Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Valid Deterministic Localization Plan Problem

Grid graphs are popular testbeds for planning with incomplete information. In particular, it is studied a fundamental planning problem, localization, to investigate whether gridworlds make good testbeds for planning with incomplete information. It is found empirically that greedy planning methods that interleave planning and plan execution can localize robots very quickly on random gridworlds or mazes. Thus, they may not provide adequately challenging testbeds. On the other hand, it is showed that finding localization plans that are within a log factor of optimal is NP-hard. Thus there are instances of gridworlds on which all greedy planning methods perform very poorly. These theoretical results help empirical researchers to select appropriate planning methods for planning with incomplete information as well as testbeds to demonstrate them. However, for practical application of difficult instances we need a method for their fast decision. In this paper, we consider an approach to solve localization problem. In particular, we consider an explicit polynomial reduction from the decision version of the valid deterministic localization plan problem to the 3-satisfiability problem.
Developmental Gene Discovery in a Hemimetabolous Insect: De Novo Assembly and Annotation of a Transcriptome for the Cricket Gryllus bimaculatus

Most genomic resources available for insects represent the Holometabola, which are insects that undergo complete metamorphosis like beetles and flies. In contrast, the Hemimetabola (direct developing insects), representing the basal branches of the insect tree, have very few genomic resources. We have therefore created a large and publicly available transcriptome for the hemimetabolous insect Gryllus bimaculatus (cricket), a well-developed laboratory model organism whose potential for functional genetic experiments is currently limited by the absence of genomic resources. cDNA was prepared using mRNA obtained from adult ovaries containing all stages of oogenesis, and from embryo samples on each day of embryogenesis. Using 454 Titanium pyrosequencing, we sequenced over four million raw reads, and assembled them into 21,512 isotigs (predicted transcripts) and 120,805 singletons with an average coverage per base pair of 51.3. We annotated the transcriptome manually for over 400 conserved genes involved in embryonic patterning, gametogenesis, and signaling pathways. BLAST comparison of the transcriptome against the NCBI non-redundant protein database (nr) identified significant similarity to nr sequences for 55.5% of transcriptome sequences, and suggested that the transcriptome may contain 19,874 unique transcripts. For predicted transcripts without significant similarity to known sequences, we assessed their similarity to other orthopteran sequences, and determined that these transcripts contain recognizable protein domains, largely of unknown function. We created a searchable, web-based database to allow public access to all raw, assembled and annotated data. This database is to our knowledge the largest de novo assembled and annotated transcriptome resource available for any hemimetabolous insect. We therefore anticipate that these data will contribute significantly to more effective and higher-throughput deployment of molecular analysis tools in Gryllus.
Block-sparse signal recovery with synthesized multitask compressive sensing

The paper considers the problem of reconstructing blocks-sparse signals. A new algorithm, called synthesized multitask compressive sensing (SMCS), is proposed. In contrast to existing methods that rely on the availability of the sparsity structure information, the SMCS algorithm resorts to the multitask compressive sensing (MCS) technique for signal recovery. The SMCS algorithm synthesizes new compressive sensing (CS) tasks via circular-shifting operations and utilizes the minimum description length (MDL) principle to determine the proper set of the synthesized CS tasks for signal reconstruction. An outstanding advantage of SMCS is that it can achieve good signal reconstruction performance without using prior information on the block-sparsity structure. Simulations corroborate the theoretical developments.
Social set analysis: four demonstrative case studies

This paper argues that the basic premise of Social Network Analysis (SNA) -- namely that social reality is constituted by dyadic relations and that social interactions are determined by structural properties of networks-- is neither necessary nor sufficient, for Big Social Data analytics of Facebook or Twitter data. However, there exist no other holistic computational social science approach beyond the relational sociology and graph theory of SNA. To address this limitation, this paper presents an alternative holistic approach to Big Social Data analytics called Social Set Analysis (SSA). Based on the sociology of associations and the mathematics of classical, fuzzy and rough set theories, this paper proposes a research program. The function of which is to design, develop and evaluate social set analytics in terms of fundamentally novel formal models, predictive methods and visual analytics tools for Big Social Data. Four demonstrative case studies, employing SSA, covering the range of descriptive, predictive, visual and prescriptive analytics are presented and briefly discussed.
Small Modifications to Network Topology Can Induce Stochastic Bistable Spiking Dynamics in a Balanced Cortical Model

