Miracle-dz/UniFAR-FTUs huggingface dataset:
Compiling Path Queries

Measuring the flow of traffic along network paths is crucial for many management tasks, including traffic engineering, diagnosing congestion, and mitigating DDoS attacks. We introduce a declarative query language for efficient path-based traffic monitoring. Path queries are specified as regular expressions over predicates on packet locations and header values, with SQL-like "groupby" constructs for aggregating results anywhere along a path. A run-time system compiles queries into a deterministic finite automaton. The automaton's transition function is then partitioned, compiled into match-action rules, and distributed over the switches. Switches stamp packets with automaton states to track the progress towards fulfilling a query. Only when packets satisfy a query are the packets counted, sampled, or sent to collectors for further analysis. By processing queries in the data plane, users "pay as they go", as data-collection overhead is limited to exactly those packets that satisfy the query. We implemented our system on top of the Pyretic SDN controller and evaluated its performance on a campus topology. Our experiments indicate that the system can enable "interactive debugging"-- compiling multiple queries in a few seconds--while fitting rules comfortably in modern switch TCAMs and the automaton state into two bytes (e.g., a VLAN header).
TRPM4 enhances cell proliferation through up‐regulation of the β‐catenin signaling pathway

Altered expression of some members of the TRP ion channel superfamily has been associated with the development of pathologies like cancer. In particular, TRPM4 levels are reportedly elevated in diffuse large B‐cell non‐Hodgkin lymphoma, prostate, and cervical cancer. However, whether such changes in TRPM4 expression may be relevant to genesis or progression of cancer remains unknown. Here we show that reducing TRPM4 expression decreases proliferation of HeLa cells, a cervical cancer‐derived cell line. In this cell line, constitutive TRPM4 silencing promoted GSK‐3β‐dependent degradation of β‐catenin and reduced β‐catenin/Tcf/Lef‐dependent transcription. Conversely, overexpression of TRPM4 in T‐REx 293 cells (a HEK293‐derived cell line) increased cell proliferation and β‐catenin levels. Our results identify TRPM4 as an important, unanticipated regulator of the β‐catenin pathway, where aberrant signaling is frequently associated with cancer. J. Cell. Physiol. 226: 103–109, 2010. © 2010 Wiley‐Liss, Inc.
Is operator-based mutant selection superior to random mutant selection?

Due to the expensiveness of compiling and executing a large number of mutants, it is usually necessary to select a subset of mutants to substitute the whole set of generated mutants in mutation testing and analysis. Most existing research on mutant selection focused on operator-based mutant selection, i.e., determining a set of sufficient mutation operators and selecting mutants generated with only this set of mutation operators. Recently, researchers began to leverage statistical analysis to determine sufficient mutation operators using execution information of mutants. However, whether mutants selected with these sophisticated techniques are superior to randomly selected mutants remains an open question. In this paper, we empirically investigate this open question by comparing three representative operator-based mutant-selection techniques with two random techniques. Our empirical results show that operator-based mutant selection is not superior to random mutant selection. These results also indicate that random mutant selection can be a better choice and mutant selection on the basis of individual mutants is worthy of further investigation.
Energy-Efficient Hybrid Precoding for Millimeter Wave Systems in MIMO Interference Channels

In this paper, we investigate energy-efficient hybrid precoding which consists of baseband precoding and radio frequency (RF) precoding for Millimeter Wave (mmWave) systems over multi-input multi-output (MIMO) interference channels. The considered optimization problem is intractable because both the object function and the constraint are non-convex. Instead of solving the multivariate optimization problem directly, we propose a near-optimal algorithm which includes two steps. First, the original problem is reformulated into an equivalent univariate optimization problem. Second, a near-optimal hybrid precoder is obtained via an orthogonal matching pursuit (OMP) based algorithm. Numerical results verify that the proposed hybrid precoding algorithm achieves a near-optimal EE performance. Moreover, the energy efficiency (EE) maximization algorithm outperforms the sum rate maximization algorithm in terms of the EE performance, especially at high transmit power.
Differences between liking and wanting signals in the human brain and relations with cognitive dietary restraint and body mass index.

