Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolving multirobot excavation controllers and choice of platforms using an artificial neural tissue paradigm

Autonomous robotic excavation has often been limited to a single robotic platform using a specified excavation vehicle. This paper presents a novel method for developing scalable controllers for use in multirobot scenarios and that do not require human defined operations scripts nor extensive modeling of the kinematics and dynamics of the excavation vehicles. Furthermore, the control system does not require specifying an excavation vehicle type such as a bulldozer, front-loader or bucket-wheel and it can evolve to select for an appropriate choice of excavation vehicles to successfully complete a task. The “Artificial Neural Tissue” (ANT) architecture is used as a control system for autonomous multirobot excavation and clearing tasks. This control architecture combines a variable-topology neural-network structure with a coarse-coding strategy that permits specialized areas to develop in the tissue. Training is done in a low-fidelity grid world simulation environment and where a single global fitness function and a set of allowable basis behaviors need be specified. This approach is found to provide improved training performance over fixed-topology neural networks and can be easily ported onto different robot platforms. Aspects of the controller functionality have been tested using high fidelity dynamics simulation and in hardware. An evolutionary training process discovers novel decentralized methods of cooperation employing aggregation behaviors (via synchronized movements). These aggregation behaviors are found to improve controller scalability (with increasing robot density) and better handle robot interference (antagonism) that reduces the overall efficiency of the group.
Precise Predictive Analysis for Discovering Communication Deadlocks in MPI Programs

The Message Passing Interface (MPI) is the standard API for parallelization in high-performance and scientific computing. Communication deadlocks are a frequent problem in MPI programs, and this article addresses the problem of discovering such deadlocks. We begin by showing that if an MPI program is single path, the problem of discovering communication deadlocks is NP-complete. We then present a novel propositional encoding scheme that captures the existence of communication deadlocks. The encoding is based on modeling executions with partial orders and implemented in a tool called MOPPER. The tool executes an MPI program, collects the trace, builds a formula from the trace using the propositional encoding scheme, and checks its satisfiability. Finally, we present experimental results that quantify the benefit of the approach in comparison to other analyzers and demonstrate that it offers a scalable solution for single-path programs.
Evaluation of Rice Resistance to Southern Rice Black-Streaked Dwarf Virus and Rice Ragged Stunt Virus through Combined Field Tests, Quantitative Real-Time PCR, and Proteome Analysis

Diseases caused by southern rice black-streaked dwarf virus (SRBSDV) and rice ragged stunt virus (RRSV) considerably decrease grain yield. Therefore, determining rice cultivars with high resistance to SRBSDV and RRSV is necessary. In this study, rice cultivars with high resistance to SRBSDV and RRSV were evaluated through field trials in Shidian and Mangshi county, Yunnan province, China. SYBR Green I-based quantitative real-time polymerase chain reaction (qRT-PCR) analysis was used to quantitatively detect virus gene expression levels in different rice varieties. The following parameters were applied to evaluate rice resistance: acre yield (A.Y.), incidence of infected plants (I.I.P.), virus load (V.L.), disease index (D.I.), and insect quantity (I.Q.) per 100 clusters. Zhongzheyou1 (Z1) and Liangyou2186 (L2186) were considered the most suitable varieties with integrated higher A.Y. , lower I.I. P., V.L. , D.I. and I.Q. features. In order to investigate the mechanism of rice resistance, comparative label-free shotgun liquid chromatography tandem-mass spectrometry (LC-MS/MS) proteomic approaches were applied to comprehensively describe the proteomics of rice varieties’ SRBSDV tolerance. Systemic acquired resistance (SAR)-related proteins in Z1 and L2186 may result in the superior resistance of these varieties compared with Fengyouxiangzhan (FYXZ).
GRAPE-8 -- An accelerator for gravitational N-body simulation with 20.5Gflops/W performance

In this paper, we describe the design and performance of GRAPE-8 accelerator processor for gravitational N-body simulations. It is designed to evaluate gravitational interaction with cutoff between particles. The cutoff function is useful for schemes like TreePM or Particle-Particle Particle-Tree, in which gravitational force is divided to short-range and longrange components. A single GRAPE-8 processor chip integrates 48 pipeline processors. The effective number of floating-point operations per interaction is around 40. Thus the peak performance of a single GRAPE-8 processor chip is 480 Gflops. A GRAPE-8 processor card houses two GRAPE-8 chips and one FPGA chip for PCI-Express interface. The total power consumption of the board is 46W. Thus, theoretical peak performance per watt is 20.5 Gflops/W. The effective performance of the total system, including the host computer, is around 5Gflops/W. This is more than a factor of two higher than the highest number in the current Green500 list.
Do Positive Feedback Traders Act in Germany’s Neuer Markt?

