Miracle-dz/UniFAR-FTUs huggingface dataset:
Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed.
Adaptive Clustering for Multiple Evolving Streams

In the data stream environment, the patterns generated at different time instances are different due to data evolution. As time progresses, the behavior and members of clusters usually change. Hence, clustering continuous data streams allows us to observe the changes of group behavior. In order to support flexible clustering requirements, we devise in this paper a clustering on demand framework, abbreviated as COD framework, to dynamically cluster multiple data streams. While providing a general framework of clustering on multiple data streams, the COD framework has two advantageous features, namely, one data scan for online statistics collection and compact multiresolution approximations, which are designed to address, respectively, the time and the space constraints in a data stream environment. The COD framework consists of two phases, i.e., the online maintenance phase and the offline clustering phase. The online maintenance phase provides an efficient mechanism to maintain summary hierarchies of data streams with multiple resolutions in time linear in both the number of streams and the number of data points in each stream. On the other hand, an adaptive clustering algorithm is devised for the offline phase to retrieve approximations of desired substreams from summary hierarchies according to clustering queries. We propose two summarization techniques, based on wavelet and regression analyses, to construct the summary hierarchies. The regression-based summary hierarchy approximates the data stream more precisely and provides better clustering results, at the cost of slightly longer time than and twice the storage space as the wavelet-based one. An adaptive version of COD framework is designed to make a selection between a wavelet-based model and a regression-based model for building the summary hierarchy. By the adaptive COD, we can obtain clustering results with almost the same quality as the regression-based COD while using much less storage space for the summary hierarchy. As shown in the complexity analyses and also validated by our empirical studies, the COD framework performs very efficiently in the data stream environment while producing clustering results of very high quality
An LMI condition for asymptotic stability of discrete-time system based on quadratic difference forms

This paper is concerned with the stability analysis of a linear discrete-time system described by a high-order difference-algebraic equation. It is well known that, in the behavioral approach, a Lyapunov for a linear system is characterized in terms of a so-called quadratic difference form (QDF). For a discrete-time case, Kojima and Takaba (2005) derived a necessary and sufficient condition for the asymptotic stability in terms of the QDFs. On the basis of this QDF condition, we derive a numerically more tractable stability condition in terms of LMIs
Genetic alterations and their clinical implications in gastric cancer peritoneal carcinomatosis revealed by whole-exome sequencing of malignant ascites

Peritoneal carcinomatosis accompanied by malignant ascites is a major cause of death of advanced gastric cancer (GC). To comprehensively characterize the underlying genomic events involved in GC peritoneal carcinomatosis, we analyzed whole-exome sequences of normal gastric tissues, primary tumors, and malignant ascites from eight GC patients. We identified a unique mutational signature biased toward C-to-A substitutions in malignant ascites. In contrast, the patients who received treatment of adjuvant chemotherapy showed a high rate of C-to-T substitutions along with hypermutation in malignant ascites. Comparative analysis revealed several candidate mutations for GC peritoneal carcinomatosis: recurrent mutations in COL4A6, INTS2, and PTPN13; mutations in druggable genes including TEP1, PRKCD, BRAF, ERBB4, PIK3CA, HDAC9, FYN, FASN, BIRC2, FLT3, ROCK1, CD22, and PIK3C2B; and mutations in metastasis-associated genes including TNFSF12, L1CAM, DIAPH3, ROCK1, TGFBR1, MYO9B, NR4A1, and RHOA. Notably, gene ontology analysis revealed the significant enrichment of mutations in the Rho-ROCK signaling pathway-associated biological processes in malignant ascites. At least four of the eight patients acquired somatic mutations in the Rho-ROCK pathway components, suggesting the possible relevance of this pathway to GC peritoneal carcinomatosis. These results provide a genome-wide molecular understanding of GC peritoneal carcinomatosis and its clinical implications, thereby facilitating the development of effective therapeutics.
An efficient cost model for optimization of nearest neighbor search in low and medium dimensional spaces

Existing models for nearest neighbor search in multidimensional spaces are not appropriate for query optimization because they either lead to erroneous estimation or involve complex equations that are expensive to evaluate in real-time. This article proposes an alternative method that captures the performance of nearest neighbor queries using approximation. For uniform data, our model involves closed formulae that are very efficient to compute and accurate for up to 10 dimensions. Further, the proposed equations can be applied on nonuniform data with the aid of histograms. We demonstrate the effectiveness of the model by using it to solve several optimization problems related to nearest neighbor search.
Design of robust classifiers for adversarial environments

