Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Machine Learning to Algorithm Selection for TSP

The Travelling Salesman Problem (TSP) has been extensively studied in the literature and various solvers are available. However, none of the state-of-the-art solvers for TSP outperforms the others in all problem instances within a given time limit. Therefore, the prediction of the best performing algorithm can save computational resources and optimise the results. In this paper, the TSP is studied in context of automated algorithm selection. Our aim is to identify the relevant features of problem instances and tackle this scenario as a machine learning task. We extend the set of existing features in the literature and propose several novel features to better characterise the problem. The contribution of the new features is statistically analysed and experiments show that adding our new features improves the prediction accuracy. We identified that our features based on kNN graph transformation are especially helpful. To create the training datasets, two state-of-the-art (meta-) heuristic algorithms are systematically evaluated on more than 2000 problems. Overall, we show that our prediction can be substantially more accurate than simple preference of an algorithm with the best performance for a majority of problem instances.
A novel three phase energy meter model with wireless data reading and online billing solution

Measurement of data electronically leads a combination of benefits that go well beyond the traditional rotor-plate energy meter design. In this paper with the help of an energy chip, an improved novel three phase energy metering solution is developed. Automating the progression of measurement through digital wireless communication technique is done to get high accuracy, smooth control and better reliability. The developed energy meter calculates the total average active power mainly for industrial and commercial applications. The hardware circuit accepts three phase voltages and currents as its inputs and provides the output in the form of serial interfaced data equivalent to the average real power. This serial data is fed to a remote personal computer through the wireless ZigBee network that represents the concept of distant wireless metering, practically involving no manpower. The paper also presents a software solution developed for total electrical energy billing and data management system.
Bio-inspired algorithms for query optimization in biological databases

Despite of the numerous research efforts on distributed query processing, the complexity of the problem has been little solved which paves way for the exploration on the solution of the problem. However, due to its inherent difficulty, the complexity of the majority of problems on distributed query optimization remains unknown. In this paper, we analyze and present the problems identified and the possible algorithms used for solving them. The higher probability of problem solving nature of the Bio-inspired Algorithms has been projected with a Comparative Study on literature proof and as a result a proposal for Query Optimization on Biological Databases is introduced.
LOSS FUNCTIONS FOR MULTICLASS LEARNING AND THE GEOMETRY OF THE LABEL SPACE

As bigger and more complex datasets are available, multiclass learning is becoming increasingly important. While theory and algorithms for solving binary classification problems are available, the problem of multicategory classification is much less understood. Indeed, practical multiclass algorithms often reduce the problem to a collection of binary classification problems. Binary classification algorithms typically starts from a relaxation approach: classification is posed as a non-convex minimization problem and hence relaxed to convex one, defined by suitable convex loss functions. In this context, results in statistical learning theory quantify the error incurred by relaxation (to further derive consistency and sample complexity results), see for example [1]. Generalizing the above approach to more than two classes is not straightforward. Previous results show that naive extension of binary classification approaches might lack basic properties such as statistical consistency [5]. At the root of these difficulties there is the more complex label structure when dealing with multiple categories: suitable coding strategies [2] and loss functions [6] need to be defined to avoid unnatural ordering among the classes, and constraints on the function class needs to be imposed, see[1, 6]. In particular, the latter constraints often lead to complications from a computational point of view, and is usually ignored in practice at the price of a loss on the consistency guarantees. Here we study a suitable coding/decoding strategy, namely the simplex coding [3]. This strategy is the natural generalization of the ±1 coding for binary classification and allows to cast multicategory classification as a vector valued regression problem. Our analysis shows that the simplex coding allows to generalize concepts, results and proof techniques from the binary case, which, interestingly, is recovered as a special case. In particular we prove new explicit results for the relaxation error, when considering convex loss functions, e.g. the least square and the hinge loss functions. We prove that these loss functions, coupled with the simplex coding/decoding strategies leads to fisher consistent algorithms and, especially, we derive explicit comparison theorems relating the excess misclassification error with the excess expected loss. We further describe the practical implementation of the corresponding regularization schemes in the context of vector valued reproducing kernel Hilbert spaces [4]. In particular, we discuss: 1) the complexity of the obtained algorithms (batch and online), and how the possible structure among the classes can be incorporated or learned using a constrained optimization approach.
Seeding a new transdisciplinary community of practice

