Miracle-dz/UniFAR-FTUs huggingface dataset:
Godunov method for computerized lung cancer cryosurgery planning with efficient freezing time

Cryosurgery is a technique of cancer treatment by providing extremely cold temperature to destroy abnormal cells. In this article, cryosurgery simulation of lung cancer is presented. Mathematical formulation of cryosurgery procedure is governed by heat conduction equations both in the solid and liquid phase which then reformulated into enthalpy form. Godunov method is used to obtain the numerical solutions of enthalpy equation. Numerical results for one-dimensional case are validated against the exact solution. In two-dimensional simulation, efficient freezing time, which is a time when all cancer areas are frozen, is obtained by calculating the area of ice ball formation which covers the cancer cells area in each time. It is found that for lung cancer configuration being studied, the efficient cryosurgery time is 522.24 seconds. Temperature distribution and interface position during cryosurgery and heating process are then simulated based on efficient freezing time. The simulation results provide an important information for computerized lung cancer cryosurgery planning.
Robustness and Reproducibility of Flow Territories Defined by Planning-Free Vessel-Encoded Pseudocontinuous Arterial Spin-Labeling

SUMMARY: Flow-territory mapping by MR imaging ASL noninvasively provides a unique insight into the distribution of cerebral perfusion. The introduction of planning-free vessel-encoded pCASL made flow-territory mapping feasible for clinical use, though lack of individual planning could impede reproducibility of this technique. We assessed the reproducibility of planning-free vessel-encoded pCASL in patients and controls. Results indicated that planning-free vessel-encoded pCASL is a reproducible method that could assist in clinical decision-making.
Estimation of the Rate–Distortion Function

Motivated by questions in lossy data compression and by theoretical considerations, the problem of estimating the rate-distortion function of an unknown (not necessarily discrete-valued) source from empirical data is examined. The focus is the behavior of the so-called ldquoplug-inrdquo estimator, which is simply the rate-distortion function of the empirical distribution of the observed data. Sufficient conditions are given for its consistency, and examples are provided demonstrating that in certain cases it fails to converge to the true rate-distortion function. The analysis of its performance is complicated by the fact that the rate-distortion function is not continuous in the source distribution; the underlying mathematical problem is closely related to the classical problem of establishing the consistency of maximum-likelihood estimators (MLEs). General consistency results are given for the plug-in estimator applied to a broad class of sources, including all stationary and ergodic ones. A more general class of estimation problems is also considered, arising in the context of lossy data compression when the allowed class of coding distributions is restricted; analogous results are developed for the plug-in estimator in that case. Finally, consistency theorems are formulated for modified (e.g., penalized) versions of the plug-in, and for estimating the optimal reproduction distribution.
Bayesian fusion of confidence measures for speech recognition

The application of Bayesian fusion of confidence measures to speech recognition is proposed. Feature level, decision level, and hybrid fusion are considered under the Bayesian framework. The use of speaker-adapted feature-level Bayesian fusion reduced the error rate by 19.4% as compared to the conventional single feature-based confidence scoring in an isolated word out-of-vocabulary rejection test. The decision-level Bayesian fusion also showed better performance than the majority rule. Finally, hybrid Bayesian fusion, which can combine both confidence measure features and local decisions, achieved the best performance.
Concurrent Error Detection for a Network of Combinational Logic Blocks Implemented with Memory Embedded in FPGAs

We propose a concurrent error detection (CED) scheme for a network of combinational logic blocks implemented with memory embedded in FPGAs. The proposed scheme is proven to detect - without latency - any permanent or transient fault associated with a single input or output of any component of the network. The experimental results show that the overhead for the presented CED technique is low (23.9% for the examined network), especially when compared with solutions intended for gate-based designs (55% or 81%, depending on the technique, for the same network).
Heterogeneous information saliency features' fusion approach for machine's environment sounds based awareness

Human beings are more intelligent in dealing with sound which occurred in everyday life than robots or other kind of unmanned ground vehicles because of the instinct of "sense" or "awareness", which is an ability to distinguish the most salient sound, object or events in the surrounding environment. Inspired by the biological acoustic awareness of human hearing system and the visual saliency talent of human vision, a heterogeneous information saliency feature fusion (HISFF) approach which simulates human awareness of environment sound for machine's awareness is proposed in this paper. The sound signal is visualized by using the Short-Time Fourier Transform (STFT) algorithm in order to convert the acoustic saliency into visual saliency, and the Mel-Frequency Cepstrum Coefficient (MFCC) is used to represent the human acoustic awareness. The proposed HISFF approach is tested by using the environment sound data which collected from the real world of both indoor and outdoor environment. The results show that this approach is able to extract the saliency signal from both long-term and short-term sound signal successfully and clearly, and conducts to very distinguishable features for machine's environment sounds based awareness.
LearnLib: a library for automata learning and experimentation

