reflecting unconscious drives. Temptations yield consumption value, but keeping a good self-image (a high belief of being the type of person that resists) yields self-esteem. We identify conditions for individuals to build an introspective reputation for goodness ("moral capital") and for good actions to lead to a stronger disposition to do good. Bad actions destroy moral capital and lock-in further wrongdoing. Economic shocks that result in higher temptations have persistent effects on wrongdoing that fade only as new generations replace the shocked cohorts. Small parametric differences across societies may lead to large wrongdoing differentials, and societies with the same moral fundamentals may display different wrongdoing rates depending on how much past luck has polarized the distribution of individual beliefs. The model illustrates how optimal deterrence may change under endogenous moral costs and how wrongdoing may be compounded as high temptation activities attract individuals with low moral capital.
What Is an Astronomical Data Model? 1. Origins of the Concept of a Data Model

Using techniques from semantic data modeling and object-oriented data modeling, an astronomical data model is identiied as a kind of conceptual analysis. The general characteristics of such a model are presented, as well as its relationship to computer-based data structures and the FITS standard for transporting data. The eeective use of data models in relation to instrument speciic data, in conjunction with data archives, and in fundamental areas of the data analysis is discussed. The basic concept of a data model originated within the database community as the logical model of an integrated database management system (DBMS). The early DBMSs employed hierarchical, network, and relational data models that were directly implemented by the DBMS software. The entity-relationship model was introduced as a generalization of these previous models and is a more intuitive approach to modeling data relationships. It is widely used as a conceptual tool for relational database design (Batini 1992). Semantic data modeling is an outgrowth of the entity-relationship model in which additional structures are employed to portray more complex data relationships. Object-oriented modeling techniques (Booch 1994) are closely related to semantic data modeling, the major diierence being that the object-oriented techniques add behavior to characterize entities rather than being restricted to data attributes. A semantic data model or an object-oriented data model portrays named objects and relationships between objects. Each object has a set of speciic attributes characterized by data items of a speciic type. The relationships between objects also have names and quantitative aspects, one to one, one to many, many to many, etc. Relationships may have data attributes as well. For example, if Book and Student are objects in a library data model, then Borrows represents a relationship between Book and Student and the data item, due-date, belongs to the Borrows relationship. Most relationships tend to be binary, but they can be n-ary as well. For example, Meets may be a relationship between three objects, a Course, a Room, and a Time (\a course meets in a room at a time"). There are two other important ways in which objects may be related. One object may be a part of another object (an Engine is part of a Car) and one object may be a specialization of another object (a Rectangle is a kind of Polygon).
Generalizing an Approximate Subgraph Matching-based System to Extract Events in Molecular Biology and Cancer Genetics

We participated in the BioNLP 2013 shared tasks, addressing the GENIA (GE) and the Cancer Genetics (CG) event extraction tasks. Our event extraction is based on the system we recently proposed for mining relations and events involving genes or proteins in the biomedical literature using a novel, approximate subgraph matching-based approach. In addition to handling the GE task involving 13 event types uniformly related to molecular biology, we generalized our system to address the CG task targeting a challenging set of 40 event types related to cancer biology with various arguments involving 18 kinds of biological entities. Moreover, we attempted to integrate a distributional similarity model into our system to extend the graph matching scheme for more events. In addition, we evaluated the impact of using paths of all possible lengths among event participants as key contextual dependencies to extract potential events as compared to using only the shortest paths within the framework of our system.
A Model-based Book Dewarping Method Using Text Line Detection

In this paper, we propose a book dewarping model based on the assumption that the book surface is warped as a cylinder. This model extends the model proposed by Cao and makes Cao’s model a special case of our model. This extension removes the constraint of Cao’s model that the camera lens must be strictly parallel to the book surface, which is hard to make in practice, therefore enables a user to take a picture from different point of views conveniently. The main idea of the model is to build up the correspondence between a rectangle region on a flat surface and its curved region on the distorted book image and the dewarping task is to flatten the curved region to its original rectangle shape. The experimental results demonstrate the effectiveness of our proposed book dewarping approach.
Computer-aided detection of colonic polyps with level set-based adaptive convolution in volumetric mucosa to advance CT colonography toward a screening modality