In this paper we provide the first empirical evidence on the importance of positive feedback trading on the return behavior in Germany 's Neuer Markt. Relying on the theoretical models of Shiller, Sentana, and Wadhwani, we exploit the link between index return autocorrelation and volatility to receive deeper insight into the return characteristics generated by traders adhering to positive feedback trading strategies. Our empirical evidence shows that positive feedback traders are present in Germany's Neuer Markt and induce negative return autocorrelation during periods of high volatility.
Vection with large screen 3D imagery

Vection is the illusory impression of self motion that can be obtained when an observer views a I,argescreen display containing a rotating or translating pattern. To aid in our construction of an interactive large screen interface to virtual 3D environments, we conducted studies to determine the factors which induce a sense of vection. We found that having a foreground frame and a stereo display increased vection. If subjects moved when the display was being observed then vection decreased. However, if the perspective was coupled to their head position while they moved then vection was restored.
About subjective evaluation of adaptive video streaming

The usage of HTTP Adaptive Streaming (HAS) technology by content providers is increasing rapidly. Having available the video content in multiple qualities, using HAS allows to adapt the quality of downloaded video to the current network conditions providing smooth video-playback. However, the time-varying video quality by itself introduces a new type of impairment. The quality adaptation can be done in different ways. In order to find the best adaptation strategy maximizing users perceptual quality it is necessary to investigate about the subjective perception of adaptation-related impairments. However, the novelties of these impairments and their comparably long time duration make most of the standardized assessment methodologies fall less suited for studying HAS degradation. Furthermore, in traditional testing methodologies, the quality of the video in audiovisual services is often evaluated separated and not in the presence of audio. Nevertheless, the requirement of jointly evaluating the audio and the video within a subjective test is a relatively under-explored research field. In this work, we address the research question of determining the appropriate assessment methodology to evaluate the sequences with time-varying quality due to the adaptation. This was done by studying the influence of different adaptation related parameters through two different subjective experiments using a methodology developed to evaluate long test sequences. In order to study the impact of audio presence on quality assessment by the test subjects, one of the experiments was done in the presence of audio stimuli. The experimental results were subsequently compared with another experiment using the standardized single stimulus Absolute Category Rating (ACR) methodology.
Formalisms for Specifying Markovian Population Models

We compare several languages for specifying Markovian population models such as queuing networks and chemical reaction networks. These languages --matrix descriptions, stochastic Petri nets, stoichiometric equations, stochastic process algebras, and guarded command models-- all describe continuous-time Markov chains, but they differ according to important properties, such as compositionality, expressiveness and succinctness, executability, ease of use, and the support they provide for checking the well-formedness of a model and for analyzing a model.
Function and ribosomal localization of aIF6, a translational regulator shared by archaea and eukarya

The translation factor IF6 is shared by the Archaea and the Eukarya, but is not found in Bacteria. The properties of eukaryal IF6 (eIF6) have been extensively studied, but remain somewhat elusive. eIF6 behaves as a ribosome-anti-association factor and is involved in miRNA-mediated gene silencing; however, it also seems to participate in ribosome synthesis and export. Here we have determined the function and ribosomal localization of the archaeal (Sulfolobus solfataricus) IF6 homologue (aIF6). We find that aIF6 binds specifically to the 50S ribosomal subunits, hindering the formation of 70S ribosomes and strongly inhibiting translation. aIF6 is uniformly expressed along the cell cycle, but it is upregulated following both cold- and heat shock. The aIF6 ribosomal binding site lies in the middle of the 30-S interacting surface of the 50S subunit, including a number of critical RNA and protein determinants involved in subunit association. The data suggest that the IF6 protein evolved in the archaeal–eukaryal lineage to modulate translational efficiency under unfavourable environmental conditions, perhaps acquiring additional functions during eukaryotic evolution.
Adversarial Bandit for online interactive active learning of zero-shot spoken language understanding

Many state-of-the-art solutions for the understanding of speech data have in common to be probabilistic and to rely on machine learning algorithms to train their models from large amount of data. The difficulty remains in the cost of collecting and annotating such data. Another point is the time for updating an existing model to a new domain. Recent works showed that a zero-shot learning method allows to bootstrap a model with good initial performance. To do so, this method relies on exploiting both a small-sized ontological description of the target domain and a generic word-embedding semantic space for generalization. Then, this framework has been extended to exploit user feedbacks to refine the zero-shot semantic parser parameters and increase its performance online. In this paper, we propose to drive this online adaptive process with a policy learnt using the Adversarial Bandit algorithm Exp3. We show, on the second Dialog State Tracking Challenge (DSTC2) datasets, that this proposition can optimally balance the cost of gathering valuable user feedbacks and the overall performance of the spoken language understanding module.
A low-power SoC-based moving target detection system for amphibious spherical robots