Transdisciplinary research is a bundle of interwoven social practices taking different forms in different contexts. There is no single such practice; instead, there are many different transdisciplinarities. Each has its own particular meanings, materials and skills, in addition to shared elements. In this chapter, I use social practice theory as a lens to guide personal reflection on my role in establishing a new community of transdisciplinary research practice at Wintec in New Zealand. In sharing my particular practice with the Wintec community, I played the role of a catalyst or seed for a new community of practice. The emerging practice at Wintec resembles my own, but departs from it in ways that respond to the local context. In this dynamic engagement with the Wintec practitioners, my own practice changed and deepened. I conclude that there is a critical need for diverse transdisciplinary communities of practice to prioritise mutual engagement. Introduction: The practice of transdisciplinary research Transdisciplinary research is a rich, complex, contested body of theory and practice that rose to prominence over the last four decades. Klein (2015) identifies three overlapping discourses that capture the concerns driving this rise: transcendence; problem solving; and transgression. Transdisciplinary research aims to transcend the fragmentation of knowledge into disciplines by synthesising knowledge towards a more holistic view. It is concerned with delivering more effective responses to wicked problems (Rittel and Webber 1973) and sustainability challenges. Finally, it transgresses traditional scientific boundaries by recognising the value of knowledge held by actors outside of academia. While Klein’s analysis provides valuable orientation, it is a high-level view. I am interested in how these discourses play out in practice, at the scale of practitioner communities. In this chapter, I explore the ‘doing’ of transdisciplinary research in two practitioner communities, drawing on theories of social practice. Reckwitz (2002: 249) defines a social practice as: A routinized type of behaviour which consists of several elements, interconnected to one [an]other: forms of bodily activities, forms of mental activities, ‘things’ and their use, a background knowledge in the form of understanding, know-how, states of emotion and motivational knowledge. When people do transdisciplinary research, they engage in particular physical and mental activities, using particular tools, frameworks and knowledge, with particular motivations. These practices vary to suit the local context so that there is no single transdisciplinary research practice; instead, there are many transdisciplinarities. I am interested here in how the nature of transdisciplinary research practice shifts and changes as it moves from one context
PALS: Saving Network Power With Low Overhead to ISPs and Applications

Power saving in the network infrastructure has received great attention in recent years. Power-aware traffic management is proposed in many works, in which a subset of routers/links are preferentially used to carry traffic while other links are activated only when traffic load is high. However, it remains challenging how to minimize the overhead to both ISPs and applications, which is important to the successful deployment of power-aware traffic management in a real network. This paper presents PALS, a new Power-Aware Link State routing based traffic management protocol. Compared with previous solutions, PALS remarkably reduces the overheads to ISPs and applications by the following innovations. First, PALS minimizes the forwarding table expansion due to dynamic power-aware routing, by using destination based routing instead of pairwise routing (e.g., MPLS). Second, PALS limits packet reordering for applications, by never splitting traffic between an IE (ingress-egress) router pair to multiple paths. Third, PALS significantly reduces the computation complexity of the power-aware routing algorithm, by running a simple path selection algorithm at each ingress router with the knowledge of local traffic information as well as global link utilization, which are much easier to obtain than global traffic matrix required by the state-of-the-art solutions (e.g., Zhang , IEEE ICNP 2010). Extensive simulations and testbed experiments show that, although bearing the simplicities to minimize the overhead, PALS saves satisfactory network power, with quick response to traffic variance and negligible impact on the packet delivery performance for applications.
Output Feedback Model Predictive Control: a probabilistic approach

Abstract In this paper a novel approach for output-feedback model predictive control of discrete-time linear stochastic systems is presented, allowing for the presence of unbounded noise. The satisfaction of probabilistic constraints and the stability properties of the method are guaranteed by imposing suitable constraints on the mean and the variance of the state variable. A simulation example is presented, illustrating the effectiveness of the proposed control scheme.
Coordination Among Agents Using Reactive Rules

Coordination and collaboration are naturally used by groups for carrying out activities and solving problems that require cooperation. However, getting a set of computer agents to do that same has been a problem -- primarily addressed by the AI community and recently by the database community as workflow and process management problems (e.g. in business processes, electronic commerce, logistics). Not surprisingly, the problem has been addressed at different levels of abstraction by the two communities. Coordination protocols (both static and dynamic) as well as task and result sharing have been investigated by the AI community; system level support as well as specification and execution of relaxed notions of transaction (sometimes termed an activity) have been addressed by the database community. It is evident that combining the two will provide an effective unified solution for a class of problems that require cooperation. This paper classifies problems addressed in the AI and database literature according to degree of coordination and collaboration. It reports on work done by the authors in utilising the reactive paradigm to synthesize, from the yechniques in these areas, a common framework for the support of multi-agent problem solving, workflow, and process management. In addition to resolving the terminology used by different groups, task sharing is used to demonstrate the approach described. It is accomplished by creating either static or dynamic plans that are coordinated by ECA rules -- both pre-defined and dynamically created. The paper details the applicability of ECA rules in this domain, their adequacy, and a prototype implementation.
Simulation models of partnership choice and the examination of marital homogamy

