Miracle-dz/UniFAR-FTUs huggingface dataset:
Using BLSTM for Interpretation of 2D Languages - Case of Handwritten Mathematical Expressions

In this work, we study how to extend the capability of BLSTM networks to process data which are not only text strings but graphical two-dimensional languages such as handwrit- ten mathematical expressions. The proposed solution aims at transforming the mathematical expression description into a sequence including at the same time symbol labels and relation- ship labels, so that classical supervised sequence labeling with recurrent neural networks can be applied. For simple one-dimensional (1-D) expression, we use the Right label to segment one symbol from the next one, as with the standard blank label for regular text. For genuine two- dimensional (2-D) expressions, we introduce additional speciﬁc labels assigned to each of the different possible spatial relationships that exist between sub-expressions. As a result, BLSTM network is able to perform at the same time the symbol recognition task and the segmentation task, which is a new perspective for the mathematical expression domain.
Neural network emulation of a rainfall-runoff model

The potential of an artificial neural network to perform simple non-linear hydrological transformations is examined. Four neural network models were developed to emulate di erent facets of a recognised non-linear hydrological transformation equation that possessed a small number of variables and contained no temporal component. The 5 modeling process was based on a set of uniform random distributions. The cloning operation facilitated a direct comparison with the exact equation-based relationship. It also provided broader information about the power of a neural network to emulate existing equations and model non-linear relationships. Several comparisons with least squares multiple linear regression were performed. The first experiment involved a di10 rect emulation of the Xinanjiang Rainfall-Runo Model. The next two experiments were designed to assess the competencies of two neural solutions that were developed on a reduced number of inputs. This involved the omission and conflation of previous inputs. The final experiment used derived variables to model intrinsic but otherwise concealed internal relationships that are of hydrological interest. Two recent studies 15 have suggested that neural solutions o er no worthwhile improvements in comparison to traditional weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. Yet such fundamental properties are intrinsic aspects of catchment processes that cannot be excluded or ignored. The results from the four experiments that are reported in this paper are used to challenge the interpretations 20 from these two earlier studies and thus further the debate with regards to the appropriateness of neural networks for hydrological modelling.
Fingerprints: detecting meaningful moments for mobile health intervention

Personalized and contextual interventions are promising techniques for mobile persuasive technologies in mobile health. In this paper, we propose the "fingerprints" technique to analyze the users' daily behavior patterns to find the meaningful moments to better support mobile persuasive technologies, especially mobile health interventions. We assume that for many persons, their behaviors have patterns and can be detected through the sensor data from smartphones. We develop a three-step interactive machine learning workflow to describe the concept and approach of the "fingerprints" technique. By this we aim to implement a practical and light-weight mobile intervention system without burdening the users with manual logging. In our feasibility study, we show results that provide first insights into the design of the "fingerprints" technique.
Motion Planning and Trajectory Tracking on 2-D Manifolds embedded in 3-D Workspaces

In this paper we present a methodology that drives and stabilizes a robotic agent moving in a three dimensional environment, to a 2-dimensional manifold embedded in the workspace. Once the agent reaches the manifold, depending on the application, it performs a motion planning or a trajectory tracking task. Appropriately constructed belt-zone vector fields guarantee that the agent will not depart the 2-D manifold proximity area, while carrying out the motion planning or trajectory tracking task. The derived closed form feedback control law guarantees global convergence and collision avoidance. The properties of the proposed algorithm are verified through non-trivial computer simulations.
AURA: a hybrid approach to identify framework evolution

Software frameworks and libraries are indispensable to today's software systems. As they evolve, it is often time-consuming for developers to keep their code up-to-date, so approaches have been proposed to facilitate this. Usually, these approaches cannot automatically identify change rules for one-replaced-by-many and many-replaced-by-one methods, and they trade off recall for higher precision using one or more experimentally-evaluated thresholds. We introduce AURA, a novel hybrid approach that combines call dependency and text similarity analyses to overcome these limitations. We implement it in a Java system and compare it on five frameworks with three previous approaches by Dagenais and Robillard, M. Kim et al., and Schäfer et al. The comparison shows that, on average, the recall of AURA is 53.07% higher while its precision is similar, e.g., 0.10% lower.
The impact of authentication on health digital resources

