Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and simulating the dynamics of service agent networks

The dynamic interactions within a social network can be modeled with respect to service transactions. In this paper, we develop a framework for modeling a service agent network in an attempt to study the emergent dynamic behavior (e.g., performance and formation) of the network. In the framework, a service transaction process is introduced that is composed of such steps as service matching, service evaluation, service propagation, and agent profile evolution. Through the transaction process model, the dynamic performance and formation of the agent network can readily be simulated. Our simulation results show that a network of service agents that select their cooperative partners based on high cumulative profits can yield high service throughputs.
Modeling Routers under Internet Traffic ( Extended Version )

In this paper, we study the interactions of user-based congestion control algorithms and router-based switch scheduling algorithms. We show that switch scheduling algorithms that were designed without taking into account these interactions can exhibit a completely different behavior when interacting with feedback-based Internet traffic. Previous papers neglected or mitigated these interactions, and typically found that flow rates reach a fair equilibrium. On the contrary, we show that these interactions can lead to extreme unfairness with temporary flow starvation, as well as to large rate oscillations. For instance, we prove that this is the case for the MWM switch scheduling algorithm, even with a single router output and basic TCP flows. We also show that the iSLIP switch scheduling algorithm achieves fairness among ports, instead of fairness among flows. Finally, we fully characterize the network dynamics for both these switch scheduling algorithms.
Network Emulation with NetEm

Many protocols and applications perform poorly when exposed to real life networks with delay and packet loss. Often, it is costly and di(cid:14)cult to reproduce Internet behavior in a controlled environment. There are tools available for testing, but they are either expensive hardware solutions, proprietary software, or limited research projects. NetEm is a recent enhancement of the traf-(cid:12)c control facilities of Linux that allows adding delay, packet loss and other scenario’s. Documentation and discussion of NetEm is maintained at http://developer.osdl. org/shemminger/ netem . NetEm is built using the existing Quality Of Service (QOS) and Di(cid:11)erentiated Services (di(cid:11)serv) facilities in the Linux kernel
An Approach to Parameterized Resiliency Problems Using Fourier-Motzkin Elimination for Integer Linear Programming

We present a new technique in parameterized algorithms whose purpose is to solve "resiliency versions" of decision problems. In resiliency problems, the goal is to decide whether an instance remains positive after any (appropriately defined) perturbation applied to it. To tackle this kind of problems, which might be of important practical interest, we introduce a notion of resiliency for Integer Linear Programs (ILP). While a celebrated result from Lenstra (Mathematics of Operations Research, 1983) implies that testing ILP feasibility is Fixed-Parameter Tractable (FPT) parameterized by the number of variables (i.e. runs in time O*(f(n)), where n is the number of variables, f is some computable function, and O*(.) suppresses polynomial terms), this tool seems too weak to solve our resiliency version in an efficient way. We first give evidence that testing resiliency of an ILP is computationally harder than simply testing its feasibility, by proving that our problem is $\Pi_2^P$ -complete (i.e. belongs to the second level of the polynomial hierarchy). We then provide an analog of Lenstra's theorem but using a larger parameter. Our result uses an adaptation of the Fourier-Motzkin elimination for Linear Programming to the integral case introduced by Williams (J. Comb. Theory, Ser. A, 1976), which might be of independent interest. We believe that our method provides a generic framework for proving that resiliency versions of many parameterized problems remain FPT. To illustrate this fact, we present two applications of our approach to concrete problems: the Resiliency Checking Problem, which can be seen as a generalization of the classical Set Cover problem, and Closest String, a problem arising in computational biology.
Modeling and analysis of content caching in wireless small cell networks

Network densification with small cell base stations is a promising solution to satisfy future data traffic demands. However, increasing small cell base station density alone does not ensure better users' quality-of-experience and incurs high operational expenditures. Therefore, content caching on different network elements has been proposed as a mean of offloading the backhaul by caching strategic contents at the network edge, thereby reducing latency. In this paper, we investigate cache-enabled small cells in which we model and characterize the outage probability, defined as the probability of not satisfying users' requests over a given coverage area. We analytically derive a closed form expression of the outage probability as a function of signal-to-interference ratio, cache size, small cell base station density and threshold distance. By assuming the distribution of base stations as a Poisson point process, we derive the probability of finding a specific content within a threshold distance and the optimal small cell base station density that achieves a given target cache hit probability. Furthermore, simulation results are performed to validate the analytical model.
Expression and Function of Serotonin 2A and 2B Receptors in the Mammalian Respiratory Network