BACKGROUND Eating behavior is determined, to a significant extent, by the rewarding value of food (ie, liking and wanting). OBJECTIVE We determined brain regions involved in liking and wanting and related brain signaling to body mass index (BMI; in kg/m(2)) and dietary restraint. DESIGN Fifteen normal-weight female subjects [mean ± SEM age: 21.5 ± 0.4 y; BMI: 22.2 ± 0.2] completed a food-choice paradigm by using visually displayed food items during functional magnetic resonance imaging scans. Two scans were made as follows: one scan was made in a fasted condition, and one scan was made in a satiated condition. The paradigm discriminated between liking and wanting, and subjects were offered items rated highly for wanting immediately after each scan. Imaging contrasts for high and low liking and wanting were made, and data for regions of interest were extracted. Activation related to liking and wanting, respectively, was determined. Outcomes were correlated to cognitive dietary restraint and BMI. RESULTS Dietary restraint predicted liking task-related signaling (TRS) in the amygdala, striatum, thalamus, and cingulate cortex (r = -0.5 ± 0.03, P < 0.00001). In the nucleus accumbens, the premeal liking and wanting TRS and premeal to postmeal liking TRS changes correlated positively with dietary restraint [bilateral average r = 0.6 ± 0.02, P < 0.04 (Bonferroni corrected)]. BMI and hunger predicted wanting TRS in the hypothalamus and striatum (P < 0.05). Postmeal liking TRS in the striatum, anterior insula, and cingulate cortex and wanting TRS in the striatum predicted the energy intake (liking: r = -0.3 ± 0.05, P < 0.0001; wanting: r = -0.3 ± 0.03, P < 0.00001). CONCLUSIONS Successful dietary restraint was supported by liking TRS from premeal to postmeal in the nucleus accumbens. Reward-related signaling was inversely related to BMI and energy intake, indicating reward deficiency.
Linear and Nonlinear Projective Nonnegative Matrix Factorization

A variant of nonnegative matrix factorization (NMF) which was proposed earlier is analyzed here. It is called projective nonnegative matrix factorization (PNMF). The new method approximately factorizes a projection matrix, minimizing the reconstruction error, into a positive low-rank matrix and its transpose. The dissimilarity between the original data matrix and its approximation can be measured by the Frobenius matrix norm or the modified Kullback-Leibler divergence. Both measures are minimized by multiplicative update rules, whose convergence is proven for the first time. Enforcing orthonormality to the basic objective is shown to lead to an even more efficient update rule, which is also readily extended to nonlinear cases. The formulation of the PNMF objective is shown to be connected to a variety of existing NMF methods and clustering approaches. In addition, the derivation using Lagrangian multipliers reveals the relation between reconstruction and sparseness. For kernel principal component analysis (PCA) with the binary constraint, useful in graph partitioning problems, the nonlinear kernel PNMF provides a good approximation which outperforms an existing discretization approach. Empirical study on three real-world databases shows that PNMF can achieve the best or close to the best in clustering. The proposed algorithm runs more efficiently than the compared NMF methods, especially for high-dimensional data. Moreover, contrary to the basic NMF, the trained projection matrix can be readily used for newly coming samples and demonstrates good generalization.
A Learning Algorithm For Neural Network Ensembles

