Miracle-dz/UniFAR-FTUs huggingface dataset:
Antioxidant Defenses in the Brains of Bats during Hibernation

Hibernation is a strategy used by some mammals to survive a cold winter. Small hibernating mammals, such as squirrels and hamsters, use species- and tissue-specific antioxidant defenses to cope with oxidative insults during hibernation. Little is known about antioxidant responses and their regulatory mechanisms in hibernating bats. We found that the total level of reactive oxygen species (ROS) and reactive nitrogen species (RNS) in the brain of each of the two distantly related hibernating bats M. ricketti and R. ferrumequinum at arousal was lower than that at torpid or active state. We also found that the levels of malondialdehyde (product of lipid peroxidation) of the two hibernating species of bats were significantly lower than those of non-hibernating bats R. leschenaultia and C. sphinx. This observation suggests that bats maintain a basal level of ROS/RNS that does no harm to the brain during hibernation. Results of Western blotting showed that hibernating bats expressed higher amounts of antioxidant proteins than non-hibernating bats and that M. ricketti bats upregulated the expression of some enzymes to overcome oxidative stresses, such as superoxide dismutase, glutathione reductase, and catalase. In contrast, R. ferrumequinum bats maintained a relatively high level of superoxide dismutase 2, glutathione reductase, and thioredoxin-2 throughout the three different states of hibernation cycles. The levels of glutathione (GSH) were higher in M. ricketti bats than in R. ferrumequinum bats and were significantly elevated in R. ferrumequinum bats after torpor. These data suggest that M. ricketti bats use mainly antioxidant enzymes and R. ferrumequinum bats rely on both enzymes and low molecular weight antioxidants (e.g., glutathione) to avoid oxidative stresses during arousal. Furthermore, Nrf2 and FOXOs play major roles in the regulation of antioxidant defenses in the brains of bats during hibernation. Our study revealed strategies used by bats against oxidative insults during hibernation.
Incremental Finite-State Parsing

This paper describes a new finite-state shallow parser. It merges constructive and reductionist approaches within a highly modular architecture. Syntactic information is added at the sentence level in an incremental way, depending on the contextual information available at a given stage. This approach overcomes the inefficiency of previous fully reductionist constraint-based systems, while maintaining broad coverage and linguistic granularity. The implementation relies on a sequence of networks built with the replace operator. Given the high level of modularity, the core grammar is easily augmented with corpus-specific sub-grammars. The current system is implemented for French and is being expanded to new languages.
An anomaly-based botnet detection approach for identifying stealthy botnets

Botnets (networks of compromised computers) are often used for malicious activities such as spam, click fraud, identity theft, phishing, and distributed denial of service (DDoS) attacks. Most of previous researches have introduced fully or partially signature-based botnet detection approaches. In this paper, we propose a fully anomaly-based approach that requires no a priori knowledge of bot signatures, botnet C&C protocols, and C&C server addresses. We start from inherent characteristics of botnets. Bots connect to the C&C channel and execute the received commands. Bots belonging to the same botnet receive the same commands that causes them having similar netflows characteristics and performing same attacks. Our method clusters bots with similar netflows and attacks in different time windows and perform correlation to identify bot infected hosts. We have developed a prototype system and evaluated it with real-world traces including normal traffic and several real-world botnet traces. The results show that our approach has high detection accuracy and low false positive.
Predictors of mother-infant interaction quality: The role of parental social cognitions and parenting stress

Using a social cognition theoretical lens and based upon tenets from symbolic interactionism theory, this study explores the influence of parental social cognitions and parenting stress on the quality of the parent-infant interaction. Parenting stress, parent expectations of self in the parenting role, parent expectations of infant development, and observations of parent-infant interactions were obtained from 150 mother-infant dyads. Hierarchical linear regression analyses tested the hypothesis that parenting stress mediates the relationship between parental social cognitions and quality of the parent-infant interaction. After controlling for maternal age, parent expectations of self in the parenting role were not related to observed interaction quality, but parent expectations of infant development were related to observed interaction quality. The predicted hypothesis of mediation was not supported, however stress related to parent-child dysfunctional interaction was related to observed quality of interaction. These findings indicate a need for services focused on educating parents about developmentally appropriate expectations for infant development, as well as supporting parents in their interactions with their infants.
Privacy-Preserving Data Mining in Electronic Surveys

The invention relates to a strip composite material with a base material of a metal or a metal alloy and a tin coating on the surface, whereby an intermetallic phase (IMP) is formed between the base material and the coating. In particular for achieving good wear and corrosion resistance of the composite material with a simultaneously oxide-free surface, 1 to 50 At. -%, preferably 6 to 30 At. -%, carbon (C) are embedded in an outer surface region of the tin coating up to a thickness D of approximately 2 mu m. A method (oil treatment) and apparatus for the manufacture of the composite material of the invention are also disclosed.
On QoS Support to Ofelia and OpenFlow