Neurons of the respiratory network in the lower brainstem express a variety of serotonin receptors (5-HTRs) that act primarily through adenylyl cyclase. However, there is one receptor family including 5-HT2A, 5-HT2B, and 5-HT2C receptors that are directed towards protein kinase C (PKC). In contrast to 5-HT2ARs, expression and function of 5-HT2BRs within the respiratory network are still unclear. 5-HT2BR utilizes a Gq-mediated signaling cascade involving calcium and leading to activation of phospholipase C and IP3/DAG pathways. Based on previous studies, this signal pathway appears to mediate excitatory actions on respiration. In the present study, we analyzed receptor expression in pontine and medullary regions of the respiratory network both at the transcriptional and translational level using quantitative RT-PCR and self-made as well as commercially available antibodies, respectively. In addition we measured effects of selective agonists and antagonists for 5-HT2ARs and 5-HT2BRs given intra-arterially on phrenic nerve discharges in juvenile rats using the perfused brainstem preparation. The drugs caused significant changes in discharge activity. Co-administration of both agonists revealed a dominance of the 5-HT2BR. Given the nature of the signaling pathways, we investigated whether intracellular calcium may explain effects observed in the respiratory network. Taken together, the results of this study suggest a significant role of both receptors in respiratory network modulation.
Fruit Detection using Improved Multiple Features based Algorithm

icient locating the fruit on the tree is one of the major requirements for the fruit harvesting system. This paper presents the fruit detection using improved multiple features based algorithm. To detect the fruit, an image processing algorithm is trained for efficient feature extraction. The algorithm is designed with the aim of calculating different weights for features like intensity, color, orientation and edge of the input test image. The weights of different features represent the approximate locations of the fruit within an image. The Detection Efficiency is achieved up to 90% for different fruit image on tree, captured at different positions. The input images are the section of tree image. The proposed approach can be applied for targeting fruits for robotic fruit harvesting.
Frequency spectrum of the intracardiac and body surface ECG during ventricular fibrillation - a computer model study

Recent findings indicate that stable organized centers of rapid activity (mother rotors) are the maintaining mechanism of ventricular fibrillation (VF). A computer model study was performed for understanding how intracardiac cellular activity is reflected at the body surface. The model contained a driving region of fast periodic activity (22 Hz). On the intracardiac layer the dominant frequency (DF) map revealed a constant DF of 22 Hz in the region of the mother rotor. A sharp drop of frequency (2 Hz - 12 Hz) occurred in the surrounding tissue of chaotic fibrillatory conduction. No organized pattern was observable on the body surface and the DF was reduced to 4.6 Hz -8.5 Hz. It was shown that wave propagation transforms the spatial low pass filtering of the thorax into a temporal low pass in the far field. It hampers the observation of intracardiac organization from the body surface. No RC low pass was included in the model.
Sensorization of continuum soft robots for reconstructing their spatial configuration

This work deals with the sensorization of continuum soft robots for reconstructing their spatial configuration. Since at the present time the proprioceptive perception is prevalently achieved using external 3D optical methods, an embedded system is needed for the improvement of the performances of this promising kind of robots. The idea is to use stretch sensors to reconstruct the spatial configuration of a robotic structure: differential measurements on strain values can be used to derive local curvatures performed by the arm. Strain values have been extracted using a conductive textile that presents very high elasticity and lightness. The main feature is that its electrical resistance depends on its mechanical strain and it has been characterized in order to derive this strain-resistance relation. The reconstruction method that allows to obtain spatial configuration from material deformations is then reported. A practical application on reconstructing the configuration of an octopus-inspired robot arm is finally shown and discussed.
Joint Learning for Coreference Resolution with Markov Logic

Pairwise coreference resolution models must merge pairwise coreference decisions to generate final outputs. Traditional merging methods adopt different strategies such as the best-first method and enforcing the transitivity constraint, but most of these methods are used independently of the pairwise learning methods as an isolated inference procedure at the end. We propose a joint learning model which combines pairwise classification and mention clustering with Markov logic. Experimental results show that our joint learning system outperforms independent learning systems. Our system gives a better performance than all the learning-based systems from the CoNLL-2011 shared task on the same dataset. Compared with the best system from CoNLL-2011, which employs a rule-based method, our system shows competitive performance.
Influence activity and the organization of research and development

The organizational design of research and development conditions the incentives of the researchers of the research project. In particular, the organizational form determines the allocation of effort of the researcher between time spent on research and time spent lobbying m anagement. Researchers prefer to spend their time on research. However, the researchers only get utility from performing research if the project is approved for its full duration. Spending time lobbying management for the continuation of the researcher’s project increases the probability that the management observes a favorable signal about the project. Organizing a research joint venture increases the flexibility of the organizational form with respect to the continuation decision. For low correlation between the signals of the partners about the expected profitability of the project, we find that the organization of a research joint venture reduces influence activity by the researchers and increases expected profits of the partners. For high correlation between the signals, internal research projects lower influence activity by the researchers. We try to relate the correlation of the partners signals to the characteristics of basic research versus more applied research projects, and find that the model is consistent with the observation that research joint ventures seem involved in more basic research projects compared to internal R&D departments, which concentrate on more applied research.
A tool for RNA sequencing sample identity check

