Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Virus Generation Using Behavioral Models of Circuits

The problem of peak power estimation in CMOS circuits is essential for analyzing the reliability and performance of circuits at extreme conditions. The dynamic power dissipated is directly proportional to the switching activity (number of gate outputs that toggles (changes state)) in the circuit. The power virus problem involves finding input vectors that cause maximum dynamic power dissipation (maximum toggles) in circuits. As the power virus problem is NP-complete the gate-level techniques are less scalable with increasing design size and produce less optimal vectors. In this paper, an approach for power virus generation using behavioral models of digital circuits is presented. The proposed technique converts the given behavioral model automatically to an integer (word-level) constraint model and employs an integer constraint solver to generate the required power virus vectors. Experimenting the proposed technique on ISCAS behavioral level benchmark circuits and the standard DLX processor model show that the above technique is fast and yields higher-quality results than the known gate-level techniques. Interestingly, the paper attempts to generate an assembly program that cause the maximum dynamic power dissipation on the given DLX processor model. To the best of our knowledge the proposed technique is the first reported that considers power virus generation using behavioral level models.
Radio wave propagation in potato fields

Reliable communication is crucial for successful deployment of wireless sensor networks. Therefore, it is important to understand the impact of environmental conditions on the performance of the radios (Chipcon CC1000 transceivers) used in typical sensor nodes. This paper reports on an extensive set of measurements taken in a potato field, where the foliage has an important effect on the propagation of radio waves. The influence of the growth stage of the potato crop is significant. We observed a reduction of 15 dB in signal strength at 15 m between nodes, when a flowering crop is compared to a crop on its return. This effectively reduces the radio range from 23 m to 10 m. Another important result is that radio waves propagate better in conditions with a high humidity (i.e., at night and during rain). We attribute this to changes in the reflection coefficient of the top of the potato canopy.
From Report to Court: Rape cases and the criminal justice system in the North East

Introduction This research was commissioned by the Northern Rock Foundation to examine progression of individual cases relating to rape through the criminal justice system, from reporting to conviction, across three police force areas in the North East of England. The study came about due to concerns within the criminal justice sector in the region that little is known about the detailed pattern of progression, attrition or related criminal justice system practice in rape cases.
Perception and probabilistic anchoring for dynamic world state logging

Knowing precisely where objects are located enables a robot to perform its tasks both more efficiently and more reliably. To acquire the respective knowledge and to effectively use it as a resource, a robot has to go through the world with “open eyes”. Specifically, it has to become environment-aware by keeping track of where objects of interest are located and explicitly represent their geometrical properties. In this paper, we propose to equip robots with a perception system that passively monitors the environment using a 3D data acquisition system, identifying objects that might become the subject of future manipulation tasks. Our system encompasses a 3D semantic mapping and reconstruction pipeline and a storage and data merging unit for perceived information that provides on-demand modeling and comparison capabilities. Based on probabilistic logical models, we address the important perceptual subtask of object identity resolution, i.e. inferring which observations refer to which entities in the real world (perceptual anchoring). Our system can be used as a bootstrapping system for the generation of object-centric knowledge and can, in this way, be used as a mid-level perception system that enables activity recognition, scene recognition and high-level planning.
Attribution of Musical Works to Josquin des Prez

Josquin des Prez was one of the most important composers of the middle renaissance era. However, there is disagreement in the academic community about what musical pieces should be attributed to him. The project described here uses a machine learning approach to classify dubiously attributed works to Josquin or his near contemporaries. Using features known as counterpoint modules, we were able to build supervised learning models from a training set of known music. We attempted both multi-class (classifying all composers) and binary (Josquin or not Josquin) classification. The best multi-class model had a crossvalidation accuracy of 81%. The best binary classifier had an Area Under the ROC Curve of . 8707 and an accuracy of 84%. Using this model, we predict that between 90 and 130 of our test set of 285 questionable works were actually written by Josquin.
Model Checking the Biological Model of Membrane Computing with Probabilistic Symbolic Model Checker by Using Two Biological Systems