simulation models of partnership choice were run in Netlogo and cross-tabulations of male and female partnership choice were produced with respect to a generic hierarchical trait. The effect of three attraction methods: random attraction, “attracted to most similar” and “attracted to highest” were compared, together with the effect of constrained neighbourhoods and the distribution of the trait in question. The “attracted to most similar” method generated tables with the greatest degree of homogamy, as measured by the Social Homogamy Index, followed by the “attracted to highest level”. The distribution of the trait altered the marginal distributions across the trait scores but had little impact on the homogamy of the table. Reducing the sight of the agents into “constrained neighbourhoods” generated tables with less homogamy as agents were restricted to fewer options. Applying these findings to a proposed Census based simulation indicates the importance of the decision rules and the strong effect of regional variation on partnering patterns.
Ethnic Earnings Gap in Colombia

We study the earnings gap against indigenous and afro-descendants, separately, and use the non-parametric decomposition approach proposed by Ñopo (2008) to understand the role of observable characteristics and historical legacies in explaining it. The earnings gap against indigenous workers is about 70%, over three times higher than the one against afrodescendants. For indigenous populations, the unexplained portion of the gap is almost one third of the total gap, while for afro-descendants it is either low or insignificant. Minorities are underrepresented in formal jobs and some types of employment; region and housing surroundings are important in explaining the gap.
Either B7-1 or B7-2 is required for Listeria monocytogenes-specific production of gamma interferon and interleukin-2

Listeria infection results in the induction of gamma interferon (IFN-gamma)-producing T lymphocytes. Blocking of the costimulatory molecule B7 in vivo led to a marked decrease in antigen-specific production of IFN-gamma and interleukin-2 by lymphocytes. Blocking of both B7-1 (CD80) and B7-2 (CD86) was required in order to inhibit cytokine production, indicating that either molecule could act alone. Although IFN-gamma production by cultured spleen cells was significantly suppressed by B7 blocking, mice cleared primary and secondary Listeria infection as effectively as control mice.
Adaptation structurelle des os du membre supérieur et de la clavicule à l'activité

Cette recherche suit une demarche de type actualiste et s'appuie sur une serie osteoarcheologique documentee (sexe et profession connus). L'etude permet de dire qu'une part du developpement de l'asymetrie des ossements du membre superieur se met en place sous l'influence des contraintes physiques en relation avec le mode de vie. L'activite agricole conduit a un developpement plus symetrique du squelette, puisque a la fois des personnes non-manuelles et des specialistes montrent un degre d'asymetrie plus important. De plus, on apprend que l'asymetrie constitue une partie integrante des caracteristiques morphologiques d'une population. Le schema de distribution des asymetries laisserait apparaitre la composante ethnique ou populationnelle de l'echantillon et les differences mises en evidence entre groupes d'activites sont alors le reflet d'adaptations fonctionnelles. Cette approche methodologique pose les bases permettant des applications a l'etude de populations anciennes regionales.
The research based on the video sequence of face key frames extraction

Accurate, real-time as well as high fitness of face detection is always the difficult problem of Face Recognition System. The paper aiming at the problem come up with a solution which can extract key frames of face from the video sequence. The algorithm using skin color and AdaBoost face detection to construct a real-time, high-efficient face key frames extractor. It can reduce AdaBoost cascade classifier training intensity and enhance the efficiency of Face Detection. On the other hand, we combine 40 Gabor filter groups in different scales and directions to detect human eyes, and then verify the extracted face key frames that significantly improve the speed of Gabor eye location. From the above, our method can extract face key frames from video sequence in real-time.
Antioxidant and Hepatoprotective Activities of Ethanolic Extracts of Leaves of Premna esculenta Roxb. against Carbon Tetrachloride-Induced Liver Damage in Rats