As a promising second reader of computed tomographic colonography (CTC) screening, the computer-aided detection (CAD) of colonic polyps has earned fast growing research interest. In this paper, we present a CAD scheme to automatically detect colonic polyps in CTC images. First, a thick colon wall representation, ie, a volumetric mucosa (VM) with several voxels wide in general, was segmented from CTC images by a partial-volume image segmentation algorithm. Based on the VM, we employed a level set-based adaptive convolution method for calculating the first- and second-order spatial derivatives more accurately to start the geometric analysis. Furthermore, to emphasize the correspondence among different layers in the VM, we introduced a middle-layer enhanced integration along the image gradient direction inside the VM to improve the operation of extracting the geometric information, like the principal curvatures. Initial polyp candidates (IPCs) were then determined by thresholding the geometric measurements. Based on IPCs, several features were extracted for each IPC, and fed into a support vector machine to reduce false positives (FPs). The final detections were displayed in a commercial system to provide second opinions for radiologists. The CAD scheme was applied to 26 patient CTC studies with 32 confirmed polyps by both optical and virtual colonoscopies. Compared to our previous work, all the polyps can be detected successfully with less FPs. At the 100% by polyp sensitivity, the new method yielded 3.5 FPs/dataset.
The pressure altitude computation using artificial neural network for Air Data Computer

Air Data Computer (ADC) calculates the key data necessary for performing a safe flight and accurate air navigation. Altitude information is one of the important parameter computed by the ADC. Accuracy of the altitude corrected using tables or charts showing the actual corrections in altitude information special to aircraft type. This correction is important in any high performance and jet aircraft. In order to correct these imperfections, certain calibrations need to be made within the ADC. In this paper, artificial neural networks (ANNs) are used to calculate altitude information more easily and accurately using data obtained from tables formed under standard atmosphere conditions.
Nonweight‐bearing anterior knee laxity is related to anterior tibial translation during transition from nonweight bearing to weight bearing

We examined the relationship between anterior knee laxity (AKL), evaluated while the knee was nonweight bearing, and anterior translation of the tibia relative to the femur (ATT), evaluated when the knee transitioned from nonweight‐bearing to weight‐bearing conditions in response to an applied compressive load at the foot. Twenty subjects with normal knees (10 M, 10 F; 25.2 ± 4.1 years, 169.8 ± 11.5 cm, 71.6 ± 16.9 kg) underwent measurements of AKL and ATT of the right knee on 2 days. AKL was measured at 133N with the KT‐2000™. ATT was measured with the Vermont Knee Laxity Device and electromagnetic position sensors attached to the patella and the anteromedial aspect of the proximal tibia. Three trials for each measure were averaged and analyzed. Measurement consistency was high for both AKL (ICC = 0.97; SEM = 0.44 mm) and ATT (ICC = 0.88; SEM = 0.84 mm). Linear regression revealed that AKL predicted 35.5% of the variance in ATT (p = 0.006), with a prediction equation of YATT = 3.20 + 0.543(XAKL). Our findings suggest that increased AKL is associated with increased ATT as the knee transitions from nonweight‐bearing to weight‐bearing conditions. The potential for increased knee joint laxity to disrupt normal knee biomechanics during activities such as landing from a jump, or the foot strike phase of gait deserves further study. © 2006 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 24:516–523, 2006
Functional unparsing

A string-formatting function such as printf in C seemingly requires dependent types, because its control string determines the rest of its arguments. Examples: formula here We show how changing the representation of the control string makes it possible to program printf in ML (which does not allow dependent types). The result is well typed and perceptibly more efficient than the corresponding library functions in Standard ML of New Jersey and in Caml.
Continuous Discovery of Semantic Knowledge ∗

We report research toward a never-ending language learning system, focusing on a first implementation which learns to classify occurrences of noun phrases according to lexical categories such as “city” and “university. ” Our experiments suggest that the accuracy of classifiers produced by semi-supervised learning can be improved by coupling the learning of multiple classes based on background knowledge about relationships between the classes (e.g., ”university” is mutually exclusive of ”company”, and is a subset of ”organization”).
The role of retributive justice and the use of international criminal tribunals in post-conflict reconciliation

Four experiments examined people's responses to intergroup violence either committed or suffered by their own group. Experiment 1 demonstrated that Serbs who strongly glorified Serbia were more supportive of future violence against, and less willing to reconcile with, Bosniaks after reading about Serbian victimization by Bosniaks rather than Serbian transgressions against Bosniaks. Replicating these effects with Americans in the context of American–Iranian tensions, Experiment 2 further showed that demands for retributive justice explained why high glorifiers showed asymmetrical reactions to ingroup victimization vs. perpetration. Again in the Serb and the American context, respectively, Experiments 3 and 4 demonstrated that post-conflict international criminal tribunals can help satisfy victim group members' desire for retributive justice, and thereby reduce their support for future violence and increase their willingness to reconcile with the perpetrator group. The role of retributive justice and the use of international criminal justice in intergroup conflict (reduction) are discussed.
COUPLED SPIN AND SHAPE EVOLUTION OF SMALL RUBBLE-PILE ASTEROIDS: SELF-LIMITATION OF THE YORP EFFECT