Abstract Directed random graph models frequently are used successfully in modeling the population dynamics of networks of cortical neurons connected by chemical synapses. Experimental results consistently reveal that neuronal network topology is complex, however, in the sense that it differs statistically from a random network, and differs for classes of neurons that are physiologically different. This suggests that complex network models whose subnetworks have distinct topological structure may be a useful, and more biologically realistic, alternative to random networks. Here we demonstrate that the balanced excitation and inhibition frequently observed in small cortical regions can transiently disappear in otherwise standard neuronal-scale models of fluctuation-driven dynamics, solely because the random network topology was replaced by a complex clustered one, whilst not changing the in-degree of any neurons. In this network, a small subset of cells whose inhibition comes only from outside their local cluster are the cause of bistable population dynamics, where different clusters of these cells irregularly switch back and forth from a sparsely firing state to a highly active state. Transitions to the highly active state occur when a cluster of these cells spikes sufficiently often to cause strong unbalanced positive feedback to each other. Transitions back to the sparsely firing state rely on occasional large fluctuations in the amount of non-local inhibition received. Neurons in the model are homogeneous in their intrinsic dynamics and in-degrees, but differ in the abundance of various directed feedback motifs in which they participate. Our findings suggest that (i) models and simulations should take into account complex structure that varies for neuron and synapse classes; (ii) differences in the dynamics of neurons with similar intrinsic properties may be caused by their membership in distinctive local networks; (iii) it is important to identify neurons that share physiological properties and location, but differ in their connectivity.
Convolutive Blind Signal Separation Based on Asymmetrical Contrast Functions

In this paper, we consider the blind signal separation problem for convolutive mixtures, in the real case. More precisely, we present a generalization of classical contrast functions to more flexible asymmetric forms. We provide several examples of these new criteria which are useful for sources having different high-order statistics. We also perform a statistical study of the proposed source separation approach, including both the consistency and the asymptotic normality aspects. These theoretical results are also confirmed by numerical simulations
Shrinkage k-Means: A Clustering Algorithm Based on the James-Stein Estimator

In this work, we propose Shrinkage k-means (Sk-means), a novel variant of k-means based on the James-Stein estimator for the mean of a multivariate normal given a single sample point. We evaluate Sk-means on both synthetic and real-world data. The proposed method outperformed standard clustering methods and also the existing method based on k-means which uses the James-Stein estimator. Results also suggest that Sk-means is robust to outliers.
The Impact of Nursing Characteristics and the Work Environment on Perceptions of Communication

Failure to communicate openly and accurately to members of the healthcare team can result in medical error. The purpose of this study was to explore the impact of nursing characteristics and environmental values on communication in the acute care setting. Nurses (n = 135) on four medical-surgical units in two hospitals completed a survey asking nurses' perceptions of communication, work environment, and nursing demographics. LPNs perceived significantly higher levels of open communication with nurses than did RNs (P = .042). RNs noted higher levels of accuracy of communication among nurses than did LPNs (P < .001). Higher experience levels resulted in greater perceptions of open communication. Only environmental values (e.g., trust, respect) were a significant predictor of both openness and accuracy of communication. These findings suggest understanding the environment (e.g., presence or absence of trust, respect, status equity, and time availability) is a foundational step that must occur before implementing any strategies aimed at improving communication.
Modeling of irregular non-planar transition effects formed by bulk micromachining on high resistivity silicon wafer

An irregular non-planar transition effect, that connects a 50Omega microstrip to a 16Omega partially shielded valley microstrip is studied. The transition effect is modeled using LC T-network and distributed transmission line methods. The circuit model results show agreement with full-wave simulation and measurement data up to approximately 32GHz
A Framework for Formalizing UML Models with Formal Language Rcos

In this article, the formal object-oriented specification notation (Rcos) is used to formally combine some UML models. From the combination we can study the consistency conditions among a number of related UML models. The refinement calculus for Rcos will then allow us to transform these related models to preserve the consistency and functionality of the models.
Redundancy reduction for multi-document summaries using A* search and discriminative training