In this paper we present the LearnLib, a library for automata learning and experimentation. Its modular structure allows users to configure their tailored learning scenarios, which exploit specific properties of the envisioned applications. As has been shown earlier, exploiting application-specific structural features enables optimizations that may lead to performance gains of several orders of magnitude, a necessary precondition to make automata learning applicable to realistic scenarios.
Computers and 2D geometric learning of Turkish fourth and fifth graders

This research investigated the possible impacts of computers on Turkish fourth-grade students’ geometry scores and further geometric learning. The study used a pretest–intervention–posttest experimental design. Results showed that students who did not have computers at home initially had lower geometry scores. However, these differences were minimised with an appropriate intervention containing computer-based Tangram puzzles. This suggests that at schools, it seems more effective to integrate mathematical content and technology in a manner that enables students to do playful mathematical discoveries.
A VRP-Based Route Planning for a Mobile Robot Group

In this study, a vehicle routing problem-based approach is presented to construct non-intersecting routes for the members of a mobile robot team. It is assumed that each robot starts from a central location such as the charging point, completes its route and returns to the starting location. The proposed method consists of three algorithms: a sweep algorithm determines the position of each node in clockwise (or counter clockwise) manner with respect to the starting location; savings algorithm calculates the saving obtained by adding a node to the route of a robot; Dijkstra’s shortest path algorithm is used to calculate the shortest distance from any node to another one when the network is sparse. Simulations are performed using some benchmark VRP problems and results are compared with the optimal solution of the same problems. It is shown that our approach constructs routes significantly fast with near optimal energy consumption.
Suzanne Shontz Statement of Research Interests

My research interests are in scientific computing, specifically, nonlinear equations, nonlinear optimization, and unstructured meshing. There are many application domains in which algorithms in these areas play an important role such as electronic structures calculations, cardiology, and nonlinear elasticity. The main goal of my resesarch is, then, to identify and understand the mathematical problems in these application areas and develop algorithms and numerical methods to solve these problems efficiently and effectively. My approach to research balances algorithm development, theory, and applications, as all three aspects are necessary in scientific computing to develop efficient, effective algorithms with solid theoretical understanding. Interdisciplinary collaboration is central to my research. As a postdoc at the University of Minnesota, working on the Pseudopotential Algorithms for Real Space Energy Calculations (PARSEC) project, I have been affiliated with computer scientists here as well as with materials scientists at the University of Texas at Austin. During my graduate studies at Cornell University, I worked with mathematicians, computer scientists, and cardiologists at Cornell, in addition to biomedical engineers at the National Institute of Health and Johns Hopkins University. These interdisciplinary collaborations have taught me the importance of team work, communication, and planning of a research project. They have also taught me the importance of learning how to communicate with researchers from another discipline and just how valuable collaborations with researchers in varied disciplines are. The main theme of my research in nonlinear equations and nonlinear optimization has been the reduction of the time to solution by reducing the number of function evaluations required. This is important for the application problems I have worked on, as function evaluations are very expensive; for some of these problems, several hours are required to perform a single function evaluation. In unstructured meshing, my work has focused on the generation of high-quality triangulations (meshes). Unstructured meshes are generated as input to Finite Element Methods and other partial differential equation solvers for problems with complex, irregular geometries. These discretization techniques require good quality meshes in order to develop accurate solutions in as efficient a manner as possible.
A recommendation model based on collaborative filtering and factorization machines for social networks

The communication of persons based on mobile Internet is more and more frequency as the widespread of social network media like Facebook, Weibo and so on. Faced with the vast amount of user groups and variety user features, the traditional single recommended algorithm turns to noneffective. In this paper, we use the collaborative filtering to select features in social network relationship which is used to build a vector space model. Also, we propose a distributed algorithm based on factorization machines and genetic algorithm, and use the vector space model as one part of input. We split the records into different sessions depend on the time sequence for the filter of train dataset. In the experiment part we proved that all the methods mentioned above remarkable improved the recommendation accuracy.
Enhanced clustered voltage scaling for low power

This paper presents a voltage scaling approach that is based on an enhanced variant of clustered voltage scaling originally proposed by Usami and Horowitz ([1]) The results show that subtituting the original depth first strategy with a breadth first one results in improved speed and quality of results. Data are validated through power and timing analysis performed with a commercial tool.
Discovery of a Be/X-Ray Binary Consistent with the Position of GRO J2058+42