Premna esculenta Roxb. (family Verbenaceae) is a shrub used by the ethnic people of Chittagong Hill Tracts of Bangladesh for the treatment of hepatocellular jaundice. The present study was done to evaluate the hepatoprotective and the in vivo antioxidant activity of ethanolic extracts of leaves of the plant in carbon tetrachloride-induced liver damage in rats. Hepatotoxicity was induced in rats by i.p. injection of CCl 4 diluted with olive oil (1:1 v/v; 1 mL/kg body weight) on alternate days for 7 days. After 7 days of pretreatment of test extracts, the biochemical markers such as Serum Glutamate Oxaloacetate Transaminase (SGOT), Serum Glutamate Pyruvate Transaminase (SGPT), Alkaline Phosphatase (ALP), total protein, and albumin were estimated followed by the measurement of liver cytosolic antioxidant enzymes such as superoxide dismutase, catalase, and peroxidase.The data were analyzed using one-way analysis of variance (ANOVA) followed by Dunnett′s t-test. The extract both at the doses of 200 and 400 mg/kg p.o. significantly (P < 0.001) reduced the elevated levels of SGPT, SGOT, ALP and increased the reduced levels of total protein and albumin compared to the CCl 4 -treated animals. The extracts also showed a significant (P < 0.001) increase in the reduced levels of superoxide dismutase (SOD), catalase, and peroxidase. The effects of the extracts on these parameters were comparable with those of the standard, silymarin. The findings of the study indicate that the leaf extract of P. esculenta showed a potential hepatoprotective activity and the protective action might have manifested by restoring the hepatic SOD, catalase, and peroxidase levels. The results justify the traditional use of this plant in liver disorders.
Gender and noun inflection: The fate of ‘vulnerable’ categories in Northern Norwegian

Research has predicted an uncertain future for gender and noun inflection in Northern Norwegian varieties. Gender is expected to disappear and the noun inflection system to undergo simplification. The driving force is said to be contact between Norwegian, a gender language, and Sami and Kven, non-gender languages. On the basis of data from the Nordic Dialect Corpus, we argue that these predictions are not borne out. Gender is a stable category and noun inflection has divergent patterns. These findings are interpreted in light of the socio-historical background of language contact and shift characterising the region.
Performance Analysis of Group-Blind Multiuser Detectors for Synchronous CDMA

Blind multiuser detectors are attractive for the suppression of interference in a CDMA environment. This paper deals with the performance of group blind multiuser detector for synchronous CDMA is analyzed. The blind multi user detectors are Direct Matrix Inversion(DMI),Subspace and group blind multiuser detector. The performance analysis is performed by means of the Signal to Interference Noise Ratio(SINR) and Bit Error Rate(BER). The numerical results are plotted as variation of SINR Vs SNR, K and M, SINR with respect to correlation coefficient(  ) and BER Vs Number of samples(M) for three detectors using MATLAB software. The gain rises in group blind multiuser detector over the DMI and subspace detectors. The comparison is carried out for equicorrelated signals for mathematical simplicity.
Utilizing quantum dot transistors with programmable threshold voltages for low-power mobile computing

Power consumption poses one of the fundamental barriers for deploying mobile computing devices in energy-constrained situations with varying operation conditions. In particular, leakage power is projected to increase exponentially in future semiconductor process nodes. This challenging problem is pressing for renewed focus on power-performance optimization at all levels of design abstract, from novel device structures to fundamental shifts in design paradigm. In this article, we propose to exploit the programmable threshold voltage quantum dot (QD) transistors to reduce leakage thereby improving the energy efficiency for mobile computing. The unique programmability and reconfigurability enabled by QD transistors extend our capability in design optimization for new power-performance trade-offs. Simulation results demonstrate the significant leakage reduction over conventional techniques.
Curcumin Improves Amyloid β-Peptide (1-42) Induced Spatial Memory Deficits through BDNF-ERK Signaling Pathway

Curcumin, the most active component of turmeric, has various beneficial properties, such as antioxidant, anti-inflammatory, and antitumor effects. Previous studies have suggested that curcumin reduces the levels of amyloid and oxidized proteins and prevents memory deficits and thus is beneficial to patients with Alzheimer’s disease (AD). However, the molecular mechanisms underlying curcumin’s effect on cognitive functions are not well-understood. In the present study, we examined the working memory and spatial reference memory in rats that received a ventricular injection of amyloid-β1-42 (Aβ1-42), representing a rodent model of Alzheimer’s disease (AD). The rats treated with Aβ1-42 exhibited obvious cognitive deficits in behavioral tasks. Chronic (seven consecutive days, once per day) but not acute (once a day) curcumin treatments (50, 100, and 200 mg/kg) improved the cognitive functions in a dose-dependent manner. In addition, the beneficial effect of curcumin is accompanied by increased BDNF levels and elevated levels of phosphorylated ERK in the hippocampus. Furthermore, the cognition enhancement effect of curcumin could be mimicked by the overexpression of BDNF in the hippocampus and blocked by either bilateral hippocampal injections with lentiviruses that express BDNF shRNA or a microinjection of ERK inhibitor. These findings suggest that chronic curcumin ameliorates AD-related cognitive deficits and that upregulated BDNF-ERK signaling in the hippocampus may underlie the cognitive improvement produced by curcumin.