OpenFlow is the most promising architecture for future Software Defined Networks (SDNs). However, from the aspects of large-scale or carrier-grade networks, it still lacks some key components. For example, QoS (Quality of Service) provisioning is an indispensable part of such production networks. During the evolution of the OpenFlow standard, some QoS capabilities have been added to the protocol, however, even the latest version has only a limited and not well-defined QoS framework. Hence, integrated QoS support is missing in current OpenFlow experimental test beds including Ofelia. This paper describes a possible architectural extension to Ofelia in order to make it capable of running QoS related experiments. We summarize the initial tasks regarding the survey of QoS features and limitations of OpenFlow switches deployed in Ofelia islands and the performance tests needed to characterize these devices. For extending the feature palette of Ofelia, we propose a QoS management platform with full integration into the existing management framework. By means of this envisioned extension, QoS settings of the whole Ofelia test bed can be adjusted easily, in a user friendly fashion. Moreover, we walk through the main steps needed not only towards an integrated OpenFlow test bed with QoS support but towards a QoS architecture to OpenFlow.
In vivo Conditions Induce Faithful Encoding of Stimuli by Reducing Nonlinear Synchronization in Vestibular Sensory Neurons

Previous studies have shown that neurons within the vestibular nuclei (VN) can faithfully encode the time course of sensory input through changes in firing rate in vivo. However, studies performed in vitro have shown that these same VN neurons often display nonlinear synchronization (i.e. phase locking) in their spiking activity to the local maxima of sensory input, thereby severely limiting their capacity for faithful encoding of said input through changes in firing rate. We investigated this apparent discrepancy by studying the effects of in vivo conditions on VN neuron activity in vitro using a simple, physiologically based, model of cellular dynamics. We found that membrane potential oscillations were evoked both in response to step and zap current injection for a wide range of channel conductance values. These oscillations gave rise to a resonance in the spiking activity that causes synchronization to sinusoidal current injection at frequencies below 25 Hz. We hypothesized that the apparent discrepancy between VN response dynamics measured in in vitro conditions (i.e., consistent with our modeling results) and the dynamics measured in vivo conditions could be explained by an increase in trial-to-trial variability under in vivo vs. in vitro conditions. Accordingly, we mimicked more physiologically realistic conditions in our model by introducing a noise current to match the levels of resting discharge variability seen in vivo as quantified by the coefficient of variation (CV). While low noise intensities corresponding to CV values in the range 0.04–0.24 only eliminated synchronization for low (<8 Hz) frequency stimulation but not high (>12 Hz) frequency stimulation, higher noise intensities corresponding to CV values in the range 0.5–0.7 almost completely eliminated synchronization for all frequencies. Our results thus predict that, under natural (i.e. in vivo) conditions, the vestibular system uses increased variability to promote fidelity of encoding by single neurons. This prediction can be tested experimentally in vitro.
On the integration of speech recognition and statistical machine translation

This paper focuses on the interface between speech recognition and machine translation in a speech translation system. Based on a thorough theoretical framework, we exploit word lattices of automatic speech recognition hypotheses as input to our translation system which is based on weighted finite-state transducers. We show that acoustic recognition scores of the recognized words in the lattices positively and significantly affect the translation quality. In experiments, we have found consistent improvements on three different corpora compared with translations of single best recognized results. In addition we build and evaluate a fully integrated speech translation model.
Knowledge Mapping Evolution Guided by Data Mining

Preserve knowledge, retain know, these are the objectives of a scalable company. The knowledge mapping is graphical techniques which allows preserve and visualize the strategic heritage of the acquired know-how during the years. We use Data mining techniques to exploit the different sources of data and to improve the process of acquiring this knowledge. In this paper, we present a new knowledge mapping approach based on the one hand, on the Boolean modeling of mapping domains knowledge and on the other hand, the use of different data sources by data mining technique to improve the process of acquiring knowledge explicit. To evaluate our approach, we have initiated a process of mapping guided by machine learning which operates in two stages: a data mining and automatic mapping. Data mining is to run initially from an induction Boolean case studies (explicit knowledge). The mapping rules are then used to automatically improve the Boolean model of the knowledge mapping.
Data-Driven Dependency Parsing of New Languages Using Incomplete and Noisy Training Data