SUMMARY RNA sequencing data are becoming a major method of choice to study transcriptomes, including the mapping of gene expression quantitative trait loci (eQTLs). RNA sample contamination or swapping is a serious problem for downstream analysis and may result in false discovery and lose power to detect the true biological relationships. When genetic data are available, for example, in eQTL studies or samples have been previously genotyped or DNA sequenced, it is possible to combine genetic data and RNA-seq data to detect sample contamination and resolve sample swapping problems. In this article, we introduce a tool (IDCheck) that allows easy assessment of concordance between genotype (from SNP arrays or DNA sequencing) and gene expression (RNA-seq) samples. IDCheck compares the identity of RNA-seq reads and SNP genotypes using a likelihood-based method. Based on maximum likelihood estimates of relevant parameters, we can detect sample contamination and identify correct sample pairs when swapping occurs. Our tool provides an efficient and convenient way to evaluate and resolve these problems. AVAILABILITY A complete description of the software is included on the application home page. The software is freely available in the public domain at http://eqtl.rc. fas.harvard. edu/idcheck/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Measures for Corpus Similarity and Homogeneity

How similar are two corpora? A measure of corpus similarity would be very useful for NLP for many purposes, such as estimating the work involved in porting a system from one domain to another. First, we discuss difculties in identifying what we mean by ‘corpus similarity’: human similarity judgements are not negrained enough, corpus similarity is inherently multidimensional, and similarity can only be interpreted in the light of corpus homogeneity. We then present an operational denition of corpus similarity which addresses or circumvents the problems, using purposebuilt sets of iknown-similarity corporai. These KSC sets can be used to evaluate the measures. We evaluate the measures described in the literature, including three variants of the information theoretic measure ‘perplexity’. A -based measure, using word frequencies, is shown to be the best of those tested.
Pharmacokinetic–Pharmacodynamic Modeling of Enrofloxacin Against Escherichia coli in Broilers

The purpose of the present study was to establish a pharmacokinetic/pharmacodynamic (PK/PD) modeling approach for the dosage schedule design and decreasing the emergence of drug-resistant bacteria. The minimal inhibitory concentration (MIC) of 929 Escherichia coli isolates from broilers to enrofloxacin and ciprofloxacin was determined following CLSI guidance. The MIC50 was calculated as the populational PD parameter for enrofloxacin against E. coli in broilers. The 101 E. coli strains with MIC closest to the MIC50 (0.05 μg/mL) were submitted for serotype identification. The 13 E. coli strains with O and K serotype were further utilized for determining pathogencity in mice. Of all the strains tested, the E. coli designated strain Anhui 112 was selected for establishing the disease model and PK/PD study. The PKs of enrofloxacin after oral administration at the dose of 10 mg/kg body weights (BW) in healthy and infected broilers was evaluated with high-performance liquid chromatography (HPLC) method. For intestinal contents after oral administration, the peak concentration (Cmax), the time when the maximum concentration reached (Tmax), and the area under the concentration-time curve (AUC) were 21.69–31.69 μg/mL, 1.13–1.23 h, and 228.97–444.86 μg h/mL, respectively. The MIC and minimal bactericidal concentration (MBC) of enrofloxacin against E. coli (Anhui 112) in Mueller–Hinton (MH) broth and intestinal contents were determined to be similar, 0.25 and 0.5 μg/mL respectively. In this study, the sum of concentrations of enrofloxacin and its metabolite (ciprofloxacin) was used for the PK/PD integration and modeling. The ex vivo growth inhibition data were fitted to the sigmoid Emax (Hill) equation to provide values for intestinal contents of 24 h area under concentration-time curve/MIC ratios (AUC0–24 h/MIC) producing, bacteriostasis (624.94 h), bactericidal activity (1065.93 h) and bacterial eradication (1343.81 h). PK/PD modeling was established to simulate the efficacy of enrofloxacin for different dosage regimens. By model validation, the protection rate was 83.3%, demonstrating that the dosage regimen of 11.9 mg/kg BW every 24 h during 3 days provided great therapeutic significance. In summary, the purpose of the present study was to first design a dosage regimen for the treatment E. coli in broilers by enrofloxacin using PK/PD integrate model and confirm that this dosage regimen presents less risk for emergence of floroquinolone resistance.
Recognition of gait impairment evaluated using an artificial gait stimuli