Security is a major issue within the health domain. It is important to ensure that sensitive personalised data is protected from misuse. However, there is also a need for efficient medical systems that don’t impede clinicians work practices. This paper will briefly detail some of the authentication issues that have been highlighted within two comparative hospital studies in the health domain. 93 clinicians’ perceptions towards clinical information resources were analysed whereby security and authentication issues were highlighted as a critical issue. This paper details how those issues related to organisational structures and discusses how authentication and security must be designed around communities of practice.
Force sensor system for structural health monitoring using passive RFID tags for structural health monitoring

For this study, we developed a contactless loading sensor system that can measure the internal loading of an object structure through several covering materials for the purpose of structural health monitoring. The developed system can be inserted into objects without a battery because the system consists of passive RFID for data communication and a power supply. The system uses little electric power because the power supply with RFID is generally very low. We propose an architecture by which two RFID tags are used in the system. The functions of the tags are separated for communication and for the power supply as a circuit design contraption to solve the problem. First, we explain how the developed system is useful in an actual environment and introduce details of the developed sensor system. Second, the sensor system performance is evaluated through comparison of calculated results and experimental results. Based on those evaluations, the practical utility for structural health monitoring of the system is described.
Citrus auraptene exerts dose-dependent chemopreventive activity in rat large bowel tumorigenesis: the inhibition correlates with suppression of cell proliferation and lipid peroxidation and with induction of phase II drug-metabolizing enzymes.

In our previous short-term experiment, Citrus auraptene inhibited the development of azoxymethane (AOM)-induced aberrant crypt foci, which are precursor lesions for colorectal carcinoma. In the present study, the possible inhibitory effect of dietary administration of auraptene was investigated using an animal colon carcinogenesis model with a colon carcinogen AOM. Male F344 rats were given s.c. injections of AOM (15 mg/kg body weight) once a week for 3 weeks to induce colon neoplasms. They also received diets containing 100 or 500 ppm auraptene for 4 weeks in groups of "initiation" feeding, starting 1 week before the first dosing of AOM. The diets containing auraptene were also given to rats for 38 weeks in groups of "postinitiation" feeding. At the termination of the study (38 weeks), dietary administration of auraptene caused dose-dependent inhibition in AOM-induced large bowel carcinogenesis. Auraptene feeding during the initiation phase reduced the incidence of colon adenocarcinoma by 49% at 100 ppm (P = 0.099) and 65% at 500 ppm (P = 0.0075). Auraptene administration during the postinitiation phase inhibited the incidence of colon adenocarcinoma by 58% at 100 ppm (P = 0.021) and 65% at 500 ppm (P = 0.0075). Also, the multiplicity of colon carcinoma was significantly reduced by initiation feeding at a dose level of 500 ppm (P < 0.01) and postinitiation feeding at a level of 100 and 500 ppm (P < 0.05 and P < 0.01, respectively). Feeding of auraptene suppressed the expression of cell proliferation biomarkers (ornithine decarboxylase activity and polyamine content) in the colonic mucosa and reduced the production of aldehydic lipid peroxidation [malondialdehyde and 4-hydroxy-2(E)-nonenal]. In addition, auraptene increased the activities of Phase II drug-metabolizing enzymes (glutathione S-transferase and quinone reductase) in the liver and colon. These findings suggest that the inhibitory effects of auraptene on AOM-induced colon tumorigenesis at the initiation level might be associated, in part, with increased activity of Phase II enzymes, and those at the postinitiation stage might be related to suppression of cell proliferation and lipid peroxidation in the colonic mucosa.
Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm

In this paper, we proposed an efficient and accurate text chunking system using linear SVM kernel and a new technique called masked method. Previous researches indicated that systems combination or external parsers can enhance the chunking performance. However, the cost of constructing multi-classifiers is even higher than developing a single processor. Moreover, the use of external resources will complicate the original tagging process. To remedy these problems, we employ richer features and propose a masked-based method to solve unknown word problem to enhance system performance. In this way, no external resources or complex heuristics are required for the chunking system. The experiments show that when training with the CoNLL-2000 chunking dataset, our system achieves 94.12 in F (b) rate with linear. Furthermore, our chunker is quite efficient since it adopts a linear kernel SVM. The turnaround tagging time on CoN-LL-2000 testing data is less than 50 s which is about 115 times than polynomial kernel SVM.
A large‐scale view of Space Technology 5 magnetometer response to solar wind drivers