Problem statement: Membrane computing formalism has provided better modeling capabilities for biological systems in comparison to conventional mathematical models. Model checking could be used to reason about the biological system in detail and with precision by verifying formally whether membrane computing model meets the properties of the system. Approach: This study was carried to investigate the preservation of properties of two biological systems that had been modeled and simulated in membrane computing by a method of model checking using PRISM. The two biological systems were prey-predator population and signal processing in the legend-receptor networks of protein TGF-β. Results: The model checking of membrane computing model of the biological systems with five different properties showed that the properties of the biological systems could be preserved in the membrane computing model. Conclusion: Membrane computing model not only provides a better approach in representing and simulating a biological system but also able to sustain the basic properties of the system.
Heterogeneity, Market Mechanisms, and Asset Price Dynamics

This chapter surveys the boundedly rational heterogeneous agent (BRHA) models of financial markets, to the development of which the authors and several co-authors have contributed in various papers. We give particular emphasis to role of the market clearing mechanism used, the utility function of the investors, the interaction of price and wealth dynamics, portfolio implications, the impact of stochastic elements on the markets dynamics, and calibration of this class of models. Due to agents’ behavioural features and market noise, the BRHA models are both nonlinear and stochastic. We show that the BRHA models produce both a locally stable fundamental equilibrium corresponding to that of standard paradigm, as well as instability with a consequent rich range of possible complex behaviours characterised both indirectly by simulation and directly by stochastic bifurcations. A calibrated model is able to reproduce quite well the stylized facts of financial markets. The BRHA framework is thus able to accommodate market features that seem not easily reconcilable for the standard financial market paradigm, such as fat tail, volatility clustering, large excursions from the fundamental and bubbles.
Defining specificity and on-target activity of BH3-mimetics using engineered B-ALL cell lines

One of the hallmarks of cancer is a resistance to the induction of programmed cell death that is mediated by selection of cells with elevated expression of anti-apoptotic members of the BCL-2 family. To counter this resistance, new therapeutic agents known as BH3-mimetic small molecules are in development with the goal of antagonizing the function of anti-apoptotic molecules and promoting the induction of apoptosis. To facilitate the testing and modeling of BH3-mimetic agents, we have developed a powerful system for evaluation and screening of agents both in culture and in immune competent animal models by engineering mouse leukemic cells and re-programming them to be dependent on exogenously expressed human anti-apoptotic BCL-2 family members. Here we demonstrate that this panel of cell lines can determine the specificity of BH3-mimetics to individual anti-apoptotic BCL-2 family members (BCL-2, BCL-XL, BCL-W, BFL-1, and MCL-1), demonstrate whether cell death is due to the induction of apoptosis (BAX and BAK-dependent), and faithfully assess the efficacy of BH3-mimetic small molecules in pre-clinical mouse models. These cells represent a robust and valuable pre-clinical screening tool for validating the efficacy, selectivity, and on-target action of BH3-mimetic agents.
QoS-aware downlink scheduling in multi-carrier communication systems

In order to increase the system capacity in next generation wireless networks, defining new packet scheduling and resource allocation algorithms can be an approach. In the context of the existence of multiple Component Carriers in LTE-Advanced, there is an added degree of freedom which can be used for these radio resource management algorithms. This paper presents a QoS-aware scheduling algorithm, designed for use in an LTE-Advanced system. It is compared to traditional algorithms such as Round Robin and Proportional Fair. Results show that in some situations it provides better performances than the previous algorithms.
Fuzzy clustering approach for star-structured multi-type relational data