This paper describes experiments to understand how well individuals can recognize an impaired walking pattern. The gait patterns are generated using a passive dynamic walker (PDW) model to allow a systematic change in gait patterns. The changed gait parameters include gait cadence, knee height asymmetry, step length and step time asymmetry, roll-over-shape (ROS) asymmetry, and knee damping asymmetry. Twenty participants rated twenty-four unimpaired and impaired walking patterns on a 7-point Likert scale. Results revealed that although a walking pattern may deviate dynamically from normal, there is a quantifiable range of gait impairments that may be dismissed as unimpaired. Particularly, we found that a knee height asymmetry range of roughly +10% (up) and -20%, could be dismissed as unimpaired. Findings show that the impairment perception of a damped knee joint can be countered by attaching a mass to the opposite lower limb.
Improving classifiers with unlabeled weakly-related videos

Current state-of-the-art object classification systems are trained using large amounts of hand-labeled images. In this paper, we present an approach that shows how to use unlabeled video sequences, comprising weakly-related object categories towards the target class, to learn better classifiers for tracking and detection. The underlying idea is to exploit the space-time consistency of moving objects to learn classifiers that are robust to local transformations. In particular, we use dense optical flow to find moving objects in videos in order to train part-based random forests that are insensitive to natural transformations. Our method, which is called Video Forests, can be used in two settings: first, labeled training data can be regularized to force the trained classifier to generalize better towards small local transformations. Second, as part of a tracking-by-detection approach, it can be used to train a general codebook solely on pair-wise data that can then be applied to tracking of instances of a priori unknown object categories. In the experimental part, we show on benchmark datasets for both tracking and detection that incorporating unlabeled videos into the learning of visual classifiers leads to improved results.
Supporting Dynamic GPU Computing Result Reuse in the Cloud

Graphics processing units (GPUs) have been adopted by major cloud vendors, as GPUs provide orders-of-magnitude speedup for computation-intensive data-parallel applications. In the cloud, efficiently sharing GPU resources among multiple virtual machines (VMs) is not so straightforward. Recent research has been conducted to develop GPU virtualization technologies, making it feasible for VMs to share GPU resources in a reliable manner. This paper seeks to improve the efficiency of sharing GPU resources in the cloud for accelerating general-purpose workloads. Our key observation is that redundant GPU computation requests are being seen in many GPU-accelerated workloads in the cloud, such as cloud gaming where multiple clients playing the same game call GPUs to perform physics simulation. We have measured this redundancy using a gaming case study, and found that more than 24% (47%) of the GPU computation requests called by the same VM (multiple VMs) are identical. To exploit this redundancy, we present GRU (GPU Result re-Use), a GPU sharing, result memoization and reuse ecosystem in a cloud environment. GRU transparently enables VMs in the cloud to share a single GPU efficiently, and memoizes GPU computation results for reuse. It leverages the GPU full-virtualization technology, which enables GPU result memoization and reuse without modification of existing device drivers and operating systems. We have implemented GRU on top of the Xen hypervisor. Preliminary experiments show that GRU is able to achieve a significant speedup of up to 18 times compared to the state-of-the-art GPU virtualization framework, while adding a rather small amount of run-time overheads.
Identification of prognostic biomarkers for glioblastomas using protein expression profiling

A set of proteins reflecting the prognosis of patients have clinical significance since they could be utilized as predictive biomarkers and/or potential therapeutic targets. With the aim of finding novel diagnostic and prognostic markers for glioblastoma (GBM), a tissue microarray (TMA) library consisting of 62 GBMs and 28 GBM-associated normal spots was constructed. Immunohistochemistry against 78 GBM-associated proteins was performed. Expression levels of each protein for each patient were analyzed using an image analysis program and converted to H-score [summation of the intensity grade of staining (0–3) multiplied by the percentage of positive cells corresponding to each grade]. Based on H-score and hierarchical clustering methods, we divided the GBMs into two groups (n=19 and 37) that had significantly different survival lengths (p<0.05). In the two groups, expression of nine proteins (survivin, cyclin E, DCC, TGF-β, CDC25B, histone H1, p-EGFR, p-VEGFR2/3, p16) was significantly changed (q<0.05). Prognosis-predicting potential of these proteins were validated with another independent library of 82 GBM TMAs and a public GBM DNA microarray dataset. In addition, we determined 32 aberrant or mislocalized subcellular protein expression patterns in GBMs compared with relatively normal brain tissues, which could be useful for diagnostic biomarkers of GBM. We therefore suggest that these proteins can be used as predictive biomarkers and/or potential therapeutic targets for GBM.
Improving deep neural networks by using sparse dropout strategy

Recently, deep neural networks(DNNs) have achieved excellent results on benchmarks for acoustic modeling of speech recognition. By randomly discarding network units, a strategy which is called as dropout can improve the performance of DNNs by reducing the influence of over-fitting. However, the random dropout strategy treats units indiscriminately, which may lose information on distributions of units outputs. In this paper, we improve the dropout strategy by differential treatment to units according to their outputs. Only minor changes to an existing neural network system can achieve a significant improvement. Experiments of phone recognition on TIMIT show that the sparse dropout fine-tuning gets significant performance improvement.