In this paper we address the problem of optimizing global multidocument summary quality using A* search and discriminative training. Different search strategies have been investigated to find the globally best summary. In them the search is usually guided by an existing prediction model which can distinguish between good and bad summaries. However, this is problematic because the model is not trained to optimize the summary quality but some other peripheral objective. In this work we tackle the global optimization problem using A* search with the training of prediction model intact and demonstrate our method to reduce redundancy within a summary. We use the framework proposed by Aker et al. [1] as a baseline and adapt it to globally improve the summary quality. Our results show significant improvements over the baseline.
The Antemortem Diagnosis of Primary Malignant Pericardial Mesothelioma: A Multidisciplinary Evaluation

A 64-year-old woman complaining of progressive dyspnea was admitted with recurrence of massive pericardial effusion. The patient had been diagnosed with radiation pericarditis based on a previous case of pericardiocentesis. To make a diagnosis and improve her symptoms, imaging examinations and pericardial fenestration were performed. Because of difficulty making a diagnosis, after some months, pericardiotomy and incision of the epicardium were performed. The patient was ultimately diagnosed with primary malignant pericardial mesothelioma of the epithelioid type. Primary malignant pericardial mesothelioma is a rare tumor that is difficult to diagnose. An antemortem diagnosis can be made by a multidisciplinary evaluation.
Databugger: a test-driven framework for debugging the web of data

Linked Open Data (LOD) comprises of an unprecedented volume of structured data on the Web. However, these datasets are of varying quality ranging from extensively curated datasets to crowd-sourced or extracted data of often relatively low quality. We present Databugger, a framework for test-driven quality assessment of Linked Data, which is inspired by test-driven software development. Databugger ensures a basic level of quality by accompanying vocabularies, ontologies and knowledge bases with a number of test cases. The formalization behind the tool employs SPARQL query templates, which are instantiated into concrete quality test queries. The test queries can be instantiated automatically based on a vocabulary or manually based on the data semantics. One of the main advantages of our approach is that domain specific semantics can be encoded in the data quality test cases, thus being able to discover data quality problems beyond conventional quality heuristics.
Secondary User Sensing Time Optimization in Sensing-Transmission Scheduling for Dynamic Spectrum Access in Cognitive Radio

The constraint of limited frequency spectrum for wireless communication is very daunting. The radio spectrum licensed to permanent users is wasted at times when not used continuously. Cognitive radio technology involves a change in regulation of radio spectrum. In cognitive radio networks a secondary user network is designed for efficiently using gaps in spectrum and not causing at the same time harmful interference to license holding primary users. Opportunistic spectrum access involves the sensing of white spaces in the transmissions of Primary Users and their utilization by Secondary Users. There is a continuous tradeoff between the opposing goals of Primary User transmission protection and Secondary User throughput maximization. For a Secondary User, sensing is important so as to avoid collisions, hence packet loss. At the same time being too cautious and having long sensing periods can be costly. This paper presents optimization of Secondary User sensing time in sensing-transmission scheduling for dynamically accessing the spectrum in cognitive radios. The need to maximize the Secondary User access is achieved by optimizing its sensing time with respect to collision cost in a threshold based sensing-transmission structure using simulations. The results are presented in the form of traces of thresholds for different sensing times and collision costs. This scheme optimizes Secondary User throughput while safeguarding Primary User interests. This defines a standard to give an insight into the design of sensing-transmission scheduling in cognitive radio networks.
The effects of co-present embodiments on awareness and collaboration in tabletop groupware

Most current tabletop groupware systems use direct touch, where people manipulate objects by touching them with a pen or a fingertip. The use of people's real arms and hands provides obvious awareness information, but workspace access is limited by the user's reach. Relative input techniques, where users manipulate a cursor rather than touching objects directly, allow users to reach all areas of the table. However, the only available awareness information comes from the virtual embodiment of the user (e.g., their cursor). This presents designers with a tradeoff: direct-touch techniques have advantages for group awareness; relative input techniques offer additional power but less awareness information. In this paper, we explore this tradeoff, and we explore the design space of virtual embodiments to determine whether factors such as size, realism, and visibility can improve awareness and coordination. We conducted a study in which seven groups carried out a picture-categorizing task using seven techniques: direct touch and relative input with six different virtual embodiments. Our results provide both valuable information to designers of tabletop groupware, and a number of new directions for future research.
Reinforcement Learning for Elevator Control