Recently, mining interrelated data among multiple types of objects attracts a lot of attention due to its importance in many real-world applications. Despite of extensive study on fuzzy clustering of vector space data and homogeneous relational data, very limited exploration has been made on fuzzy clustering of relational data involving several object types. In this paper, we propose FC-SMR, a fuzzy approach for clustering star-structured multi-type relational data, where the central type is related to multiple attribute types. In FC-SMR, objects of the central type are clustered based on the rankings of objects of different attribute types. We formulate the clustering problem as a constrained maximization problem and give an efficient algorithm for finding local solutions of the defined objective function. Experimental studies conducted on real-world document data show the effectiveness of the new approach.
Professional identity among student teachers of physical education: the role of physicality

ABSTRACT In this study, we investigate the role of physicality in the professional identities of physical education (PE) student teachers. Twenty PE student teachers participated in semi-structured interviews during their final teaching practice. Data were analysed using qualitative thematic analysis and typologies. The results showed substantial diversity in the student teachers’ conceptions of physicality and the ways these conceptions were embedded in their developing professional identities. The results also brought to light criticism directed at traditional notions of the body and physicality in PE and PE teacher education (PETE). We suggest that conceptions of the body and physicality represent a central element of PE teachers’ professional identity. The results also indicate a relationship between conceptions of physicality and pedagogies implemented in the future. Time and place for student teachers to discuss body-related matters should be provided in PETE.
Adaptive Replication and Replacement in P 2 P Caching

Caching large audio and video files in a community of peers is a compelling application for P2P. Assuming an underlying DHT substrate for the caching community, we propose adaptive object replication and replacement strategies for P2P caches. One such strategy, TopK MFR, is shown to provide near optimal performance.
Performance of a Chirality-affected Channel exhibiting Giant Optical Activity for Terahertz Communications

Terahertz frequency band is an emerging research area related to nano-scale communications. In this frequency range, specific features can provide the possibility to overcome the issues related to the spectrum scarcity and capacity limitation. Apart high molecular absorption, and very high reflection loss that represent main phenomena in THz band, we can derive the characteristics of the channel affected by chirality effects occurring in the propagation medium, specifically, in the case where a Giant Optical Activity is present. This effect is typical of the so-called chiral-metamaterials in (4-10) THz band, and is of stimulating interest particularly for millimeter wireless communications. In this paper, we analyze the behavior of specific parameters of a chiral-metamaterial, like the relative electrical permittivity, magnetic permeability and chirality coefficients, and from that we derive the channel behavior both for Line-of-Sight and No Line-of-Sight propagations. We notice the presence of spectral windows, due to peaks of resonance of chiral parameter. Finally, performances of the chirality-affected channel have been assessed in terms of (i) channel capacity, (ii) propagation delay, and (iii) coherence bandwidth, for different distances.
Energy Efficient Cross Layer Design for Broadcast in Ad hoc Wireless Networks

Power is a precious resource in ad hoc wireless networks due to the limited battery capability. Broadcast is an important operation in ad hoc wireless networks. The broadcast algorithm must be designed carefully to avoid serious contention, collision, and congestion. Contention, collision and congestion of broadcast used up valuable resources in the ad hoc network such as power and bandwidth. Hence, it is important to design energy efficient broadcast algorithm to prolong the life span of ad hoc network and improve the performance for broadcast. Many literatures showed that a rigid layered structure does not work efficiently to preserve energy and broadcast in ad hoc networks. We develop energy efficient cross layer broadcast (CLBA) algorithm which jointly addresses the issues of transmitted power control and dynamic connected dominating set formation to improve performance of broadcasting in Ad hoc network. Simulation studies indicate that compared with flooding and TOP algorithms our algorithm has good performance in terms of reachability, average broadcast latency and energy efficiency.
Environmental risk factors associated with bovine tuberculosis among cattle in high-risk areas