In adversarial classification tasks like spam filtering, intrusion detection in computer networks, and biometric identity verification, malicious adversaries can design attacks which exploit vulnerabilities of machine learning algorithms to evade detection, or to force a classification system to generate many false alarms, making it useless. Several works have addressed the problem of designing robust classifiers against these threats, although mainly focusing on specific applications and kinds of attacks. In this work, we propose a model of data distribution for adversarial classification tasks, and exploit it to devise a general method for designing robust classifiers, focusing on generative classifiers. Our method is then evaluated on two case studies concerning biometric identity verification and spam filtering.
Adaptive Closed-Loop Control Provides Blood-Glucose Regulation Using Dual Subcutaneous Insulin and Glucagon Infusion in Diabetic Swine

Background: In order to stave off deleterious complications of the disease, the ultimate task for people with diabetes is to maintain their blood glucose in euglycemic range. Despite technological advancements, conventional open-loop therapy often results in prolonged hyperglycemia and episodic hypoglycemia, in addition to necessitating carbohydrate counting, frequent glucose monitoring, and drug administration. The logical conclusion in the evolution of exogenous insulin therapy is to develop an automated closed-loop control system. Methods: Eleven closed-loop control experiments were conducted in four anesthetized diabetic pigs, with carbohydrate loads simulated by intravenous glucose administration through ear-vein catheters. Type 1 diabetes-like pathology was induced using intravenous administration of cytotoxin streptozotocin. The augmented model-predictive control algorithm accounts for the accumulation of subcutaneous insulin, which is critical in avoiding excessive insulin dosing. Results: Control results consistently showed successful blood-glucose regulation to euglycemic range within 80–120 minutes after intravenous glucose loads, with no incidence of hypoglycemia. This is consistent with a negative oral glucose tolerance test for diabetes and is the optimal postprandial regulation that can be achieved with subcutaneous insulin administration. Results also demonstrated the potency of subcutaneous glucagon in staving off episodic hypoglycemia and revealed efficacy of the control algorithm in coping with a twofold variation in subject weights, while simultaneously overlooking erratic blood-glucose fluctuations. Conclusions: Using an automated adaptive glucose-control system, we show successful blood-glucose regulation in vivo and establish, definitively, the plausibility and practicality of closed-loop blood-glucose control using subcutaneous insulin and glucagon infusion in type 1 diabetes. The control system strikes an intricate balance between tight blood-glucose control and optimal drug consumption, while simultaneously maintaining emphasis on simplicity and reliability.
A study on fault tolerance methods in Cloud Computing

The emergence of Cloud Computing has brought new dimension to the world of information technology. Even though Cloud Computing provide various benefits like agility, on-demand provisioning of resources, reduced cost, multi-tenancy etc., there are risks and flaws associated with it. One key research challenge in Cloud Computing is to ensure continuous reliability and guaranteed availability of resources provided by it. So there is a need for a robust Fault Tolerant (FT) system in Cloud Computing. To better understand FT in Cloud Computing, it is essential to understand the different types of faults. In this paper, we highlight the basic concepts of fault tolerance by understanding the different FT policies like Reactive FT policy and Proactive FT policy and the associated FT techniques used on different types of faults. A study on various fault tolerant methods, algorithms, frameworks etc., has been carried out which are developed and implemented by research experts in this field. This is an area where lot of research is happening and these studies will guide us to build a robust FT technique in Cloud.
Prospective Study of Rape Perpetration by Young South African Men: Incidence & Risk Factors