Abstract The performance of a single regressor/classifier can be improved by combining the outputs of several predictors. This is true provided the combined predictors are accurate and diverse enough, which posses the problem of generating suitable aggregate members in order to have optimal generalization capabiliti es. We propose here a new method for selecting members of regression/classification ensembles. In particular, using artificial neural networks as learners in a regression context, we show that this method leads to small aggregates with few but very diverse individual networks. The algorithm is favorably tested against other methods recently proposed in the literature, producing equal performance on the standard statistical databases used as benchmarks with ensembles that have 75% less members on average. Keywords: Neural Networks, Ensemble Learning, Regression, Bias/Variance Decomposition. 1. Introduction Ensemble techniques have been used recently in regression/classification tasks with considerable success. The motivation for this procedure is based on the intuitive idea that by combining the outputs of several individual predictors one might improve on the performance of a single generic one [1]. However, this idea has been proved to be true only when the combined predictors are simultaneously accurate and diverse enough, which requires an adequate trade-off between these two conflicting conditions. Some attempts [2,3] to achieve a good compromise between these properties include elaborations of bagging[4], boosting[5] and stacking[6] techniques. Artificial neural networks (ANN) provide a natural framework for ensemble techniques. This is so because ANN are a very unstable learning method, i.e. small changes in training set and/or parameter selection can produce large changes in prediction outputs. This diversity of ANN comes naturally from the inherent data and training process randomness, and also from the intrinsic non-identifiability of the model (many different but a priori equally good local minima of the error surface). On the other hand, the above mentioned trade-off between the ensemble diversity and the accuracy of the individual members posses the problem of generating a set of ANN with both reasonably good (individual) generalization capabilities and distributed predictions for the test points. This problem has been considered in several recent works in the literature [2,3,7-9]. We provide here an alternative way of generating an ANN ensemble with members that are both accurate and diverse. The method essentially amounts to the sequential aggregation of individual predictors where, unlike in standard aggregation techniques which combine individually optimized ANN [7], the learning process of a (potential) new member is validated by the overall aggregate prediction performance. That is, an ANN is incorporated to the
Logical Framing of Query Interface to refine Divulging Deep Web Data

This work is an extension to research works, being carried out entitled “Study of Information Retrieval Performance Refinement in Deep Web Mining”. It is an effort to study overcoming the limitations of the existing Deep Web Information Retrieval Systems prevailing lack of logics, in my belief, regarding the retrieval process. Addressing this problem from the information providing services perspective indicates the significant potential of additional Logical Framings provided by websites. Web query interfaces, the interfaces to the majority of available information on the Deep Web, are interpreted as Semantic Deep Web Services [1]. Our introduction of a Logical Deep Web Data Retrieval Services (LDWDRS) framing carries to great potential for Information Retrieval services is based on the large variety of information available on the Deep Web. In this paper we report some of our analysis in framing our system and outline the main challenges we see in the further exploration and divulging deep-web content.
Lossless and lossy coding for multispectral image based on sRGB standard and residual components

In this paper, we present a multispectral image (MSI) compression method using a lossless and lossy coding scheme, which focuses on the seamless coding of the RGB bit stream to enhance the usability of the MSI. The proposed method divides the MSI data into two components: RGB and residual. The RGB component is extracted from the MSI by using the XYZ color matching functions, a color conversion matrix, and a gamma curve. The original MSI is estimated by an RGB data encoder and the difference between the original and the estimated MSI, which is referred to as the residual component in this paper. Next, the RGB and residual components are encoded by using JPEG2000, and progressive decoding is achieved from the losslessly encoded code stream. Experimental results show that a high-quality RGB image can be obtained at a low bit rate with primary encoding of the RGB component. In addition, by using the proposed method, the quality of a spectrum can be improved by decoding the residual data, and the quality is comparable to that obtained by using JPEG2000. The lossless compression ratio obtained by using this method is also similar to that obtained by using JPEG2000 with the integer Karhunen-Loeve transform.
A pivotal role of GSK-3 in synaptic plasticity

Glycogen synthase kinase-3 (GSK-3) has many cellular functions. Recent evidence suggests that it plays a key role in certain types of synaptic plasticity, in particular a form of long-term depression (LTD) that is induced by the synaptic activation of N-methyl-D-aspartate receptors (NMDARs). In the present article we summarize what is currently known concerning the roles of GSK-3 in synaptic plasticity at both glutamatergic and GABAergic synapses. We summarize its role in cognition and speculate on how alterations in the synaptic functioning of GSK-3 may be a major factor in certain neurodegenerative disorders.
C1q Tumor Necrosis Factor α-related Protein Isoform 5 Is Increased in Mitochondrial DNA-depleted Myocytes and Activates AMP-activated Protein Kinase*