In this data report we discuss reprocessing of the Space Technology 5 (ST5) magnetometer database for inclusion in NASA's Coordinated Data Analysis Web (CDAWeb) virtual observatory. The mission consisted of three spacecraft flying in elliptical orbits, from 27 March to 27 June 2006. Reprocessing includes (1) transforming the data into the Modified Apex Coordinate System for projection to a common reference altitude of 110 km, (2) correcting gain jumps, and (3) validating the results. We display the averaged magnetic perturbations as a keogram, which allows direct comparison of the full‐mission data with the solar wind values and geomagnetic indices. With the data referenced to a common altitude, we find the following: (1) Magnetic perturbations that track the passage of corotating interaction regions and high‐speed solar wind; (2) unexpectedly strong dayside perturbations during a solstice magnetospheric sawtooth oscillation interval characterized by a radial interplanetary magnetic field (IMF) component that may have enhanced the accompanying modest southward IMF; and (3) intervals of reduced magnetic perturbations or “calms,” associated with periods of slow solar wind, interspersed among variable‐length episodic enhancements. These calms are most evident when the IMF is northward or projects with a northward component onto the geomagnetic dipole. The reprocessed ST5 data are in very good agreement with magnetic perturbations from the Defense Meteorological Satellite Program (DMSP) spacecraft, which we also map to 110 km. We briefly discuss the methods used to remap the ST5 data and the means of validating the results against DMSP. Our methods form the basis for future intermission comparisons of space‐based magnetometer data.
General capacity region for the fully-connected 3-node packet erasure network

This work considers the fully-connected 3-node packet erasure network: For each time slot, with some probabilities a packet sent by any node i may be received by both of the other nodes j and k; received only by node j (or node k); or received by neither nodes. Interference is avoided by enforcing that at most one node can transmit in each time slot. We assume that node i can always reach node j, possibly with the help of the third node k, for any i ≠ j pairs (thus the term fully-connected). One example of this model is any Wi-Fi network with 3 nodes within the hearing range of each other. We consider the most general traffic demands. Namely, there are six private-information flows with rates (R<sub>1→2</sub>,R<sub>1→3</sub>,R<sub>2→1</sub>, R<sub>2→3</sub>,R<sub>3→1</sub>,R<sub>3→2</sub>), respectively, and three common-information flows with rates (R<sub>1→23</sub>,R<sub>2→31</sub>,R<sub>3→12</sub>), respectively. We characterize the 9-dimensional Shannon capacity region within a gap that is inversely proportional to the packet size (bits). The gap can be attributed to exchanging reception status (ACK) and can be further reduced to 0 if we allow ACK to be transmitted via a separate control channel. For normal-sized packets, say 12000 bits, our results have thus effectively characterized the capacity region. Technical contributions of this work include a new converse for many-to-many network communications and a new capacity-approaching simple linear network coding scheme.
High level domain definition in a relational date base system

A relational data base is herein defined as a collection of normalized relations (relations in first normal form) and a collection of domains. A normalized relation may be viewed as a table, wherein each row of the table corresponds to a tuple of the relation, and the entries in a given column belong to the set of values constituting the domain underlying that column. The domains of a data base have an abstract existence apart from the data base relations. The data base also includes various types of semantic integrity rules, which specify additional properties of the data in the data base. One such type of semantic integrity rule is the domain definition. A domain definition includes the precise description of the set of values (objects) constituting the domain. In a normalized data base, all domains are sets of atomic data values. A domain definition also includes a specification of the ordering on the values in a domain, for comparability purposes. In addition, a domain definition contains a specification of the action that is to occur if an attempt is made to violate the restriction that every entry in each column of a relation must be from the underlying domain of that column. A nonprocedural language permitting the high level expression of domain definitions is defined. Language details and examples are presented, and the syntax and informal semantics of the domain definition language are given. This approach to domain definition is analyzed in terms of its impact on other aspects of data base semantic integrity. The relationship with the data base system in general is outlined. An analysis of intradomain and interdomain comparability is included. An introduction to relevant implementation issues is also presented. Emphasis is placed on a general approach to implementation and implementation techniques, rather than on a specific system.
R-Linear Convergence of Limited Memory Steepest Descent

The limited memory steepest descent method (LMSD) proposed by Fletcher is an extension of the Barzilai-Borwein "two-point step size" strategy for steepest descent methods for solving unconstrained optimization problems. It is known that the Barzilai-Borwein strategy yields a method with an R-linear rate of convergence when it is employed to minimize a strongly convex quadratic. This paper extends this analysis for LMSD, also for strongly convex quadratics. In particular, it is shown that the method is R-linearly convergent for any choice of the history length parameter. The results of numerical experiments are provided to illustrate behaviors of the method that are revealed through the theoretical analysis.
Analysis of divergences from area navigation departure routes at DFW airport