We present the first self-consistent simulations of the coupled spin-shape evolution of small gravitational aggregates under the influence of the YORP effect. Because of YORP’s sensitivity to surface topography, even small centrifugally driven reconfigurations of aggregates can alter the YORP torque dramatically, resulting in spin evolution that can differ qualitatively from the rigid-body prediction. One-third of our simulations follow a simple evolution described as a modified YORP cycle. Two-thirds exhibit one or more of three distinct behaviors—stochastic YORP, self-governed YORP, and stagnating YORP—which together result in YORP self-limitation. Self-limitation confines rotation rates of evolving aggregates to far narrower ranges than those expected in the classical YORP cycle, greatly prolonging the times over which objects can preserve their sense of rotation. Simulated objects are initially randomly packed, disordered aggregates of identical spheres in rotating equilibrium, with low internal angles of friction. Their shape evolution is characterized by rearrangement of the entire body, including the deep interior. They do not evolve to axisymmetric top shapes with equatorial ridges. Mass loss occurs in one-third of the simulations, typically in small amounts from the ends of a prolate-triaxial body. We conjecture that YORP self-limitation may inhibit formation of top-shapes, binaries, or both, by restricting the amount of angular momentum that can be imparted to a deformable body. Stochastic YORP, in particular, will affect the evolution of collisional families whose orbits drift apart under the influence of Yarkovsky forces, in observable ways.
Spectroscopic binaries among Hipparcos M giants - II. Binary frequency

Context. This paper is the second in a series devoted to studying the properties of binaries with M giant primaries. Aims. The binary frequency of field M giants is derived and compared with the binary fraction of K giants. Methods. Diagrams of the CORAVEL spectroscopic parameterSb (measuring the average line width) vs. radial-velocity sta ndard deviation for our samples were used to define appropriate bin arity criteria. These then served to extract the binarity fr action among the M giants. Comparison is made to earlier data on K giant binarity frequency. The Sb parameter is discussed in relation to global stellar parameters, and the Sb vs. stellar radius relation is used to identify fast rotator s. Results. We find that the spectroscopic binary detection rate among fie ld M giants, in a sample with few velocity measurements (� 2), unbiased toward earlier known binaries, is 6.3%. This is less than half of the analogous rate for field K giants. This dif ference originates in the greater difficulty of finding binaries amon g M giants because of their smaller orbital velocity amplitudes and larger intrinsic jitter and in the different distributions of K and M giants in the eccentricity ‐ period diagram. A higher detection rate was obtained in a smaller M giant sample with more radial velocity measurements per object: 11.1% confirmed plus 2.7% poss ible binaries. The CORAVEL spectroscopic parameterSb was found to correlate better with the stellar radius than wi th either luminosity or effective temperature separately. Two outliers of the Sb vs. stellar radius relation, HD 190658 and HD 219654, have been recognised as fast rotators. The rotation is companion-induced, as bot h objects turn out to be spectroscopic binaries.
Design of Radial Basis Function Neural Networks for Software Effort Estimation

In spite of the several software effort estimation models developed over the last 30 years, providing accurate estimates of the software project under development is still unachievable goal. Therefore, many researchers are working on the development of new models and the improvement of the existing ones using artificial intelligence techniques such as: case-based reasoning, decision trees, genetic algorithms and neural networks. This paper is devoted to the design of Radial Basis Function Networks for software cost estimation. It shows the impact of the RBFN network structure, especially the number of neurons in the hidden layer and the widths of the basis function, on the accuracy of the produced estimates measured by means of MMRE and Pred indicators. The empirical study uses two different software project datasets namely, artificial COCOMO’81 and Tukutuku datasets.
A low-power FPGA implementation of eye tracking

The use of eye-tracking in energy-constrained applications requires systems capable of tracking human eyes without consuming large amount of power. In this paper we present a low-power implementation of real-time non-intrusive eye-tracking by single camera. Unlike existing hardware designs, our eye-tracker does not restrict the user nor requires computationally expensive tools for maintaining high quality tracking. The system provides over 88% eye detection accuracy at 8f/s image processing rate. We describe the hardware and present the results of its experimental evaluation.
Investigating Teachers' Life Satisfaction.