Background There has been very little prospective research on rape perpetration among men. This paper describes the incidence and risk factors for new rape and attempted rape events among young South African men in an HIV prevention trial. Methods We followed 1,147 men aged 15–26 years who enrolled into a cluster randomised controlled trial to evaluate the HIV prevention behavioural intervention Stepping Stones. Incidence rate ratios for factors associated with incident rape were derived from Poisson models. Results The young men reported 217 incident rapes (completed or attempted) of a girl or woman over 1,914 person years of follow up, yielding a rape incidence of 11.2 per 100 person years. Overall 24.9% of men had previously raped at baseline, and 18.9% did so during the follow up. Among the latter, 61.3% raped for the first time, and 38.7% re-offended. Multivariable Poisson modelling showed a higher incidence of rape perpetration among men who had ever used drugs (IRR 1.86 95%CI 1.39, 2.49), had eight or more lifetime partners (IRR 1.48 95% CI 1.09, 2.01), had been physically violent toward a female partner (IRR 1.50 95%CI 1.11, 2.03) and had disclosed rape perpetration at baseline (IRR 1.45 95%CI 1.07, 1.97). A lower incidence was found among those with greater resistance to peer pressure (IRR 0.85 95%CI 0.74, 0.97). Conclusions The findings highlight the importance of male gender socialisation and addressing delinquent youth sub-cultures in rape prevention. Prevention requires change in hegemonic masculinity, with its emphasis on gender hierarchy, exaggerated performance of heterosexuality and control of women. Interventions are needed to address male socialisation with delinquent peers, by reducing exposure to childhood trauma and strengthening opportunities for gainful employment (in work or recreation).
Testosterone increases bioavailability of carotenoids: Insights into the honesty of sexual signaling

Androgens and carotenoids play a fundamental role in the expression of secondary sex traits in animals that communicate information on individual quality. In birds, androgens regulate song, aggression, and a variety of sexual ornaments and displays, whereas carotenoids are responsible for the red, yellow, and orange colors of the integument. Parallel, but independent, research lines suggest that the evolutionary stability of each signaling system stems from tradeoffs with immune function: androgens can be immunosuppressive, and carotenoids diverted to coloration prevent their use as immunostimulants. Despite strong similarities in the patterns of sex, age and seasonal variation, social function, and proximate control, there has been little success at integrating potential links between the two signaling systems. These parallel patterns led us to hypothesize that testosterone increases the bioavailability of circulating carotenoids. To test this hypothesis, we manipulated testosterone levels of red-legged partridges Alectoris rufa while monitoring carotenoids, color, and immune function. Testosterone treatment increased the concentration of carotenoids in plasma and liver by >20%. Plasma carotenoids were in turn responsible for individual differences in coloration and immune response. Our results provide experimental evidence for a link between testosterone levels and immunoenhancing carotenoids that (i) reconciles conflicting evidence for the immunosuppressive nature of androgens, (ii) provides physiological grounds for a connection between two of the main signaling systems in animals, (iii) explains how these signaling systems can be evolutionary stable and honest, and (iv) may explain the high prevalence of sexual dimorphism in carotenoid-based coloration in animals.
Network Capacity Maximization Using Route Choice and Signal Control With Multiple OD Pairs

In this paper, we investigate a hybrid dynamical system which incorporates flow swap process, green-time proportion swap process, and flow divergence for a general network with multiple Origin-Destination (OD) pairs and multiple routes, where flow swap process is specified in which traffic swaps from more costly to less costly input links, green-time proportion swap process is specified in which green time at each intersection swaps from less pressurized stages to more pressurized stages, flow may diverge at each intersection from one OD pair to other OD pairs. Unlike the dynamical system model, where bottleneck delays need to be intentionally constructed to yield the equilibrium flow vector and green-time proportion vector, we propose a novel control policy to fill the gap by only adjusting the green-time proportion vector. We derive a sufficient condition for the existence of equilibrium of the dynamical system under the mild constraints that 1) the travel cost function and stage pressure function should be continuous functions and 2) the flow and green-time proportion swap processes project all flow and green-time proportion vectors on the boundary of the feasible region onto itself. We derive the condition of unique equilibrium for fixed green-time proportion vector and show that with varying green-time proportion vector, the set of equilibria is a compact, non-convex set, and with the same partial derivative of travel cost function with respect to the flow and green-time proportion vectors. Finally, we prove the stability of the proposed dynamical system by using Lyapunov stability analysis.
Improved Classification for a Data Fusing Kohonen Self Organizing Map Using A Dynamic Thresholding Technique