Our research shows that environmental features are important predictors of bovine tuberculosis (bTB) in British cattle herds in high-prevalence regions. Data from 503 case and 808 control farms included in the randomized badger culling trial (RBCT) were analysed. bTB risk increased in larger herds and on farms with greater areas of maize, deciduous woodland and marsh, whereas a higher percentage of boundaries composed of hedgerows decreased the risk. The model was tested on another case–control study outside RBCT areas, and here it had a much smaller predictive power. This suggests that different infection dynamics operate outside high-risk areas, although it is possible that unknown confounding factors may also have played a role.
The Mass Spectrum Of Machos From Parallax Measurements

We demonstrate that by making satellite-based parallax measurements of Macho events, it is possible to distinguish disk from bulge Machos and to determine individual masses of disk Machos to an accuracy of $0.2$ in the log. This is at least a 3-fold improvement over what can be done without parallaxes, i.e., just from the observed time scale of the events. In addition, we show that the physical distribution of disk Machos can be found from `reduced' velocities determined by parallax measurements.
Do Sensornet Protocol Variants Yield Real Benefits?

Simple, stateless networking protocols provide a low-cost and predictable foundation upon which to build decentralised applications. Sensornets are complex, containing emergent behaviour; identifying protocols offering appropriate behaviour may be difficult. In this paper we evaluate the relative performance of gossiping protocol variants in non-ideal sensornets. We examine the extent to which a "standard" gossiping protocol might be outperformed by variants of this standard, each specialised and optimised to mitigate anticipated problems. We categorise and measure the undesired behaviours that remain, as a foundation for future protocols which will address these specific issues.
MiRNA–miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features

Synergistic regulations among multiple microRNAs (miRNAs) are important to understand the mechanisms of complex post-transcriptional regulations in humans. Complex diseases are affected by several miRNAs rather than a single miRNA. So, it is a challenge to identify miRNA synergism and thereby further determine miRNA functions at a system-wide level and investigate disease miRNA features in the miRNA–miRNA synergistic network from a new view. Here, we constructed a miRNA–miRNA functional synergistic network (MFSN) via co-regulating functional modules that have three features: common targets of corresponding miRNA pairs, enriched in the same gene ontology category and close proximity in the protein interaction network. Predicted miRNA synergism is validated by significantly high co-expression of functional modules and significantly negative regulation to functional modules. We found that the MFSN exhibits a scale free, small world and modular architecture. Furthermore, the topological features of disease miRNAs in the MFSN are distinct from non-disease miRNAs. They have more synergism, indicating their higher complexity of functions and are the global central cores of the MFSN. In addition, miRNAs associated with the same disease are close to each other. The structure of the MFSN and the features of disease miRNAs are validated to be robust using different miRNA target data sets.
Simulating Dynamic Covariance Structures for Testing the Adaptive Behavior of Variable Selection Algorithms (Invited Paper)

Variable selection for regression is a classical statistical problem, motivated by concerns that too large a number of covariates may bring about overfitting and unnecessarily high measurement costs. Novel difficulties arise in streaming contexts, where the correlation structure of the process may be drifting, in which case it must be constantly tracked so that selections may be revised accordingly. A particularly interesting phenomenon is that non-selected covariates become missing variables, inducing bias on subsequent decisions. This raises an intricate exploration-exploitation tradeoff, whose dependence on the covariance tracking algorithm and the choice of variable selection scheme is too complex to be dealt with analytically. We hence capitalise on the strength of simulations to explore this problem, taking the opportunity to tackle the difficult task of simulating dynamic correlation structures.
Frequency of Autoantibodies and Connective Tissue Diseases in Chinese Patients with Optic Neuritis