Depletion of mtDNA in myocytes causes insulin resistance and alters nuclear gene expression that may be involved in rescuing processes against cellular stress. Here we show that the expression of C1q tumor necrosis factor α-related protein isoform 5 (C1QTNF5) is drastically increased following depletion of mtDNA in myocytes. C1QTNF5 is homologous to adiponectin in respect to domain structure, and its expression and secretion from myocytes correlated negatively with the cellular mtDNA content. Similar to adiponectin, C1QTNF5 induced the phosphorylation of AMP-activated protein kinase (AMPK), leading to increased cell surface recruitment of GLUT4 and increased glucose uptake. Treatment of cells with purified recombinant C1QTNF5 increased the phosphorylation of acetyl-CoA carboxylase and stimulated fatty acid oxidation. C1QTNF5-mediated phosphorylation of AMPK or acetyl-CoA carboxylase was unaffected by depletion of adiponectin receptors such as AdipoR1 or AdipoR2, which indicated that adiponectin receptors do not participate in C1QTNF5-induced activation of AMPK. Serum C1QTNF5 levels were significantly higher in obese/diabetic animals (OLETF rats, ob/ob mice, and db/db mice). These results highlight C1QTNF5 as a putative biomarker for mitochondrial dysfunction and a potent activator of AMPK.
The Brisbane Longitudinal Twin Study: Pathways to Cannabis Use, Abuse, and Dependence Project—Current Status, Preliminary Results, and Future Directions

We describe the data being collected from the Brisbane Longitudinal Twin Study in Australia as part of the US National Institute on Drug Abuse (NIDA)-funded project, Pathways to Cannabis Use, Abuse and Dependence. The history, recruitment, assessment, and retention of twin families in this project are described in detail, along with preliminary findings and plans for future research. The goal of this NIDA project is to make a significant contribution to the discovery of quantitative trait loci influencing cannabis use disorders. Although the focus is cannabis use, abuse, and dependence in young adults, measures of comorbid illicit drug use disorders are also being collected. In addition, a variety of internalizing and externalizing disorders are being assessed, funded by support from the Australian National Health and Medical Research Council. Because these same twins have participated in numerous twin studies since 1992, future plans will include linking different phenotypes to investigate relationships between drug use, psychiatric disorders, and psychological phenotypes within cross-sectional and longitudinal or developmental frameworks.
Webpage importance analysis using conditional Markov random walk

In this paper, we propose a novel method to calculate the Web page importance based on a conditional Markov random walk model. The main assumption in this model is that given the hyperlinks in a Web page, users are not really randomly clicking one of them. Instead, many factors may bias their behaviors, for example, the anchor text, the content relevance and the previous experiences when visiting the Web site that a destination page belongs to. As one of the results, the user might tend to visit those pages in high-quality Web sites with higher probability. To implement this idea, we reformulate the Web graph to be a two-layer structure, and the Web page importance is calculated by conditional random walk in this new Web graph. Experiments on the topic distillation task of TREC 2003 Web track showed that our new method can achieve about 18% improvement on mean average precision (MAP) and 16% on precision at 10 (P@10) over the PageRank algorithm.
Customer Recognition of Network Finance Based on MKL Algorithm

Given Bayes neural network method, the direct marketing customer repurchase behavior shall be built to analyze how different customer groups have different expectations on ATM services. Also, application of GMKL algorithm in customer recognition of network finance is proposed, training with the use of 20, 000 customer records extracted from RESSET/DB financial research database. First, transform the numerical characteristics of sample for collected customer record and normalize the data. Next, try to obtain multi-kernel function with different array modes of kernel function and compare the results, and the results show that accuracy of MKL in customer recognition of network finance can be up to 86. 4%, which is obvious higher than that of support vector machine.
3 Automatic Facial Feature Extraction for Face Recognition