The use of linear data fusion is a fast developing area in the field of military information and combat systems. However, the use of data fusion in conventional application areas is not as wide spread. To date linear data fusion has been used only in applications in which substantial knowledge of both the problem domain and the sensor devices in use are available. However, in applications such as condition monitoring the problem domain can be very complex, with little or no knowledge about the interactions between measured parameters. This paper describes the use of non-linear self-learning or self-organising systems as a tool for data fusion, since these systems can learn complex interrelationships between a number of parameters, and use this information as a tool for improved classification.
Reducing the error in mobile location estimation using robust window functions

The non-symmetric nature of the distortion that NLoS propagation introduces into the measurements when performing location estimation from distance measurements makes standard robust statistical estimation techniques relatively ineffective. In this paper two new window functions obtained by modifying the conventional Huber and Tukey window functions are presented to reduce the error in distance measurements that are corrupted by multipath fading. It was found that for the Gaussian distributed NLoS component present in our data, both the modified Huber and modified Tukey window functions yield lower root mean square error (RMSE) compared to the maximum likelihood estimator and the original Huber window function. Results also show that the modified window functions are more robust to the data outliers created by NLoS measurements than conventional robust estimation algorithms.
Comparative evaluation of transform domain speech compression techniques

In this paper, a comparison is made among the performance of transform domain techniques with respect to various performance parameters of speech signal. The speech signal is taken from the International Phonetic Alphabet data base in the category of a Narrative of the French language and then the computation of compression ratio is carried out by setting appropriate threshold values in all the transform domain techniques. After that, the other speech signal parameters such as mean square error, signal o noise ratio, peak signal to noise ratio and normalized root mean square error are computed at the decoder end. The results shown indicate that the performance of the discrete sine transform is better than the discrete cosine transform and fast Fourier transform in terms of compression ratio and other computed speech signal parameters.
Target domain definition and classification in CASP8

In order to be successful CASP experiments require experimentally determined protein structures. These structures form the basis of the experiment. Structural genomics groups have provided the vast majority of these structures in recent editions of CASP. Before the structure prediction assessment can begin these target structures must be divided into structural domains for assessment purposes and each assessment unit must be assigned to one or more tertiary structure prediction categories. In CASP8 target domain boundaries were based on visual inspection of targets and their experimental data, and on superpositions of the target structures with related template structures. As in CASP7 target domains were broadly classified into two different categories: “template‐based modeling” and “free modeling. ” Assessment categories were determined by structural similarity between the target domain and the nearest structural templates in the PDB and by whether or not related structural templates were used to build the models. The vast majority of the 164 assessment units in CASP8 were classified as template‐based modeling. Just 10 target domains were defined as free modeling. In addition three targets were assessed in both the free modeling and template based categories and a subset of 50 template‐based models was evaluated as part of the “high accuracy” subset. The targets submitted for CASP8 confirmed a trend that has been apparent since CASP5: targets submitted to the CASP experiments are becoming easier to predict. Proteins 2009. © 2009 Wiley‐Liss, Inc.
Language discrimination and clustering via a neural network approach

We classify twenty-one Indo-European languages starting from written text. We use neural networks in order to define a distance among different languages, construct a dendrogram and analyze the ultrametric structure that emerges. Four or five subgroups of languages are identified, according to the "cut" of the dendrogram, drawn with an entropic criterion. The results and the method are discussed.
Inhibition of West Nile Virus Multiplication in Cell Culture by Anti-Parkinsonian Drugs

West Nile virus (WNV) is a mosquito-borne flavivirus maintained in a transmission cycle between mosquitoes and birds, but it can also infect other vertebrates, including humans, in which it can cause neuroinvasive diseases. To date, no licensed vaccine or therapy for human use against this pathogen is yet available. A recent approach to search for new antiviral agent candidates is the assessment of long-used drugs commonly administered by clinicians to treat human disorders in drug antiviral development. In this regard, as patients with West Nile encephalitis frequently develop symptoms and features of parkinsonism, and cellular factors altered in parkinsonism, such as alpha-synuclein, have been shown to play a role on WNV infection, we have assessed the effect of four drugs (L-dopa, Selegiline, Isatin, and Amantadine), that are used as therapy for Parkinson’s disease in the inhibition of WNV multiplication. L-dopa, Isatin, and Amantadine treatments significantly reduced the production of infectious virus in all cell types tested, but only Amantadine reduced viral RNA levels. These results point to antiparkinsonian drugs as possible therapeutic candidates for the development of antiviral strategies against WNV infection.