A moving target detection and tracking system is critical important for autonomous mobile robot to accomplish complicated tasks. Aiming at application requirements of our amphibious spherical robot proposed in previous researches, a low-power and portable moving target detection system was designed and implemented in this paper. Xilinx Zynq-7000 SoC (System on Chip) was used to fabricate the image processing system of the robot for detection and tracking. An OmniVision OV7670 COMS image sensor controlled by customized IP cores in the PL (Programmable Logic) of the SoC was adopted to acquire 640×480 RGB images at 30 frames per second. The Gaussian background modeling method was implemented with Vivado HLS in the PL to detect moving targets. And a FCT (Fast Compressive Tracking) tracker with motion estimation mechanism was running in the PS (Processing System) of the SoC to track targets captured by the detection subsystem subsequently. Besides, the dynamical power management (DPM) and the dynamical voltage frequency scaling (DVFS) mechanisms were used for a higher power-efficiency. Experimental results verified the validation and performance of the detection system. The design in this paper may have reference value for vision-based mobile robots or vehicles.
The Design and Application of PHABS: A Novel Benchmark Platform for Perceptual Hashing Algorithms

Numerous perceptual hashing algorithms have been developed for identification and verification of multimedia objects in recent years. Various application schemes have been adopted to provide the ability of forensic tracking against the illegal use or distribution of multimedia content. Developers and choice-maker are looking for a benchmark tool to compare and evaluate their ongoing algorithms or technologies. In this while, a novel benchmark platform is desired. PHABS provides an open framework to let its users define their own test strategy, perform tests, collect and analyze test data. With PHABS, various performances of algorithms can be tested, and different algorithms or algorithms with different parameters can be evaluated and compared easily.
Acquisition and automated 3-D segmentation of respiratory/cardiac-gated PET transmission images

To evaluate the impact of respiratory motion on attenuation correction of cardiac PET data, we acquired and automatically segmented gated transmission data for a dog breathing on its own under gas anesthesia. Data were acquired for 20 min on a CTI/Siemens ECAT EXACT HR (47-slice) scanner configured for 12 gates in a static study. Two respiratory gates were obtained using data from a pneumatic bellows placed around the dog's chest, in conjunction with 6 cardiac gates from standard EKG gating. Both signals were directed to a LabVIEW-controlled Macintosh, which translated them into one of 12 gate addresses. The respiratory gating threshold was placed near end-expiration to acquire 6 cardiac-gated datasets at end-expiration and 6 cardiac-gated datasets during breaths. Breaths occurred about once every 10 sec and lasted about 1-1.5 sec. For each respiratory gate, data were summed over cardiac gates and torso and lung surfaces were segmented automatically using a differential 3-D edge detection algorithm. Three-dimensional visualizations showed that lung surfaces adjacent to the heart translated 9 mm inferiorly during breaths. Our results suggest that respiration-compensated attenuation correction is feasible with a modest amount of gated transmission data and is necessary for accurate quantitation of high-resolution gated cardiac PET data.
Exploring Client-Side Instrumentation for Personalized Search Intent Inference : Preliminary Experiments

Clickthrough on search results have been successfully used to infer user interest and preferences, but are often noisy and potentially ambiguous. The reason mainly lies in that the clickthrough features are inherently a representation of the majority of user intents, rather than the information needs of the individual users for a given query instance. In this paper, we explore how to recover personalized search intent for each search instance, using a more sensitive and rich client-side instrumentation (including mouse movements) to provide additional insights into the intent behind each query instance. We report preliminary results of learning to infer query intent over rich instrumentation of search result pages. In particular, we explore whether we can automatically distinguish the different query classes such as navigational vs. informational queries. Our preliminary results confirm our intuition that client-side instrumentation is superior for personalized user intent inference, and suggest interesting avenues for future exploration.
A Simple Intrusion-Tolerant Reliable Multicast Protocol using the TTCB