The aim of this study is to determine the life satisfaction perceptions of teachers working at public primary schools according to some variables. In this study, descriptive survey model was used. A random sample of 200 teachers from 25 public primary schools in Diyarbakır/Turkey during 2013-2014 academic year were selected to represent the overall population. In this research, “The Satisfaction with Life Scale” developed by Diener, Emmons, Larsen and Griffin (1985) and translated into Turkish by Dağlı and Baysal (2016) was used. The results show that general means of the teachers’ perception of life satisfaction is at the “moderate” level. The highest mean score of perception is “I am satisfied with my life” (M=2,95; Agree moderately)and the lowest one is “If I could live my life over, I would change almost nothing”(M=2,24; Slightly agree). The results also show that there exists a significant difference among teachers’ perceptions according to their gender, marital status and monthly income.
RFID Path Authentication, Revisited

In an RFID-enabled supply chain, where items are outfitted with RFID tags, path authentication based on tag enables the destination checkpoints to validate the route that a tag has already accessed. In this work, we propose a novel, efficient, privacy-preserving path authentication system for RFID-enabled supply chains. Compared to existing Elliptic curve Elgamal Re-encryption (ECElgamal) based solution, our Homomorphic Message authentication Code on arithmetic circuit (HomMAC) based solution offers less memory storage (with limited scalability) and no computational requirement on the reader. However, unlike previous schemes, we allow computational ability inside the tag that consents a new privacy direction to path privacy proposed by Cai et al. in ACNS012. In addition, we customize a polynomial-based authentication scheme (to thwart potential tag impersonation and Denial of Service (DoS) attacks), so that it fits our new path authentication protocol.
Chronic dietary intake of α-linolenic acid does not replicate the effects of DHA on passive properties of entorhinal cortex neurons

n-3 PUFA are receiving growing attention for their therapeutic potential in central nervous system (CNS) disorders. We have recently shown that long-term treatment with DHA alters the physiology of entorhinal cortex (EC) neurons. In the present study, we investigated by patch-clamp the effect of another major dietary n-3 PUFA, α-linolenic acid (LNA), on the intrinsic properties of EC neurons. Mice were chronically exposed to isoenergetic diets deficient in n-3 PUFA or enriched in either DHA or LNA on an equimolar basis. GC analyses revealed an increase in DHA (34 %) and a decrease in arachidonic acid (AA, − 23 %) in brain fatty acid concentrations after consumption of the DHA-enriched diet. Dietary intake of LNA similarly affected brain fatty acid profiles, but at a lower magnitude (DHA: 23 %, AA: − 13 %). Compared to the n-3 PUFA-deficient diet, consumption of DHA, but not LNA, induced membrane hyperpolarisation ( − 60 to − 70 mV), increased cellular capacitance (32 %) and spontaneous excitatory postsynaptic current frequency (50 %). We propose that the inefficiency of LNA to modulate cellular capacitance was related to its inability to increase the brain DHA:AA ratio over the threshold necessary to up-regulate syntaxin-3 (46 %) and translocate drebrin (40 % membrane:cytosol ratio). In summary, our present study shows that the increase in brain DHA content following chronic administration of LNA was not sufficient to alter the passive and synaptic properties of EC neurons, compared to direct dietary intake of DHA. These diverging results have important implications for the therapeutic use of n-3 PUFA in CNS disease, favouring the use of preformed DHA.
Modeling and Automatic Feedback Control of Tremor: Adaptive Estimation of Deep Brain Stimulation

This paper discusses modeling and automatic feedback control of (postural and rest) tremor for adaptive-control-methodology-based estimation of deep brain stimulation (DBS) parameters. The simplest linear oscillator-based tremor model, between stimulation amplitude and tremor, is investigated by utilizing input-output knowledge. Further, a nonlinear generalization of the oscillator-based tremor model, useful for derivation of a control strategy involving incorporation of parametric-bound knowledge, is provided. Using the Lyapunov method, a robust adaptive output feedback control law, based on measurement of the tremor signal from the fingers of a patient, is formulated to estimate the stimulation amplitude required to control the tremor. By means of the proposed control strategy, an algorithm is developed for estimation of DBS parameters such as amplitude, frequency and pulse width, which provides a framework for development of an automatic clinical device for control of motor symptoms. The DBS parameter estimation results for the proposed control scheme are verified through numerical simulations.