Background Optic neuritis (ON) is often associated with other clinical or serological markers of connective tissue diseases (CTDs). To date, the effects of autoantibodies on ON are not clear. Purpose To assess the prevalence, clinical patterns, and short outcomes of autoantibodies and Sjögren’s syndrome (SS) involvement in Chinese ON patients and evaluate the relationship between ON, including their subtypes, and autoantibodies. Methods A total of 190 ON patients were divided into recurrent ON (RON), bilateral ON (BON), and isolated monocular ON (ION). Demographic, clinical, and serum autoantibodies data were compared between them with and without SS involvement. Serum was drawn for antinuclear antibody (ANA), extractable nuclear antigen antibodies (SSA/SSB), rheumatoid factor (RF), anticardiolipin antibodies (ACA), and anti-double-stranded DNA antibody (A-ds DNA), anticardiolipin antibody (ACLs), anti-β2-glycoprotein I (β2-GPI) and Aquaporin-4 antibodies (AQP4-Ab). Spectral-domain optical coherence tomography (SD-OCT) was used to evaluate the atrophy of the optic nerve. Results 68 patients (35.79%) had abnormal autoantibodies, 26(13.68%) patients met diagnostic criteria for CTDs, including 15(7.89%) patients meeting the criteria for SS. Antibodies including SSA/SSB 23 (30.26%) (p1 and p 2<0.001) and AQP4–Ab10 (13.16%) (p1 = 0.044, p2 = 0.01) were significantly different in patients in the RON group when compared with those in the BON (P1 = RON VS ION) and ION (p2 = RON VS ION) groups. SS was more common in RON patients (p1 = 0.04, p2 = 0.028). There was no significant difference between SSA/SSB positive and negative patients in disease characteristics or severity. Similar results were obtained when SS was diagnosed in SSA/SSB positive patients. Conclusion RON and BON were more likely associated with abnormal autoantibodies; furthermore, AQP4 antibody, SSA/SSB and SS were more common in the RON patients. AQP4 antibodydetermination is crucial in RON patients who will develop NMO. However, when compared with other autoantibodies, SSA/SSB detected in patients was not significantly associated with disease characteristics or severity.
Big Data and Audience Measurement: A Marriage of Convenience?

[eng] Digital convergence has gradually altered both the data and media worlds. The lines that separated media have become blurred, a phenomenon that is being amplified daily by the spread of new devices and new usages. At the same time, digital convergence has highlighted the power of big data, which is defined in terms of two connected parameters: volume and the frequency of acquisition. Big Data can be as voluminous as exhaustive and its acquisition can be as frequent as to occur in real time. Even though Big Data may be seen as risking a return to the paradigm of census that prevailed until the end of the 19th century – whereas the 20th century belonged to sampling and surveys. Mediametrie has chosen to consider this digital revolution as a tremendous opportunity for progression in its audience measurement systems.
Novel Genes Critical for Hypoxic Preconditioning in Zebrafish Are Regulators of Insulin and Glucose Metabolism

Severe hypoxia is a common cause of major brain, heart, and kidney injury in adults, children, and newborns. However, mild hypoxia can be protective against later, more severe hypoxia exposure via “hypoxic preconditioning,” a phenomenon that is not yet fully understood. Accordingly, we have established and optimized an embryonic zebrafish model to study hypoxic preconditioning. Using a functional genomic approach, we used this zebrafish model to identify and validate five novel hypoxia-protective genes, including irs2, crtc3, and camk2g2, which have been previously implicated in metabolic regulation. These results extend our understanding of the mechanisms of hypoxic preconditioning and affirm the discovery potential of this novel vertebrate hypoxic stress model.
An automatic load sharing approach for a DFIG based wind generator in a microgrid

The aim of this paper is to manage the load variation of a microgrid by controlling the wind generator. An automatic load sharing approach for a doubly-fed induction generator (DFIG) based wind generator is proposed in this paper considering both resistive and inductive line parameters and variable wind velocity. The load demand for the wind generator is determined based on the variation of its terminal voltage. The pitch angle of the wind blade and the currents of the rotor circuit are controlled to meet the real and reactive power demand of the wind generator. The proposed method is tested by simulating a benchmark microgrid test system. Simulation results show that the proposed control scheme is useful to manage the load variation of a microgrid.