GRO J2058+42 is a 195 s transient X-ray pulsar discovered in 1995 with BATSE. In 1996, RXTE located GRO J2058+42 to a 90% confidence error circle with a 4' radius. On 2004 February 20, the region including the error circle was observed with Chandra ACIS-I. No X-ray sources were detected within the error circle; however, two faint sources were detected in the ACIS-I field of view. We obtained optical observations of the brightest object, CXOU J205847.5+414637, which had about 64 X-ray counts and was just 0. ′3 outside the error circle. The optical spectrum contains a strong Hα line and corresponds to an infrared object in the Two Micron All Sky Survey catalog, indicating a Be/X-ray binary system. Pulsations were not detected in the Chandra observations, but similar flux variations and distance estimates suggest that CXOU J205847.5+414637 and GRO J2058+42 are the same object. We present results from the Chandra observation, optical observations, new and previously unreported RXTE observations, and a reanalysis of a ROSAT observation.
Research on Improved Data-Mining Algorithm Based on Strong Correlation

The extensive application of association rules in commerce enables itself to be one of the most active research directions in data mining. Recently, the mining of strong correlation item pairs with statistical significance in transaction database receives a certain value. In order to further reduce the cost of testing candidate item pairs in relational database, we have developed the Taper algorithm according to 1NF property. The developed TaperR algorithm can cut the number of candidate pairs to improve effciency.
High-Flux Solar-Driven Thermochemical Dissociation of CO2 and H2O Using Nonstoichiometric Ceria

Fuel from Heat Plants grow by using energy from the Sun to convert carbon dioxide into sugar-based polymers and aromatics. These compounds in turn can be stripped of their oxygen, either through millennia of underground degradation to yield fossil fuels, or through a rather more rapid process of dissolution, fermentation, and hydrogenation to yield biofuels. Can we use sunlight to turn CO2 into hydrocarbon fuel without relying on the intervening steps of plant growth and breakdown? Chueh et al. (p. 1797) demonstrate one possible approach, in which concentrated sunlight heats cerium oxide to a sufficiently high temperature (∼1500°C) to liberate some oxygen from its lattice. The material then readily strips O atoms from either water or CO2, yielding hydrogen or CO, which can then be combined to form fuels. Solar heating of ceric oxide enables a cycle for conversion of carbon dioxide to carbon monoxide or water to hydrogen. Because solar energy is available in large excess relative to current rates of energy consumption, effective conversion of this renewable yet intermittent resource into a transportable and dispatchable chemical fuel may ensure the goal of a sustainable energy future. However, low conversion efficiencies, particularly with CO2 reduction, as well as utilization of precious materials have limited the practical generation of solar fuels. By using a solar cavity-receiver reactor, we combined the oxygen uptake and release capacity of cerium oxide and facile catalysis at elevated temperatures to thermochemically dissociate CO2 and H2O, yielding CO and H2, respectively. Stable and rapid generation of fuel was demonstrated over 500 cycles. Solar-to-fuel efficiencies of 0.7 to 0.8% were achieved and shown to be largely limited by the system scale and design rather than by chemistry.
High-Altitude Particle Acceleration and Radiation in Pulsar Slot Gaps

We explore the pulsar slot gap (SG) electrodynamics up to very high altitudes, where for most relatively rapidly rotating pulsars both the standard small-angle approximation and the assumption that the magnetic field lines are ideal streamlines break down. We address the importance of the electrodynamic conditions at the SG boundaries and the occurrence of a steady state drift of charged particles across the SG field lines at very high altitudes. These boundary conditions and the deviation of particle trajectories from streamlines determine the asymptotic behavior of the scalar potential at all radii from the polar cap (PC) to near the light cylinder. As a result, we demonstrate that the steady state accelerating electric field, E∥, must approach a small and constant value at high altitude above the PC. This E∥ is capable of maintaining electrons moving with high Lorentz factors (~few × 107) and emitting curvature γ-ray photons up to nearly the light cylinder. By numerical simulations, we show that primary electrons accelerating from the PC surface to high altitude in the SG along the outer edge of the open field region will form caustic emission patterns on the trailing dipole field lines. Acceleration and emission in such an extended SG may form the physical basis of a model that can successfully reproduce some pulsar high-energy light curves.
Situational Effects of Task and Gender on Nonverbal Display

This study examines the impact of sex, psychological gender, and task on ten nonverbal conversational dominance and listener attentiveness cues. Results of a repeated measure analysis on task demonstrate that subjects, in general, use more warmth-attentiveness cues on the feminine task and more conversational control cues on the masculine one. But androgynous males adapted nonverbal cues to fit task demands, while androgynous females remained constant in frequency of cues between task situations. Masculine males, however, were more controlling on the feminine than on the masculine task, while sex-typed females were more expressive on the feminine than on the masculine task. Correlations among nonverbal acts demonstrate that cues cluster into listener behaviors and body movement or speaker acts and that the same subjects employ different patterns across task situations.
Cualidades Fisiológicas y Biomecánicas de Jugador Joven de Liga EBA