We present a simple but very effective approach to identifying high-quality data in noisy data sets for structured problems like parsing, by greedily exploiting partial structures. We analyze our approach in an annotation projection framework for dependency trees, and show how dependency parsers from two different paradigms (graph-based and transition-based) can be trained on the resulting tree fragments. We train parsers for Dutch to evaluate our method and to investigate to which degree graph-based and transition-based parsers can benefit from incomplete training data. We find that partial correspondence projection gives rise to parsers that out-perform parsers trained on aggressively filtered data sets, and achieve unlabeled attachment scores that are only 5% behind the average UAS for Dutch in the CoNLL-X Shared Task on supervised parsing (Buchholz and Marsi, 2006).
Interactive User Feedback in Ontology Matching Using Signature Vectors

When compared to a gold standard, the set of mappings that are generated by an automatic ontology matching process is neither complete nor are the individual mappings always correct. However, given the explosion in the number, size, and complexity of available ontologies, domain experts no longer have the capability to create ontology mappings without considerable effort. We present a solution to this problem that consists of making the ontology matching process interactive so as to incorporate user feedback in the loop. Our approach clusters mappings to identify where user feedback will be most beneficial in reducing the number of user interactions and system iterations. This feedback process has been implemented in the Agreement Maker system and is supported by visual analytic techniques that help users to better understand the matching process. Experimental results using the OAEI benchmarks show the effectiveness of our approach. We will demonstrate how users can interact with the ontology matching process through the Agreement Maker user interface to match real-world ontologies.
Filter Bank Feature Combination (FBFC) approach for brain-computer interface

The Filter Bank Common Spatial Pattern (FBCSP) algorithm constructs and selects subject-specific discriminative CSP features from a filter bank of spatial-temporal filters in a motor imagery brain-computer interface (MI-BCI). However, information from other types of features could be extracted and combined with CSP features to enhance the classification performance. Hence this paper proposes a Filter Bank Feature Combination (FBFC) approach and investigates the use of CSP and Phase Lock Value (PLV) features, where the latter measures the phase synchronization between the EEG electrodes. The performance of the FBFC using CSP and PLV features is evaluated on four-class motor imageries from the publicly available BCI Competition IV Dataset IIa. The experimental results showed that the proposed FBFC using CSP and PLV features yielded a significant improvement in cross-validation accuracies on the training data (p=0.008) and better session-to-session transfer accuracies to the evaluation data compared to the use of CSP features using the FBCSP algorithm. This motivates the research of FBFC using a battery of other features that could possibly benefit EEG-based BCIs and multi-modal BCI systems.
Multiple wire reconnections based on implication flow graph

Global flow optimization (GFO) can perform multiple fanout/fanin wire reconnections at a time by modeling the problem of multiple wire reconnections with a flow graph, and then solving the problem using the maxflow-mincut algorithm on the flow graph. In this article, we propose an efficient multiple wire reconnection technique that modifies the framework of GFO, and as a result, can obtain better optimization quality. First, we observe that the flow graph in GFO cannot fully characterize wire reconnections, which causes the GFO to lose optimality in several obvious cases. In addition, we find that fanin reconnection can have more optimization power than fanout reconnection, but requires more sophisticated modeling. We reformulate the problem of fanout/fanin reconnections by a new graph, called the implication flow graph (IFG). We show that the problem of wire reconnections on the implication flow graph is NP-complete and also propose an efficient heuristic on the new graph. To demonstrate the effectiveness of our proposed method, we conduct an application which utilizes the flexibility of the wire reconnections explored in the logic domain to further minimize interconnects in the physical layout. Our experimental results are very exciting.
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements

We propose a simple, scalable, and fast gradient descent algorithm to optimize a nonconvex objective for the rank minimization problem and a closely related family of semidefinite programs. With $O(r^3 \kappa^2 n \log n)$ random measurements of a positive semidefinite $n \times n$ matrix of rank $r$ and condition number $\kappa$, our method is guaranteed to converge linearly to the global optimum.
Effect of bioactive materials modified with chondroitin sulfate on human MSC