The Next Generation Air Transportation System (NextGen) calls for the extensive use of trajectory management for aircraft to achieve precision flight paths [1]. To understand, develop, and model systems that support these NextGen operations, especially in the terminal area, NASA is looking at today's precision operations to gain insight into the expected behavior. This paper documents characteristics of aircraft that are both on and vectored from routes in the execution of area navigation (RNAV) precision departures to support precision modeling and provide for NextGen super density operations research. Dallas/Fort Worth International Airport (DFW) was selected for this case study as these kinds of precise departure procedures have been in daily use there for years. One-third of DFW RNAV departures encounter some form of vectoring away from the defined RNAV routes. The majority of these, about one-quarter of the departures, are given direct routings that bypass fixes on the route and shorten the distance flown within the Terminal Radar Approach Control (TRACON). These divergences primarily result from controllers taking advantage of opportunities in the airborne traffic, similar to direct-to routing in enroute airspace [2], and are not the result of departure sequencing or avoiding loss of separation. During the planning of the RNAV procedures, some of this vectoring was expected and even encouraged, but the number of aircraft so affected has grown over time. Pilots and air traffic controllers use the precision navigation capability required for the RNAV departure procedures to bypass portions of the routes. While this is applicable to DFW alone, it is a reminder that the human elements in the system frequently find new and innovative uses for elements of the procedures, or the technology behind them. The numbers of aircraft vectored in the course of RNAV departure operations is comparable to those departing with reduced spacing, the main benefit of the original RNAV implementation. The data presented here demonstrate the flexibility of the procedures as currently used.
Approximating matrix multiplication for pattern recognition tasks

Many pattern recognition tasks, including estimation, classification, and the finding of similar objects, make use of linear models. The fundamental operation in such tasks is the computation of the dot product between a query vector and a large database of instance vectors. Often we are interested primarily in those instance vectors which have high dot products with the query. We present a random sampling based algorithm that enables us to identify, for any given query vector, those instance vectors which have large dot products, while avoiding explicit computation of all dot products. We provide experimental results that demonstrate considerable speedups for text retrieval tasks. Our approximate matrix multiplication algorithm is applicable to products of k ≥ 2 matrices and is of independent interest. Our theoretical and experimental analysis demonstrates that in many scenarios, our method dominates standard matrix multiplication.
Passive source localization using compressively sampled vector sensor array

Accurate estimation of direction of arrival (DoA) with minimum ocean bed array hardware is a challenging task. Any reduction in the data acquisition circuitry positioned in the ocean bed significantly improves the signal integrity and hence the localization performance. In this paper, a novel compressively sampled acoustic vector sensor (CS-AVS) array architecture is proposed to estimate the DoA of multiple acoustic sources. Bearing localization is effectively achieved by customizing multiple signal classification (MUSIC) and minimum variance distortionless response estimator (MVDR) to suit the CS-AVS array. This architecture guarantees a substantial reduction in the number of sensors, acquisition hardware, bandwidth, number of snapshots and the software complexity.
Towards an Architecture Model for Emotion Recognition in Interactive Systems: Application to a Ballet Dance Show

In the context of the very dynamic and challenging domain of affective computing, we adopt a software engineering point of view on emotion recognition in interactive systems. Our goal is threefold: first, developing an architecture model for emotion recognition. This architecture model emphasizes multimodality and reusability. Second, developing a prototype based on this architecture model. For this prototype we focus on gesturebased emotion recognition. And third, using this prototype for augmenting a ballet dance show.
Intra-operative monocular 3D reconstruction for image-guided navigation in active locomotion capsule endoscopy

Wireless capsule endoscopy (WCE) can be considered an example of `disruptive technology' since it represents a bright alternative to traditional diagnostic methodologies. However, currently available clinical products are passive devices whose locomotion is driven by natural peristalsis, with the drawback of failing to capture the images of potential pathological gastrointestinal tract regions, due to the impossibility to control the camera's motion and orientation. As a consequence, many research groups are working to develop active locomotion devices and endoscopic platforms that allow WCE to be performed in a totally controlled manner. In this paper we propose a system to reliably calibrate a Shape-from-Shading system to recover the unknown scale factor immediately prior to a WCE procedure. The system would allow to reconstruct in a metrically accurate fashion the surfaces obtained intraoperatively from the WCE capsule, which can be exploited for accurate trajectory planning and ultimately automatic WCE active capsule navigation. To the best of our knowledge, this is the first practical system for Shape-from-Shading calibration aimed at guidance applications that will finally be applied to an endoscopic active locomotion platform developed by the authors.