The objective of this study is identify the functional characteristics and mechanics in the jump of young basketball players of EBA League. The findings of the study will allow adaptation of methods and means of training to reach high performance levels. In 10 basketball players aged 19.3±0.7 years aerobic and anaerobic potential, corporal composition and characteristics of the mechanics of jumps were determined in both laboratory and field tests. The players had a weight of 86.8±2.6 and a height of 197.1±2.9 cm, with an average of 8.2±0.3% of body fat, and an average of 50.3±0.5% muscle mass. In the incremental treatmill test they reached a VO 2 max of 61.2±1.3 ml.kg-1.min-1; similar to the result obtained in the Course Navette (60.04±1.2 ml.kg-1.min-1) and different in the Cooper test (56.9±1.7 ml.kg-1.min-1). In the battery of jumps on the contact platform the results were: (Abalakov= 41.5±1.4 cm, SJ= 32.1±1.4 cm, CMJ= 35±1.2 cm, RJ30= 42.4±0.8 jumps with a average power of 36.9±2.2 kg/ w, and in the Wingate test they carried out a average power of 594.6±20.2 W (6.85 w/ kg), a peak power output of 693.4±24.6 W (7.98 w/ kg), and an index of fatigue of-28.7. The valuation of these characteristics is supposed to be an instrument to prescribe more individualized training sessions, in order to obtain a higher performance. KEY WORDS: Basketball, Physiology, Aerobic capacity, Anaerobic capacity; Muscular composition, VO 2 max, Wingate.
Unmanned Ground Vehicle Navigation Using Aerial Ladar Data

In this paper, we investigate the use of overhead high-resolution three-dimensional (3D) data for enhancing the performances of an unmanned ground vehicle (UGV) in vegetated terrains. Data were collected using an airborne laser and provided prior to the robot mission. Through extensive and exhaustive field testing, we demonstrate the significance of such data in two areas: robot localization and global path planning. Absolute localization is achieved by registering 3D local ground ladar data with the global 3D aerial data. The same data are used to compute traversability maps that are used by the path planner. Vegetation is filtered both in the ground data and in the aerial data in order to recover the load bearing surface.
Characterization of guided resonances in photonic crystal slabs using terahertz time-domain spectroscopy

We describe experimental studies of guided resonances in two-dimensional photonic crystal slabs using coherent single-cycle terahertz pulses. Our measurements directly reveal two stages of pulse transmission in the time domain: an initial pulse resulting from the direct transmission through the photonic crystal slab and a long-lived decaying tail resulting from the finite lifetime of guided resonance modes. These reflect the coupling of a set of discrete modes to the free-space continuum, which produces guided resonances with Fano resonance profiles. Phase spectra are also obtained experimentally, and these also exhibit the signatures of guided resonances. By changing the incident angle of the input beam, we change the spectrum of guided resonances excited by the incident wave. Our experimental results are in good agreement with numerical simulations.
Preferential Targeting of Nav1.6 Voltage-Gated Na+ Channels to the Axon Initial Segment during Development

During axonal maturation, voltage-gated sodium (Nav) channels accumulate at the axon initial segment (AIS) at high concentrations. This localization is necessary for the efficient initiation of action potentials. The mechanisms underlying channel trafficking to the AIS during axonal development have remained elusive due to a lack of Nav reagents suitable for high resolution imaging of channels located specifically on the cell surface. Using an optical pulse-chase approach in combination with a novel Nav1.6 construct containing an extracellular biotinylation domain we demonstrate that Nav1.6 channels are preferentially inserted into the AIS membrane during neuronal development via direct vesicular trafficking. Single-molecule tracking illustrates that axonal channels are immediately immobilized following delivery, while channels delivered to the soma are often mobile. Neither a Nav1.6 channel lacking the ankyrin-binding motif nor a chimeric Kv2.1 channel containing the Nav ankyrinG-binding domain show preferential AIS insertion. Together these data support a model where ankyrinG-binding is required for preferential Nav1.6 insertion into the AIS plasma membrane. In contrast, ankyrinG-binding alone does not confer the preferential delivery of proteins to the AIS.
Introducing error estimation in the shape learning framework : Spline based registration and non-parametric density estimator in the space of higher order polynomials

In this report, we introduce a new technique to shape modelling in the space of implicit polynomials. Registration consists of recovering an optimal one-to-one transformation of a higher order polynomial along with uncertainties measures that are determined according to the covariance matrix of the correspondences at the zero isosurface. Such measures are used to weight the importance of the training samples in the modelling phase according to a variable bandwidth nonparametric density estimation process. The selection of the most appropriate kernels to represent the training set is done through the maximum likelihood criterion. Exceptional results for patterns of digits, related with the registration and the modelling aspects of our approach demonstrate the potentials of our method. ∗The author gratefully acknowledge Dr. Jean-Yves Audibert for fruitful discussion regarding the probabilistic models.