Biomaterials functionalization by addition of biomolecules is an interesting approach for enhancing cell-biomaterial interactions and therefore improve their bioactivity. The global objective of this project is to enhance the bioactivity of biomaterials such as implantable devices and 3D scaffolds by adding extracellular matrix components and therefore facilitate the adhesion, growth and survival of human mesenchymal stem cells (hMSC) in biomaterials for tissue repair and tissue engineering applications. In this project chondroitin sulfate (CS) and growth factors were studied for their effect on hMSC in biomaterials. First, the effect of these biomolecules was tested in solution. Then, two kinds of biomaterials were created: bioactive surfaces for enhancing bioactivity of implantable devices and bioactive hydrogels which can be used as 3D scaffolds for cell encapsulation and delivery. A pro-survival effect of the growth factors studied in this project (epidermal growth factor, vascular endothelial growth factor and fibroblast growth factor) was not observed when tested in solution, therefore the project further focused on CS effect only. Interestingly, CS did not affect cell growth in media containing serum, while inducing cell detachment from substrate in serum free conditions. For the bioactive surfaces construction, CS was grafted to either an amine-rich plasmapolymerized coating created on polyethylene terephthalate (PET) films (further referred as LP) or to commercial cell culture plates functionalized with amino groups. The bioactive surfaces were characterized by different techniques such as contact angle, atomic force microscopy, Orange II dye and Toluidine Blue O dye colorimetric assays (for amino group and CS quantification respectively) and finally, cell culture experiments (adhesion, growth and survival). Results confirmed the presence of CS grafted on both substrates. Commercial amine plates grafted almost five times more CS compared to LP. This rendered the surface antifouling for proteins and cells as confirmed by protein adsorption and cell culture assays. Cell culture assays on bioactive surfaces based on LP demonstrated improved cell adhesion and growth when compared to tissue culture plates or bare PET films in serum containing conditions. Chitosan based hydrogels containing CS at a concentration of 500 μg/ml resulted in a cohesive hydrogel which supported hMSC viability up to 7 days. However increasing CS concentration to high level such as 10000 μg/ml led to decrease of cell viability after 4 or 7 days, probably due to lack of porosity and water since the hydrogel precipitates upon formation and expulses water. In conclusion, this work demonstrated that CS immobilization can enhance the biological interactions of hMSC with biomaterials used for implantable devices such as PET. Further studies are needed to evaluate the possible effect of CS on hMSC differentiation and phenotype. Hydrogels with CS could be very interesting for tissue engineering applications such as cartilage formation.
Blame for all

Several programming languages are beginning to integrate static and dynamic typing, including Racket (formerly PLT Scheme), Perl 6, and C# 4.0 and the research languages Sage (Gronski, Knowles, Tomb, Freund, and Flanagan, 2006) and Thorn (Wrigstad, Eugster, Field, Nystrom, and Vitek, 2009). However, an important open question remains, which is how to add parametric polymorphism to languages that combine static and dynamic typing. We present a system that permits a value of dynamic type to be cast to a polymorphic type and vice versa, with relational parametricity enforced by a kind of dynamic sealing along the lines proposed by Matthews and Ahmed (2008) and Neis, Dreyer, and Rossberg (2009). Our system includes a notion of blame, which allows us to show that when casting between a more-precise type and a less-precise type, any cast failures are due to the less-precisely-typed portion of the program. We also show that a cast from a subtype to its supertype cannot fail.
Response to Comments on Brandão et al. (2005)

In this note, we respond to Smithâs (2005) discussion of the approach outlined in our paper (Brandao et al. 2005) on using traditional decision analysis methods to solve real-options problems. Our response addresses several areas where we largely agree with Smith, but have different views on modeling preferences or on the practicality of implementing alternative modeling approaches. We view the issue raised by Smith on the estimation of process volatility to be a valid concern and propose a modification to our method to address this problem.
Linear temporal sequences and their interpretation using midpoint relationships

The temporal interval relationships formalized by Allen, and later extended to accommodate semiintervals by Freksa, have been widely utilized in both data modeling and artificial intelligence research to facilitate reasoning between the relative temporal ordering of events. In practice, however, some modifications to the relationships are necessary when linear temporal sequences are provided, when event times are aggregated, or when data is supplied to a granularity which is larger than required. This paper discusses these modifications and outlines a solution to this problem which accommodates any available knowledge of interval midpoints.
Rabies control and elimination: a test case for One Health

One Health approaches have already been shown to be successful in controlling rabies in different parts of the world. In this article, the latest in Veterinary Record's series promoting One Health, Sarah Cleaveland and her colleagues at the University of Glasgow discuss why integrated strategies are needed to enhance the cost effectiveness of measures to control and eliminate rabies, particularly in low-income countries
Inducing Constraint-based Grammars from a Small Semantic Treebank

We present a relational learning framework for grammar induction that is able to learn meaning as well as syntax. We introduce a type of constraint-based grammar, lexicalized well-founded grammar (lwfg) , and we prove that it can always be learned from a small set of semantically annotated examples, given a set of assumptions. The semantic representation chosen allows us to learn the constraints together with the grammar rules, as well as an ontology-based semantic interpretation. We performed a set of experiments showing that several fragments of natural language can be covered by a lwfg, and that it is possible to choose the representative examples heuristically, based on linguistic knowledge.