Abstract Reinforcement learning (RL) comprises an array of techniques that learn a control policy so as to maximize a reward signal. When applied to the control of elevator systems, RL has the potential of finding better control policies than classical heuristic, suboptimal policies. On the other hand, elevator systems offer an interesting benchmark application for the study of RL. In this paper, RL is applied to a single-elevator system. The mathematical model of the elevator system is described in detail, making the system easy to re-implement and re-use. An experimental comparison is made between the performance of the Q-value iteration and Q-learning RL algorithms, when applied to the elevator system.
Radiation of photons in process of charged particle volume reflection in bent monocrystal

New type of radiation in crystals is predicted and investigated in computer simulation. It is shown that process of volume reflection of electrons and positrons in bent crystals is accomplished with high-power radiation of photons. Volume reflection radiation has intensity comparable with known channeling radiation, but it is less sensitive to entrance angle and sign of charge of a particle. Simulated spectra of radiation power are presented for 10 GeV and 200GeV particles.
Biological pattern recognition by neural networks

Neural network analysis was proposed and evaluated as a method for image analysis of plankton data derived from automatic counting techniques. It was shown that a neural network with 2 layers of weights was capable of learning a large data set by the backward-error propagation method. Significant results were achieved in separating novel images of 2 co-occurring species of Ceratium spp. from the western North Atlantic Ocean.
Backhaul resource allocation in LTE-Advanced relaying systems

In 3GPP LTE-Advanced, relay is considered as an important means for extending cell coverage and improving capacity. Backhaul resource allocation needs to be carefully designed to achieve the performance advantage of relay. In this paper, we propose a two-step backhaul resource allocation scheme for an inband relaying system based on relay buffer level. The resource assignment to relays meets the actual traffic demand, takes into account the link quality of the backhaul link and the access link, and the resource partitioning between relays and macro UEs is then optimized. Our evaluations show that the proposed framework can improve the cell average and cell-edge user performance, for different deployment scenarios. This approach has very low complexity in implementation and the revision to current standard is very minimal.
An approximation algorithm for the maximum leaf spanning arborescence problem

We present an O(&sqrt;opt)-approximation algorithm for the maximum leaf spanning arborescence problem, where opt is the number of leaves in an optimal spanning arborescence. The result is based upon an O(1)-approximation algorithm for a special class of directed graphs called willows. Incorporating the method for willow graphs as a subroutine in a local improvement algorithm gives the bound for general directed graphs.
Data mining framework for fatty liver disease classification in ultrasound: A hybrid feature extraction paradigm.

PURPOSE Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.
Assessment of the Risk of Voltage Collapse in a Power System Using Intelligent Techniques

This paper describes the implementation of a fast and easy-to-use, intelligence-based algorithm to assess the risk of voltage collapse when risk is defined as the product of the event likelihood and a severity function. In the event likelihood, the effect of weather is taken into account; the failure rate of each transmission line under different weather conditions is calculated using real historical outage data. A new severity function model that utilises the voltage collapse prediction index is proposed in this paper. Two intelligent techniques, i.e., support vector machines and a generalised regression neural network are studied, and their performances are evaluated using mean absolute and mean square error. The proposed methodology has been applied in a real power system network. Simulation results show that a generalized regression neural network provides the lowest mean absolute and mean square error.
X‐chromosome inactivation during spermatogenesis is regulated by an Xist/Tsix‐independent mechanism in the mouse

Summary: Transcriptional inactivation of the single X chromosome occurs in spermatogenic cells during male meiosis in mammals and has been shown to be coincident with expression of the Xist gene in spermatogonia and spermatocytes in mice. However, male mice carrying an ablated Xist gene show normal fertility. Here we examined expression from the Xist locus during spermatogenesis in wild‐type mice and detected sense (Xist), but not antisense (Tsix) transcripts. In addition, we examined expression and chromatin conformation of X‐linked structural genes in meiotic and postmeiotic spermatogenic cells from wild‐type and Xist‐ mice and found no differences associated with the absence of a functional Xist gene. These results, along with the formation of a morphologically normal XY body in primary spermatocytes in Xist‐ mice, indicate that a functional Xist gene is not required for X‐chromosome inactivation during spermatogenesis and that this process is therefore regulated by a different mechanism than that which regulates X‐chromosome inactivation in female embryonic cells. genesis 34:257–266, 2002. © 2002 Wiley‐Liss, Inc.