Facial feature extraction consists in localizing the most characteristic face components (eyes, nose, mouth, etc.) within images that depict human faces. This step is essential for the initialization of many face processing techniques like face tracking, facial expression recognition or face recognition. Among these, face recognition is a lively research area where it has been made a great effort in the last years to design and compare different techniques. In this chapter we intend to present an automatic method for facial feature extraction that we use for the initialization of our face recognition technique. In our notion, to extract the facial components equals to locate certain characteristic points, e.g. the center and the corners of the eyes, the nose tip, etc. Particular emphasis will be given to the localization of the most representative facial features, namely the eyes, and the locations of the other features will be derived from them. An important aspect of any localization algorithm is its precision. The face recognition techniques (FRTs) presented in literature only occasionally face the issue and rarely state the assumptions they make on their initialization; many simply skip the feature extraction step, and assume perfect localization by relying upon manual annotations of the facial feature positions. However, it has been demonstrated that face recognition heavily suffers from an imprecise localization of the face components. This is the reason why it is fundamental to achieve an automatic, robust and precise extraction of the desired features prior to any further processing. In this respect, we investigate the behavior of two FRTs when initialized on the real output of the extraction method.
Performance evaluation on path diversity in radio links between unmanned aerial vehicles

This paper presents a performance evaluation on path diversity introduced in a wireless network established by unmanned aerial vehicles or unmanned aircrafts (UAs). One of usage scenarios of the wireless network established by UAs is establishment of a temporal communications instead of disrupted terrestrial networks due to a massive disaster. In order to evaluate system performance based on this usage scenario, we have conducted the following simulations; first, radio propagation characteristics between UAs flying over different two conditions have been simulated, after that, system simulations have been carried out over radio channels derived by simulations. Results regarding radio propagation reveal that characteristics including path loss and RMS delay are highly depend on not only altitude of the deployed UAs, but also its polarization of radio signal. System simulations based on the channel impulse responses obtained by propagation simulations, in which IEEE802.11g is utilized as specifications of the radio system, show that path diversity introduced by deploying multiple UAs brings remarkable improvement on achievable throughputs.
Alterations in Brain-Derived Neurotrophic Factor in the Mouse Hippocampus Following Acute but Not Repeated Benzodiazepine Treatment

Benzodiazepines (BZs) are safe drugs for treating anxiety, sleep, and seizure disorders, but their use also results in unwanted effects including memory impairment, abuse, and dependence. The present study aimed to reveal the molecular mechanisms that may contribute to the effects of BZs in the hippocampus (HIP), an area involved in drug-related plasticity, by investigating the regulation of immediate early genes following BZ administration. Previous studies have demonstrated that both brain derived neurotrophic factor (BDNF) and c-Fos contribute to memory- and abuse-related processes that occur within the HIP, and their expression is altered in response to BZ exposure. In the current study, mice received acute or repeated administration of BZs and HIP tissue was analyzed for alterations in BDNF and c-Fos expression. Although no significant changes in BDNF or c-Fos were observed in response to twice-daily intraperitoneal (i.p.) injections of diazepam (10 mg/kg + 5 mg/kg) or zolpidem (ZP; 2.5 mg/kg + 2.5 mg/kg), acute i.p. administration of both triazolam (0.03 mg/kg) and ZP (1.0 mg/kg) decreased BDNF protein levels within the HIP relative to vehicle, without any effect on c-Fos. ZP specifically reduced exon IV-containing BDNF transcripts with a concomitant increase in the association of methyl-CpG binding protein 2 (MeCP2) with BDNF promoter IV, suggesting that MeCP2 activity at this promoter may represent a ZP-specific mechanism for reducing BDNF expression. ZP also increased the association of phosphorylated cAMP response element binding protein (pCREB) with BDNF promoter I. Future work should examine the interaction between ZP and DNA as the cause for altered gene expression in the HIP, given that BZs can enter the nucleus and intercalate into DNA directly.