This paper proposes a simple reliable multicast protocol that tolerates arbitrary faults, including malicious faults such as intrusions. The goal is to show a novel way of designing intrusion-tolerant protocols based on a well- founded hybrid fault model. This model is based on a simple distributed security kernel - the TTCB - which is used by the processes only to execute securely critical steps of the protocol. Otherwise, the processes and their communication can be attacked in unlimited ways. The TTCB provides only a few basic services, which allow our protocol to tolerate a number of faults similar to accidental fault-tolerant protocols: forf faults, our protocol requiresf + 2 processes, instead of 3f + 1 in typical intrusion-tolerant (or Byzantine) protocols. The protocol exhibits fast termination in the presence of intrusions and/or crash or malicious process failures, since it does not use any cryptography in runtime. Resumo. Este artigo prop˜
Survey on Clustering Algorithm for Document Clustering

— in today era all document are in electronic format to save space and easy access. So, the biggest task is to the retrieve document from large database. The Document clustering is the process of partitioning collected data into subgroups with similarities. The purpose of document clustering is to provide technique from which human interests in searching the information and understanding. The goal is to provide an approach for extracting unknown pattern from a lager database. Clustering algorithm are basically used for document clustering, in this way, the database is summarised and presented in unique manner.
RankRC: Large-Scale Nonlinear Rare Class Ranking

Rare class problems are common in real-world applications across a wide range of domains. Standard classification algorithms are known to perform poorly in these cases, since they focus on overall classification accuracy. In addition, we have seen a significant increase of data in recent years, resulting in many large scale rare class problems. In this paper, we focus on nonlinear kernel based classification methods expressed as a regularized loss minimization problem. We address the challenges associated with both rare class problems and large scale learning, by 1) optimizing area under curve of the receiver of operator characteristic in the training process, instead of classification accuracy and 2) using a rare class kernel representation to achieve an efficient time and space algorithm. We call the algorithm RankRC. We provide justifications for the rare class representation and experimentally illustrate the effectiveness of RankRC in test performance, computational complexity, and model robustness.
Analysis of signal transmission along graphene-based interconnect structures

The signal transmission along interconnect based on graphene nanoribbon (GNR) is studied in this paper. Equivalent circuit models are implemented to characterize graphene-based interconnect and analyze its signal integrity. In equivalent circuits models, the inter-layer mutual impedance (capacitance and inductance) between adjacent layers has been taken into consideration. Meanwhile, we proposed both equivalent circuit and full-wave method to analyze the effect of bended part of graphene-based interconnect. With the help of equivalent circuits, input/output voltage of different interconnect structures is explored. Moreover, eye diagram is utilized for further study of several kinds of graphene interconnects' signal integrity.
Development of a targeted method for quantification of gypenoside XLIX in rat plasma, using SPE and LC-MS/MS.

A sensitive, selective and rapid liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed for the quantification of gypenoside XLIX, a naturally occurring gypenoside of Gynostemma pentaphyllum in rat plasma and then validated according to the US Food and Drug Administration's Guidance for Industry: Bioanalytical Method Validation. Plasma samples were prepared by a simple solid-phase extraction. Separation was performed on a Waters XBridgeTM BEH C18 chromatography column (4.6 × 50 mm, 2.5 μm) using a mobile phase of acetonitrile and water (62.5:37.5, v/v). Gypenoside XLIX and the internal standard gypenoside A were detected in the negative ion mode using selection reaction monitoring of the transitions at m/z 1045.6 → 913.5 and 897.5 → 765.4, respectively. The calibration curve was linear (R2 > 0.990) over a concentration range of 10-7500 ng/mL with the lower quantification limit of 10 ng/mL. Intra- and inter-day precision was within 8.6% and accuracy was ≤10.2%. Stability results proved that gypenoside XLIX and the IS remained stable throughout the analytical procedure. The validated LC-MS/MS method was then applied to analyze the pharmacokinetics of gypenoside XLIX after intravenous administration to rats (1.0, 2.0 and 4.0 mg/kg).
Measuring process capital from a system model perspective

Purpose – The purpose of this paper is to seek effective measurement methods that reflect the real value of process capital.Design/methodology/approach – From a system model perspective, the authors refined the existing knowledge of process measurement by distinguishing three kinds of indicator for the value of process capital: input, output, and the capability to manage process capital. The design of this study, therefore, incorporates a longitudinal analysis of the content of process capital and traces its evolution by attaching a monetary value to activities and assets.Findings – The tested results reveal that the input measure is a less effective measure for process capital, while the output measure is a valid one for measuring operational and managerial performance of process capital. The capability to manage process capital can predict all dimensions of process capital in both the short‐ and long‐term periods.Practical implications – A practical view of process capital enhances the current understan.